{"$schema": "https://c3voc.de/schedule/schema.json", "generator": {"name": "pretalx", "version": "2026.3.0.dev0", "url": "https://pretalx.com"}, "schedule": {"url": "https://pretalx.com/juliacon-2026/schedule/", "version": "0.15", "base_url": "https://pretalx.com", "conference": {"acronym": "juliacon-2026", "title": "JuliaCon 2026", "start": "2026-08-10", "end": "2026-08-15", "daysCount": 6, "timeslot_duration": "00:05", "time_zone_name": "Europe/Berlin", "colors": {"primary": "#3aa57c"}, "rooms": [{"name": "Tent \u2014 RW1", "slug": "5531-tent-rw1", "guid": "bc6ca85a-19fe-5fb9-900e-d0b219eb74dc", "description": null, "capacity": null}, {"name": "Muschel \u2014 N1", "slug": "5534-muschel-n1", "guid": "e20de1c1-6617-5b16-8a3d-41bc52005cbf", "description": null, "capacity": null}, {"name": "Muschel \u2014 N2", "slug": "5532-muschel-n2", "guid": "2d9e9194-8bff-56e1-8e22-106c7761259e", "description": null, "capacity": null}, {"name": "Muschel \u2014 N3", "slug": "5533-muschel-n3", "guid": "d5a5d776-b1d4-5fe8-a431-0ae852f12c3c", "description": null, "capacity": null}, {"name": "Alte Mensa \u2014 Audi Max", "slug": "5535-alte-mensa-audi-max", "guid": "9f79ad3b-0aef-5eca-9c2c-86a65d1987bd", "description": null, "capacity": null}, {"name": "Alte Mensa \u2014 Atrium Maximum", "slug": "5536-alte-mensa-atrium-maximum", "guid": "ad07bb12-d170-552d-ab83-18e1fa22bb93", "description": "Alte Mensa", "capacity": null}], "tracks": [{"name": "Nonlinear and complex systems analysis with Julia", "slug": "6794-nonlinear-and-complex-systems-analysis-with-julia", "color": "#654ca3"}, {"name": "Engineering with Julia", "slug": "6782-engineering-with-julia", "color": "#878b8c"}, {"name": "Health Mini Symposium", "slug": "6783-health-mini-symposium", "color": "#2fb354"}, {"name": "Geospatial minisymposium", "slug": "6784-geospatial-minisymposium", "color": "#019cc2"}, {"name": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "slug": "6785-juliahep-mini-2026-julia-for-nuclear-and-elementary-particle-physics", "color": "#fe00ff"}, {"name": "Julia for Biology and Biology for Julia", "slug": "6786-julia-for-biology-and-biology-for-julia", "color": "#024507"}, {"name": "Bringing Julia to the Computational Humanities and Social Sciences", "slug": "6787-bringing-julia-to-the-computational-humanities-and-social-sciences", "color": "#7d694f"}, {"name": "Julia for HPC Minisymposium", "slug": "6788-julia-for-hpc-minisymposium", "color": "#ff001e"}, {"name": "Julia for Partial Differential Equations and its Applications", "slug": "6789-julia-for-partial-differential-equations-and-its-applications", "color": "#ce8900"}, {"name": "JuliaMolSim Minisymposium", "slug": "6790-juliamolsim-minisymposium", "color": "#278f83"}, {"name": "Earth system science in Julia", "slug": "6791-earth-system-science-in-julia", "color": "#37261f"}, {"name": "Computational Physics Minisymposium", "slug": "6792-computational-physics-minisymposium", "color": "#b30015"}, {"name": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "slug": "6793-symbolic-and-numerical-methods-in-nonlinear-algebra", "color": "#a6009e"}, {"name": "Differentiable Computational Models and their Applications", "slug": "6795-differentiable-computational-models-and-their-applications", "color": "#2a6bc8"}, {"name": "Quantum Mini", "slug": "6796-quantum-mini", "color": "#9a00e2"}, {"name": "General", "slug": "6797-general", "color": "#000000"}, {"name": "Julia, GPUs, and Accelerators", "slug": "6802-julia-gpus-and-accelerators", "color": "#45af52"}, {"name": "Julia in Industry", "slug": "6803-julia-in-industry", "color": "#565656"}, {"name": "Methods and Applications of Scientific Machine Learning (SciML)", "slug": "6804-methods-and-applications-of-scientific-machine-learning-sciml", "color": "#c200ff"}, {"name": "Approximate Computing in Numerical Linear Algebra", "slug": "6805-approximate-computing-in-numerical-linear-algebra", "color": "#804801"}, {"name": "Pharmaceutical Research in Julia", "slug": "6909-pharmaceutical-research-in-julia", "color": "#136e85"}], "days": [{"index": 1, "date": "2026-08-10", "day_start": "2026-08-10T04:00:00+02:00", "day_end": "2026-08-11T03:59:00+02:00", "rooms": {"Muschel \u2014 N2": [{"guid": "84254a45-16f7-5d57-b50a-2b84b2162e7c", "code": "7JKGJU", "id": 92683, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7JKGJU/image_DUSuhIr.webp", "date": "2026-08-10T10:00:00+02:00", "start": "10:00", "end": "2026-08-10T13:00:00+02:00", "duration": "03:00", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92683-performance-engineering-with-julia-on-modern-supercomputers", "url": "https://pretalx.com/juliacon-2026/talk/7JKGJU/", "title": "Performance Engineering with Julia on Modern Supercomputers", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Workshop", "language": "en", "abstract": "High-Performance Computing (HPC) empowers modern science and engineering by enabling the simulation and analysis of complex systems at unprecedented scales on cutting-edge supercomputers. Julia, as a dynamic programming language designed for scientific computing, uniquely combines the ease of high-level syntax with near C and Fortran execution speed, making it a compelling vehicle for performance engineering on supercomputers.\n\nThis workshop offers a hands-on introduction to performance engineering with Julia on modern HPC systems, guiding participants through the workflow of analyzing, optimizing, and scaling Julia codes on a real HPC environment. Using interactive Jupyter notebooks backed by the [Otus system](https://pc2.uni-paderborn.de/systems-and-services/otus) at the [Paderborn Center for Parallel Computing (PC2)](https://pc2.uni-paderborn.de/), participants will experiment with live Julia code and performance analysis tools to gain practical proficiency in optimization techniques.\n\nThe three parts of the workshop will first introduce the organization of modern HPC clusters that influences Julia code performance, then examine the performance engineering workflow that identifies and solves optimization problems and finally present a case study of the workflow applied to a real scientific application.\n\nThrough the combination with hands-on exercises, participants will not only understand the core principles of performance engineering but also actively practice optimizing and scaling Julia programs on the Otus HPC system at PC2.", "description": "## Overview and Goals\n\nThis 3-hour workshop provides essential knowledge and practical skills for performance engineering with Julia on modern high-performance computing (HPC) systems. Participants will understand the hierarchy of an HPC system and CPU architectures from a Julia programmer's perspective, learn systematic workflows for performance analyzing, optimizing, and parallelizing Julia programs, and apply these methods to real-world scientific computing applications.\n\n### Session 1: Demystifying Modern Supercomputers and Processor Architectures\n\nThis session introduces the hierarchical architecture of modern HPC clusters for Julia programmers. Using the state-of-the-art [Otus system](https://pc2.uni-paderborn.de/systems-and-services/otus) at the [Paderborn Center for Parallel Computing (PC2)](https://pc2.uni-paderborn.de/) participants will explore the structure of an HPC cluster, from interconnected compute nodes and node-level memory hierarchy to the core architecture of modern CPUs, such as AMD 5th-generation Turin processors with AVX-512 vector units. Interactive Julia code examples, executed through the PC2 JupyterHub service running on Otus, will accompany the presentation.\n\nThis session concludes with an introduction to the Roofline Performance Model, which helps participants reason about computational performance, identify bottlenecks, and recognize optimization opportunities. This session establishes the foundation for performance engineering on modern HPC systems.\n\n### Session 2: Performance Engineering Workflow in Julia\n\nThis session focuses on practical performance optimization in Julia, illustrating how to fully exploit the computing power of modern CPUs. Following an introduction to performance analysis, participants will learn to profile Julia codes. Key topics include writing type-stable Julia code, optimizing memory layout for cache efficiency, reducing heap allocations, and leveraging SIMD vectorization. Participants will conduct performance measurements using BenchmarkTools.jl and LIKWID.jl to understand the interplay between hardware performance counters and performant Julia programming.\n\nAll topics will be illustrated through simple Julia examples executed on the Otus system, allowing participants to follow along and gain hands-on experience.\n\n### Session 3: Case Study: Molecular Dynamics Simulation of Liquid Argon (ArgonMD)\n\nThe final session applies the learned techniques to a realistic scientific application, ArgonMD, a molecular dynamics simulation of liquid argon based on the Lennard-Jones potential under periodic boundary conditions. Through a live demonstration, participants will observe how successive optimization steps yield performance gains. The case study progresses from a single-core baseline to parallelization across multiple compute nodes on Otus, consolidating the complete workflow for performance engineering introduced in earlier sessions.\n\nBy the end of this workshop, participants will be able to:\n\n * Understand the structure of modern HPC systems and CPU architectures in Julia programming.\n * Apply the Roofline model to analyze and reason about performance of Julia codes.\n * Use profiling tools to identify computational bottlenecks in Julia programs.\n * Utilize systematic optimization techniques to write high-performance Julia codes.\n * Employ multithreading and distributed computing to scale Julia applications on HPC systems.\n\nThis workshop integrates fundamental concepts, benchmarking, live Julia code examples, and a case study to equip participants with the knowledge and methods to develop performant Julia applications for state-of-the-art supercomputers.\nTarget Audience\n\nThis workshop is designed for a broad audience of Julia programmers, from those new to HPC environment to experienced computational scientists and HPC software developers seeking to leverage Julia as a high-performance and high-productivity language for their scientific research.\n\nNo prior experience with HPC is required for Session 1; only basic Julia programming is assumed. Session 2 builds upon this foundation and presents practical workflows for performance engineering in Julia. Session 3 is particularly suited to domain scientists interested in developing optimized and scalable Julia codes that can take full advantage of modern HPC systems.\n\n## Detailed Outline\n\nThis workshop is planned for 3 hours and combines presentations, live Julia code demos, and hands-on exercises for an interactive learning experience.\n\n### Session 1: Demystifying Modern Supercomputers and Processor Architectures\n\n * Speaker: Prof. Dr. Christian Plessl (Chair Professor W3 for High-Performance Computing, Managing Director of PC2, Paderborn University)\n * Format: Presentation with interactive Jupyter notebooks\n * Topics:\n     * Modern supercomputers: the Otus system at PC2\n     * Accessing Otus via PC2 JupyterHub\n     * Hierarchy of HPC cluster system\n     * CPU architectures in Julia programming\n     * Roofline Performance Model\n\n### Session 2: Performance Engineering Workflow in Julia\n\n * Speaker: Alex Wiens (HPC Advisor at PC2, Paderborn University)\n * Format: Presentation with interactive Jupyter notebooks\n * Topics:\n     * Introduction to code optimization in Julia\n     * Profiling\n     * Benchmarking with BenchmarkTools.jl \n     * Writing type-stable Julia code\n     * Hardware performance counters with LIKWID.jl \n     * Memory optimization and efficient cache utilization\n     * SIMD vectorization\n\n### Session 3: Case Study: Molecular Dynamics Simulation of Liquid Argon (ArgonMD)\n\n * Speaker: Dr. Xin Wu (Scientific Advisor Theoretical Physics/Chemistry at PC2, Paderborn University)\n * Format: Presentation with live demos\n * Topics:\n     * Applying performance engineering workflows to scientific computing\n     * Progressive optimization of ArgonMD on single CPU-core\n     * Node-level and multi-node parallelization on the Otus system\n\n### Hands-on Exercises\n\nParticipants only need a web browser to access the PC2 JupyterHub platform, where all exercises run directly on the Otus system. Each exercise is provided as a Jupyter notebook, containing guided explanations, starter code, and space for experimentation. Hints and reference solutions will also be provided to ensure participants progress smoothly throughout the workshop.", "recording_license": "", "do_not_record": false, "persons": [{"code": "Y7WVP3", "name": "Alex Wiens", "avatar": "https://pretalx.com/media/avatars/NXEKHU_URxrOCA.webp", "biography": "Alex Wiens works as High-Performance Computing (HPC) advisor at the Paderborn Center for Parallel Computing (PC2). His work's focus is performance analysis, consultation and training.", "public_name": "Alex Wiens", "guid": "f993e8c0-fb55-5f3b-8671-1cd2bcabd599", "url": "https://pretalx.com/juliacon-2026/speaker/Y7WVP3/"}, {"code": "8SZW7C", "name": "Xin Wu", "avatar": "https://pretalx.com/media/avatars/7BMX3W_81lgU6I.webp", "biography": "Dr. Xin Wu is a Scientific Advisor for Theoretical Physics/Chemistry at the Paderborn Center for Parallel Computing (PC2), Paderborn University. His doctoral research focused on GPU-accelerated quantum chemistry for high-performance computing. At PC2, he is responsible for HPC training, user support and consultation, as well as code optimization and parallelization, with particular emphasis on FPGA\u2011accelerated kernels for quantum chemistry calculation.", "public_name": "Xin Wu", "guid": "89b0f6af-7011-5297-b349-41e78c4ce8bb", "url": "https://pretalx.com/juliacon-2026/speaker/8SZW7C/"}, {"code": "VVHEZP", "name": "Christian Plessl", "avatar": "https://pretalx.com/media/avatars/XVJNP7_zOt6GDL.webp", "biography": "Christian Plessl is professor (W3) for High-Performance Computing at the department of Computer Science at Paderborn University. He is also managing director of the Paderborn Center for Parallel Computing, which is a central scientific institute of Paderborn University and a National High-Performance Computing center in the NHR alliance. He is a member of the board of directors of the NHR association.\n\nDr. Plessl earned a PhD degree (Dr. sc. ETH) in Computer Engineering from ETH Zurich in 2006, and a MSc degree in Electrical Engineering in 2001, also from ETH Zurich. He has been a principal investigator in numerous national and transnational research projects funded by the German Research Foundation (DFG), the German Ministry of Education and Research (BMBF), the State of North Rhine-Westfalia, and the European Commission. His research has also received support from industry, for example, by grants from AMD/Xilinx, Intel/Altera, Fujitsu, and others.\n\nDr. Plessl has authored and co-authored more than 100 peer-reviewed publications and his research has been honored with several awards, e.g., the significant paper award 2015 of FPL conference, the best paper awards at HEART 2023, ReConFig 2014 and 2012, the Paderborn University Research Award 2018 and 2009, and the SEW-EURODRIVE Studienpreis award in 2001. He is a senior member of the IEEE, member of the ACM, Gesellschaft f\u00fcr Infromatik (GI), and the HiPEAC Network of Excellence. He is a regular reviewer for scientific journals and serves on the program committee of major international conferences. His research interests include architecture and tools for high-performance parallel and reconfigurable computing, scientific computing, and adaptive computing systems.", "public_name": "Christian Plessl", "guid": "78791535-f257-5fcc-ad3f-ab6092085fa9", "url": "https://pretalx.com/juliacon-2026/speaker/VVHEZP/"}, {"code": "83QPTE", "name": "Gerrit Pape", "avatar": "https://pretalx.com/media/avatars/9CHLAB_HmLcJ70.webp", "biography": "Gerrit Pape is a Research Associate at the Paderborn Center for Parallel Computing (PC2). His work includes coordinating the evaluation of technology trends in the competence network HPC.nrw, as well as teaching Introduction to High-Performance Computing with the Julia language. His research focuses on FPGA acceleration in HPC systems, with a special interest in multi-FPGA communication and scaling.", "public_name": "Gerrit Pape", "guid": "956ae689-18e8-500a-b1b5-c619c57c6d21", "url": "https://pretalx.com/juliacon-2026/speaker/83QPTE/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7JKGJU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7JKGJU/", "attachments": [{"title": "flyer-7JKGJU", "url": "/media/juliacon-2026/submissions/7JKGJU/resources/7JKGJU_a2SPxzn.png", "type": "related"}]}, {"guid": "06732e82-b26f-5909-8206-39a6d7bba5b3", "code": "MRFYNN", "id": 93392, "logo": "https://pretalx.com/media/juliacon-2026/submissions/MRFYNN/image_8qmT20l.webp", "date": "2026-08-10T14:30:00+02:00", "start": "14:30", "end": "2026-08-10T17:30:00+02:00", "duration": "03:00", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93392-hands-on-with-julia-for-hpc-on-gpus", "url": "https://pretalx.com/juliacon-2026/talk/MRFYNN/", "title": "Hands-on with Julia for HPC on GPUs", "subtitle": "", "track": "General", "type": "Workshop", "language": "en", "abstract": "Julia offers the best of both worlds: high-level expressiveness combined with low-level performance, allowing developers to leverage modern hardware accelerators without needing expertise in hardware-specific languages. This workshop demonstrates how Julia makes high-performance computing (HPC) accessible by covering topics such as distributed GPU computing, GPU code optimization, and scalable workflows.", "description": "## Why this workshop\n\nWhy wait hours for computations when they could take seconds? Why struggle with rewriting high-level prototypes in lower-level languages just to achieve performance? Traditionally, writing fast code for HPC systems requires mastering hardware-specific languages, leading to complex, expensive, and difficult-to-maintain software.\n\nJulia removes this barrier by providing a seamless, high-performance environment and package ecosystem where domain experts can easily integrate and reuse optimized code, making HPC more approachable and efficient. Participants will gain hands-on experience running Julia code on a GPU-powered supercomputer.\n\n\n## This workshop will introduce\n\n1. Good paractices in GPU programming\n2. GPU programming using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl) and [ParallelStencil.jl](https://github.com/omlins/ParallelStencil.jl)\n3. Multi-GPU parallelization using [MPI.jl](https://github.com/JuliaParallel/MPI.jl) and [ImplicitGlobalGrid.jl](https://github.com/eth-cscs/ImplicitGlobalGrid.jl)\n4. Multi-GPU computing using Chmy v0.2, a DSL for finite-difference discretisations and automatic kernel fusion\n5. GPU computing with AI technology using [Reactant.jl](https://github.com/EnzymeAD/Reactant.jl)\n6. Solving PDEs inn parallel on CPUs using [PETSc.jl](https://github.com/JuliaParallel/PETSc.jl)\n7. Real-time visualization of multi-process simulations\n\n\n## Hands-On Learning Experience\n\nThe workshop is designed for both HPC users and newcomers curious about accelerating computations. It consists of two parts, featuring:\n\n1. **Fundamentals:** learn core Julia tools for parallel computing through simple, illustrative examples.\n2. **Application:** develop a parallelized version of a serial code and run it on a GPU-accelerated supercomputer.\n\n\n## Who Should Attend?\n\nThis workshop is for researchers, engineers, and developers looking to accelerate scientific computing, machine learning, and other computational tasks. Whether you're already using HPC systems or just getting started, this session will equip you with the knowledge and tools to write high-performance and scalable Julia applications.\n\n\n## Prerequisites\n\nParticipants should have a basic understanding of Julia (functions, modules, control flow, and arrays) and familiarity with standard development tools like Git, SSH, and the Bash command line. No prior experience with distributed computing, or GPU programming is required.\n\nWe look forward to an engaging, inclusive, and knowledge-rich event!\n\n_This workshop will offer the possibility to access one-time HPC resources on-the-fly._", "recording_license": "", "do_not_record": false, "persons": [{"code": "TBSYYM", "name": "Ludovic R\u00e4ss", "avatar": "https://pretalx.com/media/avatars/ZTATFJ_PLThLkL.webp", "biography": "Computational geoscientists with Earth science background. Julia GPU and HPC enthusiast.", "public_name": "Ludovic R\u00e4ss", "guid": "c380de01-7af0-5e6b-ae0e-85c82f5da267", "url": "https://pretalx.com/juliacon-2026/speaker/TBSYYM/"}, {"code": "7QLYPL", "name": "Collin Wittenstein", "avatar": "https://pretalx.com/media/avatars/7QLYPL_fCW8j5j.webp", "biography": "Collin Wittenstein is an incoming PhD student at MIT's Julia Lab. He is completing dual master's degrees in Physics and Computational Sciences, supervised by Hendrik Ranocha, at Johannes Gutenberg University Mainz, Germany, where he previously earned bachelor's degrees in Physics and Mathematics. His research focuses on high-performance numerical methods for PDEs, with applications ranging from dispersive water waves to geothermal energy systems. He is an active contributor to the general Julia open-source ecosystem, and is the author of [GeothermalWells.jl](https://github.com/cwittens/GeothermalWells.jl) and a co-author of [DispersiveShallowWater.jl](https://github.com/NumericalMathematics/DispersiveShallowWater.jl).\nWebsite: [cwittens.github.io](https://cwittens.github.io)", "public_name": "Collin Wittenstein", "guid": "abc6e512-5328-55dd-8224-019a29acf478", "url": "https://pretalx.com/juliacon-2026/speaker/7QLYPL/"}, {"code": "3ATAEN", "name": "Boris Kaus", "avatar": "https://pretalx.com/media/avatars/NHMXEV_6khG9rS.webp", "biography": "Professor of Geodynamics and Geophysics at the Johannes Gutenberg University Mainz (Germany). Interested in using computational models to understand geoscientific processes such as the formation of fault zones, mountain belts, magmatic processes, volcanic eruptions as well as using computational models to estimate the long-term stability of geological reservoirs.", "public_name": "Boris Kaus", "guid": "aa6e6b37-a5b0-5255-bc5e-3b332e15d506", "url": "https://pretalx.com/juliacon-2026/speaker/3ATAEN/"}, {"code": "Z83NH3", "name": "Ivan Utkin", "avatar": null, "biography": "I'm an applied mathematician working in the field of computational glaciology. My interests include GPU computing, supercomputing, computational fluid dynamics, numerical analysis, to name a few.", "public_name": "Ivan Utkin", "guid": "13d1405e-4e1b-50fe-9c9a-d24fd538b020", "url": "https://pretalx.com/juliacon-2026/speaker/Z83NH3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/MRFYNN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/MRFYNN/", "attachments": [{"title": "flyer-MRFYNN", "url": "/media/juliacon-2026/submissions/MRFYNN/resources/MRFYNN_DSoqdBV.png", "type": "related"}]}], "Muschel \u2014 N3": [{"guid": "ebff81c1-978a-5db3-9392-44fb8d2a2b73", "code": "83EN8J", "id": 92848, "logo": "https://pretalx.com/media/juliacon-2026/submissions/83EN8J/image_TEOmWe4.webp", "date": "2026-08-10T10:00:00+02:00", "start": "10:00", "end": "2026-08-10T13:00:00+02:00", "duration": "03:00", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92848-dyadagent-adding-intelligence-to-modeling-and-simulation", "url": "https://pretalx.com/juliacon-2026/talk/83EN8J/", "title": "DyadAgent: Adding intelligence to modeling and simulation", "subtitle": "", "track": "General", "type": "Workshop", "language": "en", "abstract": "What if engineers could go from concept to validated simulation model through conversation alone? DyadAgent, built on Julia and Dyad, makes this possible by combining generative AI with the SciML ecosystem to construct, compile, and rigorously validate high-fidelity physical models from natural language. It handles planning models, creating them, validating them, debugging them and using them in downstream applications such as parameter estimation, model discovery and more. This workshop demonstrates how DyadAgent is reshaping the modeling workflow across engineering domains.", "description": "Building accurate physical models requires domain expertise, careful equation formulation, and iterative testing. DyadAgent is an AI agent built on Julia and Dyad that assists with this process by combining large language models with the SciML ecosystem.\n\nGiven a natural language description of a system, DyadAgent writes Dyad model code, compiles and simulates it, and checks the output against physical expectations. When the results are off, it revises the model using that feedback.\n\nThe agent supports building new models, running transient and steady-state analyses, optimization, and translating legacy code into Dyad. It uses Julia's compiler and simulator as a source of truth rather than treating generated code as correct by assumption.\n\nIn this workshop, participants will work through hands-on examples. Attendees will explore how the agent handles model construction, simulation, and iterative refinement. Along the way, we will discuss how DyadAgent is set up, where it excels and where it has limitations.\n\nThe goal is for participants to come away with a practical understanding of how DyadAgent fits into their modeling workflow and hands-on experience with Dyad.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9ULN8Y", "name": "Venkatesh-Prasad Bhat", "avatar": null, "biography": "He builds Dyad Agent at JuliaHub. He is leveraging generative AI and scientific AI to radically change how modeling and simulation is done.\n\nHe loves to code, paint, write and trek. He likes Julia ecosystem and contributes to it.", "public_name": "Venkatesh-Prasad Bhat", "guid": "942df39f-d850-520b-ae29-ffd78f5f8554", "url": "https://pretalx.com/juliacon-2026/speaker/9ULN8Y/"}, {"code": "EADHRF", "name": "Anas Abdelrehim", "avatar": null, "biography": null, "public_name": "Anas Abdelrehim", "guid": "5051f98b-dd41-50db-bc84-100d61c9d00b", "url": "https://pretalx.com/juliacon-2026/speaker/EADHRF/"}, {"code": "F7XTSA", "name": "Ashutosh Bharambe", "avatar": null, "biography": "Software Engineer in Dyad AI team at JuliaHub.", "public_name": "Ashutosh Bharambe", "guid": "08608d5b-e0ae-5b40-81c5-17a9f6727b0d", "url": "https://pretalx.com/juliacon-2026/speaker/F7XTSA/"}, {"code": "DU7QBS", "name": "Marius Miclu\u021ba-C\u00e2mpeanu", "avatar": null, "biography": "PhD student", "public_name": "Marius Miclu\u021ba-C\u00e2mpeanu", "guid": "709ff96b-8f57-5873-9502-056a0e098a31", "url": "https://pretalx.com/juliacon-2026/speaker/DU7QBS/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/83EN8J/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/83EN8J/", "attachments": []}, {"guid": "6bf3b3e1-ca46-54ea-bfd3-52ac75cb1226", "code": "P39YQD", "id": 92707, "logo": "https://pretalx.com/media/juliacon-2026/submissions/P39YQD/image_Mm6SiyU.webp", "date": "2026-08-10T14:30:00+02:00", "start": "14:30", "end": "2026-08-10T17:30:00+02:00", "duration": "03:00", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92707-dyad-sciml-tutorial-bringing-julia-to-engineers", "url": "https://pretalx.com/juliacon-2026/talk/P39YQD/", "title": "Dyad + SciML Tutorial: Bringing Julia to Engineers", "subtitle": "", "track": "General", "type": "Workshop", "language": "en", "abstract": "The Julia language is a proven technology for technical computing.  So it is only natural for people to build engineering-related tools on top of it.  In this workshop, we'll discuss our Dyad platform for system modeling and how this utilizes both Julia and ModelingToolkit to deliver Scientific Machine Learning (SciML) to engineers in industry.", "description": "In this workshop, participants will be able to do hands-on works using our Dyad platform.  Our Dyad agent has proven remarkably effective at flattening the learning curve for Dyad, SciML and Julia.  This means that participants will be able to jump directly into solving interesting problems with these tools.\n\nDyad provides system modeling capabilities that will allow participants to create system models from nearly every engineering domain (and systems that span domains as well).  From there, they can perform a wide variety of analyses on their models (steady-state analysis, transient analysis, linearization, etc.) to answer important engineering questions.  Adventurous participants will even be able to create their own custom analyses directly in Julia (with the excellent support of our agent).\n\nWe will guide participants through the process of building models graphically and with the agent.  We will then demonstrate how they can use the Dyad language to add new primitives and components for describing both continuous and discrete behavior.  They can then integrate these seamlessly into their system models alongside the extensive collection of components available in our standard libraries.  Our agent will be there every step of the way to help users perform research, create visualizations, build Dyad models and write regression tests.\n\nWe'll provide some exercises for users to work through during the workshop.  But users are not only free to explore on their own, but encouraged to do so.  Our agent can act as your personal tutor during the workshop.  It can answer questions about any part of the Dyad platform and create examples of models and visualizations to help get you started.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9RPXXF", "name": "Michael Tiller", "avatar": "https://pretalx.com/media/avatars/CEQ7XY_Gg1AciJ.webp", "biography": "I am currently the Senior Directory of Product Management for JuliaSim at JuliaHub.  I have built my whole career on my passion for modeling, simulation and software and before coming to work for JuliaHub I had the privilege of working on engineering software at companies like Ford, LMS, Dassault Syst\u00e8mes and Ricardo.", "public_name": "Michael Tiller", "guid": "808c6ad2-58af-51ac-8bd7-435f8db363af", "url": "https://pretalx.com/juliacon-2026/speaker/9RPXXF/"}, {"code": "7ECCXX", "name": "John Batteh", "avatar": "https://pretalx.com/media/avatars/3PJBLZ_h9Q7juU.webp", "biography": "I am currently Senior Lead \u2013 Modeling and Simulation at JuliaHub.  With over 25 years of modeling and simulation experience, I enjoy working with customers to develop software solutions to solve complex multi-domain system simulation problems.  Prior to joining JuliaHub, I worked at Ford Motor Company, several engineering consulting companies, and most recently Modelon.", "public_name": "John Batteh", "guid": "83292990-f347-5309-b8b6-a6a62563531c", "url": "https://pretalx.com/juliacon-2026/speaker/7ECCXX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/P39YQD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/P39YQD/", "attachments": []}]}}, {"index": 2, "date": "2026-08-11", "day_start": "2026-08-11T04:00:00+02:00", "day_end": "2026-08-12T03:59:00+02:00", "rooms": {"Muschel \u2014 N2": [{"guid": "92c94845-0fa4-5bee-8e02-e1a2bae9059e", "code": "9FCTYW", "id": 92573, "logo": "https://pretalx.com/media/juliacon-2026/submissions/9FCTYW/image_hO1dkX5.webp", "date": "2026-08-11T10:00:00+02:00", "start": "10:00", "end": "2026-08-11T13:00:00+02:00", "duration": "03:00", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92573-finding-hidden-performance-costs-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/9FCTYW/", "title": "Finding Hidden Performance Costs in Julia", "subtitle": "", "track": "General", "type": "Workshop", "language": "en", "abstract": "Debugging performance and memory issues in Julia often requires combining multiple tools and correlating their outputs. Participants will learn how to use a runtime-level instrumentation approach to analyze and resolve performance issues in real code, including cases that are difficult to diagnose using existing tools.", "description": "There are many reasons why a Julia application may run slower than expected, ranging from inefficient code patterns to more subtle issues such as dynamic dispatch, unexpected allocations, or inference failures.\n\nWhile Julia offers several tools to analyze performance, correlating runtime behavior with source-level causes can still be challenging. This workshop focuses on a runtime instrumentation approach that provides detailed insight into what your program actually executed and how runtime behavior relates to your source code and can find performance and memory issues that are not directly visible in existing tools.\n\nThe workshop exists out:\n\n- Explain how compiler and runtime instrumentation can be used to understand performance & memory issues.\n\n- Walk through concrete workflows to find and fix common & advanced (hidden) issues.\n\n- Help participants apply these techniques to their own code or sample code.\n\nThe workshop will use [CodeGlass](https://codeglassdotio.github.io/Documentation/), our implementation of this instrumentation approach, to provide hands-on experience. While the underlying instrumentation hooks are under active development for upstream integration into Julia, the implementation used in this session will be available to participants during the workshop.\n\nParticipants should bring a laptop. You are welcome to bring your own Julia code to analyze, and sample code will also be provided. Multiple instructors will be available during the hands-on portion.", "recording_license": "", "do_not_record": false, "persons": [{"code": "VZEBF8", "name": "Joost Godschalk", "avatar": "https://pretalx.com/media/avatars/WSMVD8_JlarBrK.webp", "biography": "I am a software engineer at CodeGlass. I mainly work on building profiling instrumentation layers for different programming languages, including Julia.\n\nI like to work on:\n- Complex and high performance software\n- Low level applications\n- Optimizing existing code", "public_name": "Joost Godschalk", "guid": "419ddf13-9909-5bac-8b15-5b113cb2bd3f", "url": "https://pretalx.com/juliacon-2026/speaker/VZEBF8/"}, {"code": "ZAAWQQ", "name": "Yury Nuzhdin", "avatar": "https://pretalx.com/media/avatars/WEJRES_JZhIvzf.webp", "biography": "Software Architect in ASML working on Julia algorithms in the near real time system.\n[GitHub](https://github.com/tz-lom)", "public_name": "Yury Nuzhdin", "guid": "875f23e9-c926-5991-bb1a-569f3d231ef2", "url": "https://pretalx.com/juliacon-2026/speaker/ZAAWQQ/"}, {"code": "GAHGKP", "name": "Tyrone Krieger", "avatar": "https://pretalx.com/media/avatars/PSVYRC_8FYvPC8.webp", "biography": "I've been writing code since I was 11. Nearly two decades later, I'm still baffled by the fact that most developers spend only 32% of their time actually coding.\nMy professors used to say this was just the way things were. But instead of accepting it, I decided to push back. One step at a time.\nWhy? Because we can.\nAs developers, we build the tools that move entire industries forward. So why not turn that same energy inward and improve our own?\n\nWhat I Love:\n\u2022\tDiving deep into complex codebases\n\u2022\tSharing developer knowledge\n\u2022\tBuilding powerful tools (like CodeGlass)\n\u2022\tExploring superconductors and the Meissner effect (hoverboards when?)\n\u2022\tI Like Trains\n\u2022\tLizard Doggo", "public_name": "Tyrone Krieger", "guid": "d21f4b7c-f71d-506b-9287-1228a0bd706d", "url": "https://pretalx.com/juliacon-2026/speaker/GAHGKP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/9FCTYW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/9FCTYW/", "attachments": []}], "Muschel \u2014 N3": [{"guid": "643ffcdc-04dd-5b2b-b6bb-4c7c8f8be54b", "code": "MCXKBF", "id": 92834, "logo": "https://pretalx.com/media/juliacon-2026/submissions/MCXKBF/image_mfuoL0z.webp", "date": "2026-08-11T10:00:00+02:00", "start": "10:00", "end": "2026-08-11T13:00:00+02:00", "duration": "03:00", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92834-juliaservices-packages-for-running-julia-application-servers-in-production", "url": "https://pretalx.com/juliacon-2026/talk/MCXKBF/", "title": "JuliaServices: Packages for running Julia application servers in production", "subtitle": "", "track": "General", "type": "Workshop", "language": "en", "abstract": "While strong in sciences and technical tasks, Julia has traditionally lacked the kinds of \"application frameworks\" many other languages offer for bundling and deploying applications as servers.\n\nThe JuliaServices GitHub organization has steadily been building up just the kinds of utility/support packages that facilitate \"productionalizing\" your Julia code:\n- Servo.jl: Utility package providing auth middleware, JSON logging, background metric/observability tracking, and endpoint route-defining macros\n- OAuth.jl: full, pure-Julia implementation of OAuth 2.0; client and server functionality\n- Tempus.jl: cron-style scheduler/job executor with abstract storage options\n- Harbor.jl: powerful docker image/container managing from Julia; enables robust testing scenarios with precise \"services\" providers via docker containers.\n- CloudStore.jl: cloud-agnostic \"object store\" package, enabling easy CRUD operations across cloud storage locations\n\nThis workshop will walk through building an entire Julia application from scratch, utilizing JuliaServices packages, resulting in a fully deployed, publicly accessible application.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "WENVTF", "name": "Jacob Quinn", "avatar": "https://pretalx.com/media/avatars/MQ3SJP_Z0yuEym.webp", "biography": "Worked with Julia for a long time. Involved in many \"fundamental\" packages across the ecosystem, web or data related.", "public_name": "Jacob Quinn", "guid": "7bc45afc-d149-5fc1-a5b3-a2c178529ca3", "url": "https://pretalx.com/juliacon-2026/speaker/WENVTF/"}], "links": [{"title": "Repository URL", "url": "https://github.com/quinnj/juliacon-2026-workshop", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/MCXKBF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/MCXKBF/", "attachments": []}]}}, {"index": 3, "date": "2026-08-12", "day_start": "2026-08-12T04:00:00+02:00", "day_end": "2026-08-13T03:59:00+02:00", "rooms": {"Tent \u2014 RW1": [{"guid": "a142387d-aaac-56b9-930d-ca39aed5e753", "code": "C7HJJD", "id": 93046, "logo": "https://pretalx.com/media/juliacon-2026/submissions/C7HJJD/image_CBrDgwF.webp", "date": "2026-08-12T08:30:00+02:00", "start": "08:30", "end": "2026-08-12T08:45:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93046-opening-ceremony", "url": "https://pretalx.com/juliacon-2026/talk/C7HJJD/", "title": "Opening Ceremony", "subtitle": "", "track": "General", "type": "Ceremony", "language": "en", "abstract": "Welcome to Mainz and JuliaCon Global 2026! We are here to provide an open, welcoming, and safe place for sharing knowledge, fostering collaborations, and binding people together. Let's enjoy!", "description": "", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/C7HJJD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/C7HJJD/", "attachments": []}, {"guid": "57db4a57-e489-5809-8ac0-6aa08451193e", "code": "QWW3TE", "id": 93043, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QWW3TE/image_dzSvkvC.webp", "date": "2026-08-12T08:45:00+02:00", "start": "08:45", "end": "2026-08-12T09:45:00+02:00", "duration": "01:00", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93043-haskell-origins-evolution-and-future-by-simon-peyton-jones", "url": "https://pretalx.com/juliacon-2026/talk/QWW3TE/", "title": "Haskell: origins, evolution, and future by Simon Peyton Jones", "subtitle": "", "track": "General", "type": "Keynote", "language": "en", "abstract": "Haskell is an unusual survivor in the Darwinian landscape of programming languages: it is long-lived (36 years old), yet not mainstream; it is both a research platform and a production tool; it pushes the boundaries of what you can do with static types; and (anecdotally) it inspires joy as well as offering utility. In this talk I'll describe how Haskell came to be, and what (in retrospect) I think are its most important contributions. I'll talk about how it has evolved, especially in response to the demands of companies using Haskell in production.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "GYHSET", "name": "Simon Peyton Jones", "avatar": "https://pretalx.com/media/avatars/avatar_EYmjYnz.webp", "biography": "I\u2019m an Engineering Fellow at Epic Games. Prior to that I was a researcher at Microsoft Research Cambridge (1998-2022), and a professor at Glasgow University (1990-1998). I\u2019m a Fellow of the Royal Society, an Honorary Professor of at Glasgow, and an Honorary Distinguished Fellow of the Cambridge University Department of Computer Science and Technology.\n\nI\u2019m interested in the design, implementation, and application of lazy functional languages. In practical terms, that means I spend most of my time on the design and implementation of the language Haskell. In particular, much of my work is focused around the Glasgow Haskell Compiler, and its ramifications.", "public_name": "Simon Peyton Jones", "guid": "90b0ecc7-aad3-5f7b-93dc-6f3847c25b4f", "url": "https://pretalx.com/juliacon-2026/speaker/GYHSET/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QWW3TE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QWW3TE/", "attachments": []}, {"guid": "28f97bb4-64d2-5a6e-988e-bc664f2476b7", "code": "PBYF33", "id": 92365, "logo": "https://pretalx.com/media/juliacon-2026/submissions/PBYF33/image_ITtpWru.webp", "date": "2026-08-12T10:00:00+02:00", "start": "10:00", "end": "2026-08-12T10:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92365-why-is-compilation-as-slow-as-it-is", "url": "https://pretalx.com/juliacon-2026/talk/PBYF33/", "title": "Why is compilation as slow as it is?", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Compilation latency is one of top asks Julia users have and have had over the years. But why is it so hard to make significant leaps on that area. Or even keep the performance the same.\nSince 1.10 there have been significant regressions in (pre)compilation time. What caused those and can we recoup or even get better than 1.10 performance?\nThe talk will go through why compiling Julia quickly is hard, what are we doing to improve that and what users can do to make their code compile faster.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "UWYU7X", "name": "Gabriel Baraldi", "avatar": null, "biography": "Compiler engineer at JuliaHub and open source enthusiast.", "public_name": "Gabriel Baraldi", "guid": "16b89c71-4691-58de-bd70-314fa6729531", "url": "https://pretalx.com/juliacon-2026/speaker/UWYU7X/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/PBYF33/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/PBYF33/", "attachments": []}, {"guid": "067cd9ca-7684-5d6f-9b4f-664ecbd35bcb", "code": "3AZE7E", "id": 92377, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3AZE7E/image_XxcIcIo.webp", "date": "2026-08-12T10:30:00+02:00", "start": "10:30", "end": "2026-08-12T11:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92377-spying-into-julia-s-runtime", "url": "https://pretalx.com/juliacon-2026/talk/3AZE7E/", "title": "Spying into Julia\u2019s Runtime.", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Julia aggressively transforms your code during compilation and execution, which can make it difficult to see what actually runs. This can introduce subtle performance and memory costs that are not directly visible in existing tools. In this talk, we show a compiler and runtime instrumentation approach that provides a runtime-level view of program execution, links runtime behavior back to source code, and show how hidden costs can be uncovered and performance assumptions validated.", "description": "Julia lets you write high-level, generic code while still getting the best performance. To make this possible, the compiler and runtime apply aggressive transformations during compilation and execution. As a result, the code that runs is different from the source code. When this behavior is not fully understood, it can lead to subtle performance issues and unexpected memory use.\n\nJulia already provides useful performance tools. However, these tools often show only part of the picture. Understanding how compilation decisions, dispatch, and allocation behavior interact can require switching between tools and manually connecting the dots. It is not always clear where a runtime event came from in the source code or what caused it. This makes it difficult to answer questions like: where was a conversion inserted, why did a call dispatch dynamically, or why does a function that looks allocation-free allocate at runtime?\n\nWhen examining real-world Julia projects, we repeatedly observed hidden costs, especially allocations caused by implicit conversions, even in code that looks type stable. The compiler may insert conversions to preserve correctness, but their runtime impact is not always visible through existing tools.\n\nTo address this, we introduce a compiler and runtime instrumentation layer that provides a view into what happens during compilation and execution. It records function calls, type conversions, dynamic dispatch, allocations, deallocations, and garbage collection activity across the application. It also links runtime behavior back to call sites and method specializations, including context information such as concrete types involved. We briefly explain the key design decisions behind this approach and how we instrument compilation and execution without changing program behavior.\n\nThis session focuses mainly on practical examples from real Julia code. We look at:\n\n- Implicit conversions that allocate memory\n\n- The runtime impact of macros such as `@assert`, `@inline`, and `@noinline`\n\n- Dynamic dispatch in performance-critical or seemingly type-stable code\n\n- Allocation and garbage collection patterns that are hard to trace back to their source\n\nFor each case, we show how connecting runtime events with context helps you confirm your performance assumptions and better understand how your Julia code actually runs.\n\nFinally, we discuss how this instrumentation work is being prepared for upstreaming into Julia itself. The goal is to make runtime observability part of the language infrastructure, rather than something tied to a single tool.", "recording_license": "", "do_not_record": false, "persons": [{"code": "GAHGKP", "name": "Tyrone Krieger", "avatar": "https://pretalx.com/media/avatars/PSVYRC_8FYvPC8.webp", "biography": "I've been writing code since I was 11. Nearly two decades later, I'm still baffled by the fact that most developers spend only 32% of their time actually coding.\nMy professors used to say this was just the way things were. But instead of accepting it, I decided to push back. One step at a time.\nWhy? Because we can.\nAs developers, we build the tools that move entire industries forward. So why not turn that same energy inward and improve our own?\n\nWhat I Love:\n\u2022\tDiving deep into complex codebases\n\u2022\tSharing developer knowledge\n\u2022\tBuilding powerful tools (like CodeGlass)\n\u2022\tExploring superconductors and the Meissner effect (hoverboards when?)\n\u2022\tI Like Trains\n\u2022\tLizard Doggo", "public_name": "Tyrone Krieger", "guid": "d21f4b7c-f71d-506b-9287-1228a0bd706d", "url": "https://pretalx.com/juliacon-2026/speaker/GAHGKP/"}, {"code": "ZAAWQQ", "name": "Yury Nuzhdin", "avatar": "https://pretalx.com/media/avatars/WEJRES_JZhIvzf.webp", "biography": "Software Architect in ASML working on Julia algorithms in the near real time system.\n[GitHub](https://github.com/tz-lom)", "public_name": "Yury Nuzhdin", "guid": "875f23e9-c926-5991-bb1a-569f3d231ef2", "url": "https://pretalx.com/juliacon-2026/speaker/ZAAWQQ/"}, {"code": "PVVSXK", "name": "Jorge Alberto Vieyra Salas", "avatar": null, "biography": "Born in Mexico City. Studied a Bachelors in Chemical Engineering at UNAM. M.Sc. on Materials Science and Engineering at MIT. Studied PhD at TU Eindhoven on Applied Physics.\nWorked for Philips Research 1 year.\nWorking at ASML for 13 years on algorithms.", "public_name": "Jorge Alberto Vieyra Salas", "guid": "37fbaeb6-ab74-5f69-8b77-40fdd41c7774", "url": "https://pretalx.com/juliacon-2026/speaker/PVVSXK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3AZE7E/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3AZE7E/", "attachments": []}, {"guid": "aa68f428-30ae-560c-bd84-fdb28e1e5c2f", "code": "V9YEJL", "id": 92404, "logo": "https://pretalx.com/media/juliacon-2026/submissions/V9YEJL/image_snpBUlj.webp", "date": "2026-08-12T11:15:00+02:00", "start": "11:15", "end": "2026-08-12T11:45:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92404-appbundler-1-0-bundle-your-julia-application-and-beyond", "url": "https://pretalx.com/juliacon-2026/talk/V9YEJL/", "title": "AppBundler 1.0 - Bundle your Julia application and beyond", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Bundling applications into native installers such as MSIX, Snap, or DMG usually requires the target platform access and host system utilities. This creates a maintenance burden: each platform needs special setup, and compatibility must be preserved as operating systems evolve and utility behavior changes. AppBundler eliminates this burden by using cross-compiled, open-source utilities distributed through Julia\u2019s Yggdrasil registry. From a single UNIX host (Linux, FreeBSD, or macOS)\u2014with MSIX support on Windows\u2014developers can generate native installers through a consistent, reproducible pipeline.\n\nIn this talk, I will explain the architecture behind AppBundler and walk through each supported installer format. I\u2019ll provide an overview of the open-source pipelines that replace host system utilities, then review the configuration files and common options that control application behavior after installation. I will then introduce the AppBundler API design and show how surgical customizations via native override files are supported.\n\nThe session continues with a live demo showing how any application exposing @main can be bundled locally with minimal effort. We\u2019ll add an icon, experiment with configuration options, and iterate on the bundle. I\u2019ll then demonstrate how the same application can be packaged automatically on GitHub Actions, including a walkthrough of the Actions panel and release workflows that build installers across platforms with a single click.\n\nNext, I\u2019ll showcase several larger applications that have been successfully bundled with AppBundler. We\u2019ll discuss common pitfalls that make applications non-relocatable and how to resolve them. I\u2019ll cover JuliaC integration, including a demo of a command-line application using the `--trim` option. I\u2019ll also touch on asset inclusion and referencing via `pkgdir(@__MODULE__)`, and explain how calling `AppEnv.init()` in `@main` populates pkgorigins to enable relocation.\n\nThe talk will conclude with future directions beyond the 1.0 release, including how AppBundler could evolve to package not only Julia applications, but software written in other programming languages.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "KXUWU3", "name": "Janis Erdmanis", "avatar": "https://pretalx.com/media/avatars/BYKXMD_OfhQybm.webp", "biography": "I am a full-stack Julia developer with a Ph.D. in physics from TU Delft, and I enjoy simplifying complex concepts and making the impossible possible. I have thorough experience in Julia, HTTP, QML, cryptographic protocols, and system architectures. Find more about me on [janiserdmanis.org](https://janiserdmanis.org).", "public_name": "Janis Erdmanis", "guid": "fd90434a-7e06-51bf-9c07-5d8cb531a7ab", "url": "https://pretalx.com/juliacon-2026/speaker/KXUWU3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/V9YEJL/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/V9YEJL/", "attachments": []}, {"guid": "d9a15f84-8791-59e7-91c3-cadde0248fae", "code": "RQVGL3", "id": 93470, "logo": "https://pretalx.com/media/juliacon-2026/submissions/RQVGL3/image_969vab6.webp", "date": "2026-08-12T11:45:00+02:00", "start": "11:45", "end": "2026-08-12T12:00:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93470-the-julia-registrator-setup-and-runtime-environment", "url": "https://pretalx.com/juliacon-2026/talk/RQVGL3/", "title": "The Julia Registrator setup and runtime environment", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "The Julia Registrator bot has been quietly running in the background, listening to registration comments and making PRs to the General registry. It is the de facto tool for Julia package registrations. Hi, my name is Nishanth. I am one of the core contributors to Registrator.jl and have been responsible for keeping the Registrator server operational ever since it was first deployed in 2019. In this talk, I would like to share with you all the internal details of Registrator server setup and configuration. The talk will cover:\n- Registrator deployment process\n- Configuration & GitHub settings\n- Logging, Troubleshooting & Debugging\nThis talk will be useful for contributors interested in helping out with development and maintenance. I hope this talk motivates more members of the community to contribute to Registrator for the years to come! Thank you!", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "QVTFDH", "name": "Nishanth H. Kottary", "avatar": "https://pretalx.com/media/avatars/V3AEPY_msQFGOr.webp", "biography": "Hello! I am Nishanth. I work at JuliaHub where I develop Cloud based solutions for hosted Julia applications.", "public_name": "Nishanth H. Kottary", "guid": "b39af570-d1fc-5fbc-9dd7-9bb0f3ccfcb1", "url": "https://pretalx.com/juliacon-2026/speaker/QVTFDH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/RQVGL3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/RQVGL3/", "attachments": []}, {"guid": "0eb347f2-022a-5c1b-adfa-b900079037eb", "code": "GBYL3S", "id": 89210, "logo": "https://pretalx.com/media/juliacon-2026/submissions/GBYL3S/image_m48FUaW.webp", "date": "2026-08-12T12:00:00+02:00", "start": "12:00", "end": "2026-08-12T12:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-89210-juliasubtyping-a-logical-approach-to-types", "url": "https://pretalx.com/juliacon-2026/talk/GBYL3S/", "title": "JuliaSubtyping: A logical approach to types", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "JuliaSubtyping is a new implementation of Julia's core subtyping (and intersection / type-subtraction) algorithms. This talk will motivate the new implementation and explore the theory behind its operation.\n\nDespite being built on complex theory (SAT solvers, QBF, and more), the new design attempts to be more performant and transparently correct than Julia's existing implementation, while also being straightforward to extend with new kinds of reasoning. At the heart of the design a \"logical core\" is combined with a \"type logic\" to form the basis of the algorithm and provides semantics rich enough to describe many interesting subtyping algorithms.\n\nYou can of course expect many challenges along the way. Every theoretician's favorite enemy (undecidability) will rear its head along our journey, along with other practical engineering trade-offs. We'll investigate performance and demonstrate how the new algorithm stands with respect to the old. Finally we'll speculate about what new types of compiler reasoning this kind of typing algorithm may one day support.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "HZBXLP", "name": "Cody Tapscott", "avatar": "https://pretalx.com/media/avatars/HZBXLP_1SZl8Tx.webp", "biography": "A compiler engineer at JuliaHub.", "public_name": "Cody Tapscott", "guid": "31e3d48c-44e1-5ae6-a4d9-83d712b4c75d", "url": "https://pretalx.com/juliacon-2026/speaker/HZBXLP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/GBYL3S/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/GBYL3S/", "attachments": []}, {"guid": "43b7a1af-4ce5-59cb-b87d-015305930f42", "code": "8M3UVS", "id": 104475, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8M3UVS/image_tZdEcvc.webp", "date": "2026-08-12T12:30:00+02:00", "start": "12:30", "end": "2026-08-12T13:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-104475-a-taste-of-verse", "url": "https://pretalx.com/juliacon-2026/talk/8M3UVS/", "title": "A Taste of Verse", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "For the last four years I have been working for Tim Sweeney at Epic Games on a new programming language called Verse. Like Haskell, it is a declarative language: a variable denotes an immutable value, not a mutable cell.  But Verse is unusual and mind-expanding in many ways.  It unifies things that are completely distinct in Haskell; expressions, patterns, and types are all the same thing.  (For example, the 'array' type constructor and the 'map' function for arrays are the same thing, not just related things.)  It includes features from functional logic programming. Choice and arrays have an elegant duality. Function overloading works in a new way. And so on.\n\nIn this talk I'll give you a flavour of what Verse is like and why I am finding it so fascinating.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "GYHSET", "name": "Simon Peyton Jones", "avatar": "https://pretalx.com/media/avatars/avatar_EYmjYnz.webp", "biography": "I\u2019m an Engineering Fellow at Epic Games. Prior to that I was a researcher at Microsoft Research Cambridge (1998-2022), and a professor at Glasgow University (1990-1998). I\u2019m a Fellow of the Royal Society, an Honorary Professor of at Glasgow, and an Honorary Distinguished Fellow of the Cambridge University Department of Computer Science and Technology.\n\nI\u2019m interested in the design, implementation, and application of lazy functional languages. In practical terms, that means I spend most of my time on the design and implementation of the language Haskell. In particular, much of my work is focused around the Glasgow Haskell Compiler, and its ramifications.", "public_name": "Simon Peyton Jones", "guid": "90b0ecc7-aad3-5f7b-93dc-6f3847c25b4f", "url": "https://pretalx.com/juliacon-2026/speaker/GYHSET/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8M3UVS/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8M3UVS/", "attachments": []}, {"guid": "3811a9b8-6832-5d0a-8f3c-23804cf599f8", "code": "37PYYA", "id": 93498, "logo": "https://pretalx.com/media/juliacon-2026/submissions/37PYYA/image_aV8gi35.webp", "date": "2026-08-12T14:30:00+02:00", "start": "14:30", "end": "2026-08-12T15:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93498-lessons-in-deploying-julia-to-productions-services", "url": "https://pretalx.com/juliacon-2026/talk/37PYYA/", "title": "Lessons in deploying Julia to productions services", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "How do you run Julia programs on a machine that is not your own, a machine that is not the one where program was originally developed on? This simple question leads to a surprising array of opinions and options.  In this talk, I  present some of the hard earned lessons from over a decade of deploying Julia applications to production services.", "description": "This talk will discuss strategies, architectures and pitfalls for various options and methods for running and deploying Julia services in production. Issues addressed in this talk will include: \n* The trivial method, and when that is appropriate\n* Packaging code\n* Considerations for HPC clusters\n* Using Docker\n* Using system images for packaging\n* Need for relocability\n* Cloud deployments", "recording_license": "", "do_not_record": false, "persons": [{"code": "JR9GPS", "name": "Avik Sengupta", "avatar": "https://pretalx.com/media/avatars/JR9GPS_qh7TH6b.webp", "biography": "Avik started using Julia on they day it was originally released, and hasn't stopped since. He's an author and contributor for many Julia packages, and works for JuliaHub, Inc. during the day.", "public_name": "Avik Sengupta", "guid": "bbff57bd-ba55-5fa4-9954-e719de47a91f", "url": "https://pretalx.com/juliacon-2026/speaker/JR9GPS/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/37PYYA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/37PYYA/", "attachments": []}, {"guid": "a5979f50-50ad-5298-8996-f1a458ede4c4", "code": "NVEP3P", "id": 93353, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NVEP3P/image_Enzu9lH.webp", "date": "2026-08-12T15:00:00+02:00", "start": "15:00", "end": "2026-08-12T15:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93353-building-and-shipping-omakase-julia-distributions", "url": "https://pretalx.com/juliacon-2026/talk/NVEP3P/", "title": "Building and Shipping Omakase Julia Distributions", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "What if `using Plots, ModelingToolkit` was instantaneous, the first time on a fresh Julia installation? What if your students, users, and/or colleagues could run a command to install something and then everything \"just worked\"? `julia` was never really good at this; we optimized the vanilla distribution for flexibility, which is somewhat at odds with a smooth first-time experience. But this flexibility doesn't fit everyone. We'll present a set of tools to curate Julia distributions for your specific user base, replacing some flexibility with a more reasonable green-path experience.", "description": "Many advancements in the latest Julia versions (v1.11 and v1.12) have focused on making the core of the language leaner for users who want flexibility and smaller binaries. While we are happy to have these features available in the language, we have noticed the average user does not prioritize either of these two for their day-to-day work. More importantly, we have found they have a less optimal experience using the language for common workflows due to these changes. \n\nThe situation became a significant user experience issue as we were building out the Dyad stack, as it heavily depends on a large part of the ecosystem, including over 400 of the top Julia packages, the full SciML stack, and proprietary algorithms. In our internal testing, users with machines as little as a few years old could easily spend over 30 minutes getting set up before they could start to run analyses for models they created.\n\nWe found that the lean, approximately 300MB, download for Julia v1.11, or the performance gains from a very custom `JULIA_CPU_TARGET`, did not address our users' needs. Discomfort with these options being the defaults has also surfaced multiple times from the community, in Julia\u2019s Slack and several [Discourse](https://discourse.julialang.org/t/julia-1-11-is-slower-than-1-10/121077) [posts](https://discourse.julialang.org/t/new-julia-versions-higher-pluto-loading-times/135187). There is even a tool to [track slowness](https://juliaecosystembenchmarks.github.io/web-explorer/#tasks=Project-Path)!\n\nIn this talk, we present the solution used to deliver Dyad Studio (a JuliaHub product) and the tooling you can use to prepare custom distributions to address your and your users' needs. These distributions can be shared as tarballs or even using a custom `juliaup` server. \n\nWe show this approach significantly reduces time-to-first-plot, including installation, even for complex analyses involving precompilation-heavy workloads, such as Dyad's modeling and analysis workflows.\n\nWe consider this a viable solution for both enterprise users of Julia and for communities of users with similar needs.", "recording_license": "", "do_not_record": false, "persons": [{"code": "CDRVXA", "name": "Panagiotis Georgakopoulos", "avatar": "https://pretalx.com/media/avatars/NDRZYU_39AxTQb.webp", "biography": "Proudly developing Dyad with JuliaHub and improving the julia ecosystem in the meantime, removing one `sleep(1)` at a time. Pluto maintainer. Past lives include software engineer, a business analyst, a consultant, a data entry intern, a waiter and a sailor.", "public_name": "Panagiotis Georgakopoulos", "guid": "f8f896f9-e95e-5023-9935-fa9d6bb7dcea", "url": "https://pretalx.com/juliacon-2026/speaker/CDRVXA/"}, {"code": "JYGXSK", "name": "Joris Kraak", "avatar": "https://pretalx.com/media/avatars/VCBWCT_KywRhnV.webp", "biography": "Joris is the technical team lead for the Dyad Studio product team. He has been shipping products built on top of Julia, such as Dyad Studio and JuliaSim, to users for close to 5 years and deploying Julia powered web applications for over a decade.", "public_name": "Joris Kraak", "guid": "6a5b800a-ecf9-5dc8-a89d-b14f49748222", "url": "https://pretalx.com/juliacon-2026/speaker/JYGXSK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NVEP3P/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NVEP3P/", "attachments": []}, {"guid": "290017c1-3222-5f40-8d6f-d81284efebe4", "code": "LYBBPW", "id": 93444, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LYBBPW/image_Fo3Lhqp.webp", "date": "2026-08-12T15:45:00+02:00", "start": "15:45", "end": "2026-08-12T16:15:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93444-bringing-order-to-the-seas-defining-type-piracy-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/LYBBPW/", "title": "Bringing Order to the Seas: Defining Type-piracy in Julia", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Type-piracy is often said to be \"defining foreign behavior over foreign types\".\n\nSounds intuitive, but in practice things get complicated quickly: A basic type like `ForeignType{Float64}` may be \"obviously foreign\", but how do we extend that notion to Julia's full type language including `Union` and `where`? Are all forms of type-piracy equally bad, or is \"type-privateering\" a thing? Most importantly, how does this affect your code in practice?\n\nThis talk will attempt to construct a mathematically precise definition for the \"essence\" of type-piracy. We'll discuss how determining type-piracy might be automated and what it would mean to make this a \"first-class\" restriction in Julia. Do we lose expressivity, gain performance? What designs would this affect in practice?\n\nAlong the way, we'll examine cases of \"type-piracy in the wild\" in the community (and even in Julia's own standard libraries!). These controversial case studies will serve as a litmus test to see if we can conquer the type seas, or if the age of the type-privateer continues.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "HZBXLP", "name": "Cody Tapscott", "avatar": "https://pretalx.com/media/avatars/HZBXLP_1SZl8Tx.webp", "biography": "A compiler engineer at JuliaHub.", "public_name": "Cody Tapscott", "guid": "31e3d48c-44e1-5ae6-a4d9-83d712b4c75d", "url": "https://pretalx.com/juliacon-2026/speaker/HZBXLP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LYBBPW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LYBBPW/", "attachments": []}, {"guid": "33ab2777-8ee9-58fe-b3fa-e5f2df43f785", "code": "GDBGPJ", "id": 89319, "logo": "https://pretalx.com/media/juliacon-2026/submissions/GDBGPJ/image_lw1B5Ab.webp", "date": "2026-08-12T16:15:00+02:00", "start": "16:15", "end": "2026-08-12T16:30:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-89319-julia-novices-about-the-carpentries-lessons-for-teaching-julia", "url": "https://pretalx.com/juliacon-2026/talk/GDBGPJ/", "title": "julia-novices -- About The Carpentries lessons for teaching julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "The Carpentries is a nonprofit organization that teaches software engineering and data science skills to researchers worldwide through hands-on workshops.\n \nSince 2021, I have maintained a Carpentries Incubator lesson focused on teaching Julia, which I also integrate into my university courses. In this talk, I will talk about strengths and weaknesses of the current lesson design as well as presenting a new different lesson. I will also highlight opportunities for the Julia community to participate in collaborative lesson development.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "ADSTWS", "name": "Simon Christ", "avatar": "https://pretalx.com/media/avatars/ADSTWS_1PLM0ug.webp", "biography": "Research Software Engineer in the Computational Biology department at Leibniz University Hannover.\n\nPhD in Physics.\n\nThe Carpentries instructor.\n\nJulia enthusiast.", "public_name": "Simon Christ", "guid": "4088b933-2f67-5b86-b7de-d43c25a7d19d", "url": "https://pretalx.com/juliacon-2026/speaker/ADSTWS/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/GDBGPJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/GDBGPJ/", "attachments": []}, {"guid": "0276ef02-5064-5b87-be72-e660f8107e9a", "code": "CZDYJF", "id": 92795, "logo": "https://pretalx.com/media/juliacon-2026/submissions/CZDYJF/image_1M7wo0b.webp", "date": "2026-08-12T16:30:00+02:00", "start": "16:30", "end": "2026-08-12T16:45:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92795-how-is-julia-both-dynamic-and-fast", "url": "https://pretalx.com/juliacon-2026/talk/CZDYJF/", "title": "How is Julia both dynamic and fast?", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "This talk considers the features Julia includes, and more importantly, omits, in service of a speedier implementation, as well as how the language might grow in the future.", "description": "There are many ways a programming language can be dynamic, but not all them are equal.  By retreating from fully dynamic behaviour in a few cases, Julia is able to strike a balance that allows its dynamic features to be used pervasively, rather than only where performance allows.\n\nThis talk compares Julia's multiple dispatch mechanism with Common Lisp's, explaining how the \"world age\" concept enables Julia's compiler to make dispatch dramatically faster, while keeping eval() useful for interactive development. We also cover why Julia's immutable structs are valuable in more ways than only their unmodifiable nature.\n\nFinally, we look at some experimental Julia features that explore the dynamism-vs-speed frontier further, including opaque closures.", "recording_license": "", "do_not_record": false, "persons": [{"code": "3HXBJT", "name": "Sam Schweigel", "avatar": null, "biography": "Compiler engineer at JuliaHub, Inc.", "public_name": "Sam Schweigel", "guid": "a14589f5-1021-5059-b760-99ec48d01516", "url": "https://pretalx.com/juliacon-2026/speaker/3HXBJT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/CZDYJF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/CZDYJF/", "attachments": []}, {"guid": "a8c9ca7b-d0dc-5f86-8227-645ff53f6585", "code": "TBHR8T", "id": 93362, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TBHR8T/image_obGhLxI.webp", "date": "2026-08-12T16:45:00+02:00", "start": "16:45", "end": "2026-08-12T17:00:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93362-relint-jl-and-argus-jl-are-merging-into-a-powerful-julia-linter", "url": "https://pretalx.com/juliacon-2026/talk/TBHR8T/", "title": "ReLint.jl and Argus.jl are merging into a powerful Julia linter", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Static analysis for Julia is still an underdeveloped domain. The linting ecosystem is looking especially barren \u2013 practically one package is used for writing and running linting rules, [StaticLint](https://github.com/julia-vscode/StaticLint.jl), even though it is difficult to extend with new rules or features. [ReLint](https://github.com/RelationalAI-oss/ReLint.jl) was built to address some of StaticLint's limitations, improving upon extensibility and interoperability. However, it too suffers from shortcomings, especially in terms of flexibility. [Argus](https://github.com/iuliadmtru/Argus.jl), mainly a pattern matching framework for Julia syntax, offers a powerful and expressive language for writing code patterns and linting rules. This presentation shows the result of combining ReLint and Argus into a new version of ReLint that provides a built-in set of rules, a DSL for extending the default set with custom rules and CI/CD integration.", "description": "ReLint was developed at [RelationalAI](https://www.relational.ai) as an alternative to StaticLint. It already provides a set of \\~50 built-in linting rules that can be run right after installing the package. At RelationalAI, it is used on a daily basis on a fairly large codebase (\\~700K lines of Julia code).\n\nOne of the main goals of ReLint was to make it easier to write new rules. This was achieved with a design that allowed defining rules as types with associated `check` methods. For a simple rule, the definition might be as simple as the following:\n```julia\nstruct AsyncRule <: ViolationLintRule end\nfunction check(t::AsyncRule, x::EXPR)\n    msg = \"Use `@spawn` instead of `@async`.\"\n    generic_check(t, x, \"@async hole_variable\", msg)\n    generic_check(t, x, \"Threads.@async hole_variable\", msg)\nend\n```\nHowever, complicated rules would require complex `check` functions that inspect the internals of Julia code. That is, _of the `EXPR` representation of Julia code_, which is not even the _official_ representation. Hence, while this design is a major improvement upon StaticLint, it is still lacking expressivity, and it is not as tightly integrated with the Julia compiler as it could be.\n\nArgus proposes a pattern matching framework that represents ASTs using [JuliaSyntax](https://github.com/JuliaLang/JuliaSyntax.jl), the official compiler front-end. It provides a DSL for expressing patterns that resemble the code they are meant to match. These patterns are used to define linting rules in an elegant manner:\n```julia\nVIOLATIONS = RuleGroup(\"violations\")\n@define_rule_in_group VIOLATIONS \"@async\" begin\n    description = \"Use `@spawn` instead of `@async`.\"\n\n    pattern = @pattern ~or(\n        @async({_}...),\n        Threads.@async({_}...)\n    )\nend\n```\nThere is no need for the user to write a custom `check` method \u2013 Argus's rule matching mechanism handles everything!\n\nArgus's powerful pattern abstractions \u2013 syntax classes \u2013 offer a flexibility that ReLint's string-based patterns cannot match. The following rule, which warns against containers with abstract type parameters, would be impossible to express in ReLint:\n```julia\n@define_rule_in_group PERFORMANCE \"containers-with-abstract-type-params\" begin\n    description = \"Avoid containers with abstract type parameters.\"\n    pattern = @pattern {container}{{_}..., {t:::abstract_type}, {_}...}\nend\n```\n\nArgus seems to be the better choice for writing linting rules, but it is limited in terms of workflow integration. Here ReLint is a step ahead, embedding linting in GitHub actions and workflows. It allows automatic CI/CD checks for all rules and provides a pre-commit hook for catching severe violations especially early in the development cycle.\n\nBy integrating Argus, the latest ReLint release addresses the need for better static analysis tooling for Julia. The new linter still has the benefits of CI/CD integration and a pre-defined set of rules, but it greatly improves user experience and expressive power. Developers can now easily extend the default rule set by writing their own rules that look just like the code they want to match.\n\nThe package is openly available under the MIT license.\n\nFor more information you can watch the [JuliaCon 2025 presentation on ReLint](https://www.youtube.com/watch?v=H69bGbT0ZNQ) or the [JuliaCon Paris 2025 presentation on Argus](https://www.youtube.com/watch?v=GRRtlfCt0rQ).", "recording_license": "", "do_not_record": false, "persons": [{"code": "7GCJMY", "name": "Iulia Dumitru", "avatar": null, "biography": "Computer Science and Engineering graduate, working on static analysis tooling for Julia.", "public_name": "Iulia Dumitru", "guid": "2fa1db65-1c63-5a9f-aab2-ed33868295f5", "url": "https://pretalx.com/juliacon-2026/speaker/7GCJMY/"}, {"code": "NP37AZ", "name": "Alexandre Bergel", "avatar": "https://pretalx.com/media/avatars/NP37AZ_jJtz6jk.webp", "biography": "[Alexandre Bergel](https://bergel.eu/) is a Computer Scientist at RelationalAI, Switzerland. Until 2022, he was an Associate Professor and researcher at the University of Chile. Alexandre Bergel and his collaborators carry out research in software engineering. His interest includes designing tools and methodologies to improve the overall performance and internal quality of software systems and databases by employing profiling, visualization, and artificial intelligence techniques.\n\nAlexandre Bergel has authored over 170 articles, published in international and peer-reviewed scientific forums, including the most competitive conferences and journals in the field of software engineering. Alexandre has served on over 175 program committees for international events. Several of his research prototypes have been turned into products and adopted by major companies in the semiconductor industry, certification of critical software systems, and the aerospace industry.", "public_name": "Alexandre Bergel", "guid": "7837064b-4f27-516e-b137-f3391bb51a74", "url": "https://pretalx.com/juliacon-2026/speaker/NP37AZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TBHR8T/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TBHR8T/", "attachments": []}, {"guid": "8957a575-c3c9-5e61-829f-1f64f334b51e", "code": "WVE37X", "id": 92841, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WVE37X/image_GUPRcf1.webp", "date": "2026-08-12T17:00:00+02:00", "start": "17:00", "end": "2026-08-12T17:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92841-juliac-jl-and-the-state-of-trim", "url": "https://pretalx.com/juliacon-2026/talk/WVE37X/", "title": "JuliaC.jl and the state of --trim", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "In order to improve the tooling around --trim, we've developed JuliaC.jl. It's a package similar to PackageCompiler, but with a focus on bundling trimming applications.\nIt has features like bundling, setting rpaths and more interestingly. Privatization, allowing a JuliaC library to be loaded by Julia", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "UWYU7X", "name": "Gabriel Baraldi", "avatar": null, "biography": "Compiler engineer at JuliaHub and open source enthusiast.", "public_name": "Gabriel Baraldi", "guid": "16b89c71-4691-58de-bd70-314fa6729531", "url": "https://pretalx.com/juliacon-2026/speaker/UWYU7X/"}, {"code": "STZAPU", "name": "Jeff Bezanson", "avatar": "https://pretalx.com/media/avatars/JWDSCE_L1lwVeY.webp", "biography": "Co-creator of the Julia language and co-founder of JuliaHub, Inc.", "public_name": "Jeff Bezanson", "guid": "0f4f7e19-c31f-5938-b983-e502161711f2", "url": "https://pretalx.com/juliacon-2026/speaker/STZAPU/"}, {"code": "HZBXLP", "name": "Cody Tapscott", "avatar": "https://pretalx.com/media/avatars/HZBXLP_1SZl8Tx.webp", "biography": "A compiler engineer at JuliaHub.", "public_name": "Cody Tapscott", "guid": "31e3d48c-44e1-5ae6-a4d9-83d712b4c75d", "url": "https://pretalx.com/juliacon-2026/speaker/HZBXLP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WVE37X/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WVE37X/", "attachments": []}, {"guid": "55da342c-99a1-5a12-b65f-01dab1426b4f", "code": "XSYZLV", "id": 93042, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XSYZLV/image_SFyHpcn.webp", "date": "2026-08-12T17:45:00+02:00", "start": "17:45", "end": "2026-08-12T18:45:00+02:00", "duration": "01:00", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93042-resolving-the-edge-of-the-universe-imaging-black-holes-with-julia", "url": "https://pretalx.com/juliacon-2026/talk/XSYZLV/", "title": "Resolving the Edge of the Universe: Imaging Black Holes with Julia", "subtitle": "", "track": "General", "type": "Keynote", "language": "en", "abstract": "Black holes were once thought to be unobservable. In 2019, the Event Horizon Telescope (EHT) changed that by producing the first horizon-scale image of M87*. That breakthrough was not only an achievement in astronomy, but also in scientific computing: every step, from building a planet-sized telescope to reconstructing the final image, depended on extensive computation. In this talk, I will show how Julia is helping push black hole imaging into its next era. Its combination of speed, flexibility, and composability has made it a core part of the EHT software stack, powering modern workflows for imaging, modeling, and uncertainty quantification. Julia is also helping define the future of black hole astronomy. The Black Hole Explorer (BHEX), a NASA space mission concept, aims to produce the highest-resolution images of black holes ever made, resolving regions of spacetime where light can orbit the black hole itself. In the final part of the talk, I will highlight how Julia will form the foundation of the BHEX analysis pipeline and demonstrate its potential as the primary software platform for a next-generation scientific instrument.", "description": "Black holes were once thought to be unobservable. In 2019, the Event Horizon Telescope (EHT) changed that by producing the first horizon-scale image of M87*. That breakthrough was not only an achievement in astronomy, but also in scientific computing: every step, from building a planet-sized telescope to reconstructing the final image, depended on extensive computation. In this talk, I will show how Julia is helping push black hole imaging into its next era. Its combination of speed, flexibility, and composability has made it a core part of the EHT software stack, powering modern workflows for imaging, modeling, and uncertainty quantification. Julia is also helping define the future of black hole astronomy. The Black Hole Explorer (BHEX), a NASA space mission concept, aims to produce the highest-resolution images of black holes ever made, resolving regions of spacetime where light can orbit the black hole itself. In the final part of the talk, I will highlight how Julia will form the foundation of the BHEX analysis pipeline and demonstrate its potential as the primary software platform for a next-generation scientific instrument.", "recording_license": "", "do_not_record": false, "persons": [{"code": "RTYZ3C", "name": "Paul Tiede", "avatar": "https://pretalx.com/media/avatars/BALZVM_Ut6iB3p.webp", "biography": "Paul Tiede", "public_name": "Paul Tiede", "guid": "741cb15e-f542-5a6f-be6b-c8adf56d62bc", "url": "https://pretalx.com/juliacon-2026/speaker/RTYZ3C/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XSYZLV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XSYZLV/", "attachments": []}], "Muschel \u2014 N1": [{"guid": "1f45470d-8367-5b48-a495-762a788b5ac8", "code": "WGRWHS", "id": 91085, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WGRWHS/image_4OybWXf.webp", "date": "2026-08-12T10:00:00+02:00", "start": "10:00", "end": "2026-08-12T10:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-91085-disrupt-drug-design", "url": "https://pretalx.com/juliacon-2026/talk/WGRWHS/", "title": "Disrupt Drug Design", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Long talk", "language": "en", "abstract": "tbd", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"code": "GFZHWF", "name": "pato", "avatar": "https://pretalx.com/media/avatars/GFZHWF_n2WljtJ.webp", "biography": "Diplom-Chemiker 2002 (TUM)PhD in Pharmaceutical Chemistry 2003-2006 (UMR)\nBoehringer Ingelheim 2006-2009\nMerck KGaA 2009-2018\nTU Dortmund 2018-2022\nJGU Mainz since 2022", "public_name": "pato", "guid": "33b4cc2a-a84c-526f-9cd5-ee3519d29b49", "url": "https://pretalx.com/juliacon-2026/speaker/GFZHWF/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WGRWHS/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WGRWHS/", "attachments": []}, {"guid": "3589f5d3-eb40-5bdb-8530-abe90ac93110", "code": "BXHNXX", "id": 92821, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BXHNXX/image_h7JGUku.webp", "date": "2026-08-12T10:30:00+02:00", "start": "10:30", "end": "2026-08-12T10:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92821-julia-for-quantitative-systems-pharmacology", "url": "https://pretalx.com/juliacon-2026/talk/BXHNXX/", "title": "Julia For Quantitative Systems Pharmacology", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Short talk", "language": "en", "abstract": "Quantitative Systems Pharmacology (QSP) is a fast-growing discipline in model informed drug development. We simulate virtual patients to enhance decisions across the pharmaceutical pipeline to get the right medicines to the right patients faster. QSP models are large and often stiff differential equation systems. Workflows include intensive simulations such as parameter optimization and sensitivity analyses. In this talk, we will discuss how we utilize Julia for QSP workflows.", "description": "Contents of presentation:\n-\tBackground: QSP \n-\tModel building process and pain points \n-\tSimulations and performance improvement with Julia\n-\tTwo-language problem: Balancing model accessibility and performance", "recording_license": "", "do_not_record": true, "persons": [{"code": "LKVESW", "name": "Elisabeth Roesch", "avatar": null, "biography": "Dr. Elisabeth Roesch is a Quantitative Systems Pharmacology at Sanofi. She earned her PhD in Theoretical Systems Biology from the University of Melbourne, Australia. She has researched and published about the use of the Julia programming language in Systems Biology.", "public_name": "Elisabeth Roesch", "guid": "53770b24-3ba1-53df-9960-30a7caeffe98", "url": "https://pretalx.com/juliacon-2026/speaker/LKVESW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BXHNXX/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BXHNXX/", "attachments": []}, {"guid": "5ac94693-8080-5618-9ff8-3db3399a1cdc", "code": "7B9YZJ", "id": 93463, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7B9YZJ/image_mw0OkcC.webp", "date": "2026-08-12T10:45:00+02:00", "start": "10:45", "end": "2026-08-12T11:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93463-staged-programming-in-pharmacometrics", "url": "https://pretalx.com/juliacon-2026/talk/7B9YZJ/", "title": "Staged programming in pharmacometrics", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Short talk", "language": "en", "abstract": "The most popular modeling framework in pharmacometrics is nonlinear mixed-effects modeling. In the pharmacometric application of the model framework, the time dimension is typically a solution to a dynamical system. The combination of the nonlinearity of the statistical model and the computational costs of numerically solving differential equations has made the use of staged programming necessary in all available software packages. The staged programming requirement has historically been associated with a large maintenance burden, but staged programming is also what Julia was designed for. In this talk, I outline the modeling class most popular in pharmacometrics, give an overview of how historical solutions are used, and explain how Julia is well suited for pharmacometrics.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "HMXKYU", "name": "Andreas Noack", "avatar": "https://pretalx.com/media/avatars/GDCCSG_sR7fEFF.webp", "biography": "Andreas Noack is the Vice President of Product Development at PumasAI. His expertise includes nonlinear mixed-effects modeling, numerical linear algebra, and parallel computing, with contributions to several open source Julia packages and the proprietary Pumas application for pharmacometrics. He has a background in econometrics and computer science with a PhD in economics from the University of Copenhagen and three years of experience as a postdoctoral associate at MIT's Computer Science and Artificial Intelligence Laboratory.", "public_name": "Andreas Noack", "guid": "869d614f-7a08-5cf2-a000-d45035238466", "url": "https://pretalx.com/juliacon-2026/speaker/HMXKYU/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7B9YZJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7B9YZJ/", "attachments": []}, {"guid": "b6a02560-7167-5601-842a-70a5c229d022", "code": "NEH3S3", "id": 92691, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NEH3S3/image_QYYa8AC.webp", "date": "2026-08-12T11:00:00+02:00", "start": "11:00", "end": "2026-08-12T11:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92691-nolimits-jl-a-flexible-julia-framework-for-nonlinear-neural-and-latent-state-mixed-effects-modeling", "url": "https://pretalx.com/juliacon-2026/talk/NEH3S3/", "title": "NoLimits.jl: A flexible Julia framework for nonlinear, neural and latent-state mixed-effects modeling", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Long talk", "language": "en", "abstract": "NoLimits.jl is a flexible open-source Julia framework for nonlinear modeling and parameter estimation with random effects. It supports ODE-based mechanistic models, hidden Markov models, hybrid mechanistic-machine learning components, normalizing flows, and nested random-effect structures within a unified interface. By leveraging Julia\u2019s composability, it enables scalable frequentist and Bayesian inference beyond the constraints of traditional open-source mixed-effects software.", "description": "Nonlinear mixed-effects models are central to longitudinal and hierarchical data analysis. While many established platforms provide robust and production-ready workflows, extending open-source tools to incorporate modern machine learning components, flexible random-effect distributions, or complex latent structures often requires substantial methodological effort.\n\nThis talk introduces NoLimits.jl (NOn LInear MIxed effecTS), an open-source Julia framework for nonlinear modeling with random effects, designed for composability and rapid methodological experimentation. The framework enables users to combine mechanistic models (including DifferentialEquations.jl-based ODE systems), hierarchical random effects with multiple nested levels, hidden Markov structures, and differentiable machine learning components within a unified interface. \n\nNoLimits.jl integrates seamlessly with the Julia ecosystem, leveraging Distributions.jl, DifferentialEquations.jl, Lux.jl, Optimization.jl, and Turing.jl for both frequentist and Bayesian inference. \n\nThe focus of the talk is to present the capabilities of the framework and to outline potential improvements that can benefit from the community. Using real data examples, I will showcase the package capabilities by demonstrating practical modeling workflows, including:\n1) Embedding soft decision trees and neural networks as differentiable components alongside mechanistic ODE models,\n2) Specifying hidden Markov models within nonlinear mixed-effects structures for individualized latent transition analysis,\n3) Extending classical Gaussian random effects using normalizing flows to capture skewed or multimodal heterogeneity.\n\nI will conclude the talk with an outline of opportunities for collaboration and contribution, as well as future directions, including extending the framework toward federated learning capabilities.", "recording_license": "", "do_not_record": false, "persons": [{"code": "98RUQX", "name": "Manuel Huth", "avatar": "https://pretalx.com/media/avatars/Z3LQFQ_nVwBgSi.webp", "biography": "Manuel develops statistical methods and software in Julia, R, and Python focusing on nonlinear mixed-effects models, longitudinal data, and federated learning. He began using Julia in 2023 and now builds research software leveraging its composability and automatic differentiation ecosystem. He is the author of Coconots.jl and NoLimits.jl. Manuel is a PhD student in Mathematics in the group of Jan Hasenauer at the University of Bonn.", "public_name": "Manuel Huth", "guid": "e42bc9c1-9590-5f13-93bb-77fd88363180", "url": "https://pretalx.com/juliacon-2026/speaker/98RUQX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NEH3S3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NEH3S3/", "attachments": [{"title": "flyer-NEH3S3", "url": "/media/juliacon-2026/submissions/NEH3S3/resources/NEH3S3_8cpt3sm.png", "type": "related"}]}, {"guid": "0539622b-8fa9-5e4b-b5c3-5759f783e6eb", "code": "VEWE33", "id": 92727, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VEWE33/image_7x2whCJ.webp", "date": "2026-08-12T11:30:00+02:00", "start": "11:30", "end": "2026-08-12T11:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92727-feature-based-prediction-of-preclinical-pharmacokinetic-profiles-using-machine-learning-and-compartmental-modeling", "url": "https://pretalx.com/juliacon-2026/talk/VEWE33/", "title": "Feature based  prediction of preclinical pharmacokinetic profiles using machine learning and compartmental modeling", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Short talk", "language": "en", "abstract": "In this talk physics informed neural networks are presented to predict the plasma concentration time profiles of preclinical species and human after i.v. bolus administration. The predictions are based on the numerical representation of chemical structures or amino acid sequences and compartmental modeling is used as the physical part to describe the pharmacokinetic profiles.", "description": "For healthcare and drug discovery, particularly during the earlier stages of target identification and hit finding, recent advancements in data availability, computational power, and methods (machine learning (ML) and artificial intelligence (AI)) have shown great promise to further rationalized the drug development pipeline initiating a transition from a \u201cdata generation and triaging\u201d to a \u201dresult prediction and verification\u201d pipeline. \nWe present different feature-based concepts of predicting in vivo PK profiles for chemical and biological identities derived from their chemical structure or amino acid sequences with physics informed neural networks. \nThe first example evaluates the performance of different state of the art (hybrid) methods for predicting PK profiles based on chemical structures and benchmark their performance on a common data set for pre-clinical species.\nNext, a method for predicting non-rodent PK profiles for using allometric AI approach is introduced. The last concept utilizes protein large language models and sequence derived features to predict in vivo clearance and PK profile in pre-clinical species.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ERWXBJ", "name": "Felix Jost", "avatar": null, "biography": "Pharmacometrician at Sanofi working in the preclinical PK/PD modeling team supporting research projects via the information gain through mathematical modeling and human dose predictions.", "public_name": "Felix Jost", "guid": "d35e5503-5006-51d3-97eb-d13a3106a773", "url": "https://pretalx.com/juliacon-2026/speaker/ERWXBJ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VEWE33/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VEWE33/", "attachments": []}, {"guid": "e67435f4-beec-5427-b2f3-e2cbbc305c76", "code": "MGNSFV", "id": 92910, "logo": "https://pretalx.com/media/juliacon-2026/submissions/MGNSFV/image_ljpZPv3.webp", "date": "2026-08-12T11:45:00+02:00", "start": "11:45", "end": "2026-08-12T12:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92910-vpopmip-a-mixed-integer-programming-approach-to-virtual-population-generation", "url": "https://pretalx.com/juliacon-2026/talk/MGNSFV/", "title": "VPopMIP: A Mixed-Integer Programming Approach to Virtual Population Generation", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Short talk", "language": "en", "abstract": "Virtual Populations (VPops) are widely used in Quantitative Systems Pharmacology (QSP) to represent variability in patient responses to therapy using parameterized dynamical models. Unlike traditional modeling approaches that focus on average treatment effects, VPop methods aim to reproduce the full distribution of clinical outcomes observed in trials.\nWe introduce VPopMIP, a Julia package implementing a Mixed-Integer Programming (MIP) formulation for generating VPops that match clinical endpoints. In contrast to many existing approaches that require individual-level patient data, VPopMIP enables calibration to published clinical summary statistics (e.g., response rates, medians, and confidence intervals), which are more commonly available in practice.\nThe method formulates virtual patient selection as a constrained optimization problem that enforces agreement with multiple outcome measures across therapies.\nWe demonstrate the methodology using a solid tumor model with multiple efficacy endpoints across treatment regimens. The results illustrate how MIP-based selection provides an efficient way to construct clinically consistent virtual populations.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "QCSDBK", "name": "Ivan Borisov", "avatar": "https://pretalx.com/media/avatars/DMYTV9_d2zv9jO.webp", "biography": "Ivan Borisov is a mathematician and a software developer at InSysBio CY. His work focuses on the development and enhancement of mathematical and computational methods in Systems Biology and Quantitative Systems Pharmacology.", "public_name": "Ivan Borisov", "guid": "cc1ef899-8b5b-5815-8a7b-ddf5bda6e0c8", "url": "https://pretalx.com/juliacon-2026/speaker/QCSDBK/"}, {"code": "DX7TYW", "name": "Evgeny Metelkin", "avatar": "https://pretalx.com/media/avatars/HUBBY9_uvV0kRk.webp", "biography": "Evgeny Metelkin, PhD, is a researcher and software engineer specializing in quantitative systems pharmacology, mechanistic modeling, and computational biology. He develops methods and open-source software for reproducible modeling, identifiability analysis, virtual populations, and model interoperability.", "public_name": "Evgeny Metelkin", "guid": "f9b086b7-7cd4-5aa9-b82d-bdde20dfef12", "url": "https://pretalx.com/juliacon-2026/speaker/DX7TYW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/MGNSFV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/MGNSFV/", "attachments": []}, {"guid": "a46d2f79-38f4-5912-9f49-2f3b9ca99bf2", "code": "SA7F9J", "id": 92934, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SA7F9J/image_3E3xDBb.webp", "date": "2026-08-12T12:00:00+02:00", "start": "12:00", "end": "2026-08-12T12:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92934-reproducible-bioinformatics-pipelines-in-julia-lessons-from-alphaconformers", "url": "https://pretalx.com/juliacon-2026/talk/SA7F9J/", "title": "Reproducible Bioinformatics Pipelines in Julia: Lessons from AlphaConformers", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Long talk", "language": "en", "abstract": "I will present AlphaConformers, a Julia package to model alternative protein conformations using AlphaFold. This will showcase Julia as an incredible glue language for developing reproducible bioinformatics pipelines. In particular, we will focus on the use of JLL packages as a way to include versioned, cross-platform command-line tools. We will describe the current bioinformatics JLL ecosystem and show how developers in other languages can make their binaries available for Julia users.", "description": "Building modeling and analysis pipelines is a common task when doing molecular modeling and bioinformatics. However, keeping such pipelines reproducible and sharable is not an easy task. Thankfully, Julia's interoperability and reproducibility make it the best glue language for building such pipelines. \nJulia allows us to have everything expressed in a single high-level programming language with performance that pairs that of low-level ones. But, while it solves the two-language problem, it is unlikely that advanced tools, fruit of years of research and development, will be translated to Julia. Therefore, to be able to build on the shoulders of giants and avoid reinventing the wheel, we need a language that can also run and manage outside tools, and Julia is such a language. \nJulia's can easily interoperate with well-established languages in bioinformatics, such as Python and R, thanks to packages such as PythonCall, using CondaPkg to keep the Python environment reproducible, and RCall. But the Julia ecosystem also offers prebuilt, cross-platform native binaries for command-line tools through versioned JLL packages. That allows integrating such tools, possibly developed using languages such as C, C++, Fortran, or Rust, among others, into Julia workflows. Currently, many of the classical and more popular bioinformatics packages are available as JLL packages.\nIn this talk, I will present AlphaConformers, a Julia package for the modeling of protein alternative conformations. It takes advantage of Julia\u2019s Pkg environments and already available JLL packages for common bioinformatics tools, such as FoldSeek and USalign. Therefore, we will showcase the power of Julia for the development of bioinformatics pipelines and workflows.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BVAJXH", "name": "Diego Javier Zea", "avatar": "https://pretalx.com/media/avatars/CQ8WFY_GcD84tO.webp", "biography": "Associate Professor at Universit\u00e9 Paris-Saclay (I2BC, CNRS), interested in protein structure, interactions, and evolution.", "public_name": "Diego Javier Zea", "guid": "14933d42-ea9c-583a-ae9d-fddb5101b456", "url": "https://pretalx.com/juliacon-2026/speaker/BVAJXH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SA7F9J/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SA7F9J/", "attachments": []}, {"guid": "42111d50-af85-5de5-8716-27969cab7da0", "code": "XYQJFH", "id": 92758, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XYQJFH/image_EcKPmkM.webp", "date": "2026-08-12T12:30:00+02:00", "start": "12:30", "end": "2026-08-12T12:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92758-judock-an-open-source-ml-driven-platform-for-virtual-screening-of-phytocompounds-in-drug-discovery", "url": "https://pretalx.com/juliacon-2026/talk/XYQJFH/", "title": "juDock: An Open-Source, ML-Driven Platform for Virtual Screening of Phytocompounds in Drug Discovery", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Short talk", "language": "en", "abstract": "Virtual Screening of phytocompounds in drug discovery has surged over the years. We present juDock, a ML-Driven dockerized Linux application built in Julia. juDock automates the pipeline from the preparation of ligands to the prediction of potential compounds for a specific protein integrating AutoDock Vina, RDKit and Scikit-Learn via PythonCall.jl and Genie.jl. Furthermore, juDock is an open source project attracting researchers to contribute using the established ML pipeline for various proteins.", "description": "The Structure of the Talk is as follows:\n\n**Background and Problem:** (2 mins; Will brief clinical challenges, limitations of the existing tools)\nThere has been a growing interest in screening for phytocompounds. However, High Throughput Virtual Screening (HTVS) requires computational expertise from downloading the ligands to molecular docking including format conversions. Sometimes, it also demands scripting, producing non-reproducible results. Furthermore, various tools should be used sequentially such as RDKit, OpenBabel, AutoDock Vina, Discovery Studio Visualizer etc.\n\n**The Julia Solution:** (3 mins; Will talk about the methodologies implemented)\nWe present juDock, a containerized ML-Driven browser based Linux application that solves this fragmentation. We have used Julia to build a streamlined pipeline, as an ultimate orchestrator optimizing specifically for the virtual screening of phytocompounds. While traditional docking provides physics-based binding affinities, our integrated model provides a complementary ML-based probability score, termed as dockscore. This allows researchers to perform high-throughput screening where the software simultaneously validates spatial docking feasibility and chemical inhibitory potential, processing thousands of phytochemicals in seconds.\n\n**Key Highlights:** (5 min; Will talk about the application architecture)\n\n- **Phytocompound based Training:** We trained the Multi-Output Random Forest Regressor model on a curated phytocompounds dataset docked against 17 beta HSD1 target. The model learned to predict the binding affinity and an overall dockscore based on a combination of binding affinities, molecular interaction profiles (H bonds, Non bond interactions such as hydrophobic interactions etc) and also, physicochemical descriptors of the compounds.\n\n- **Seamless Interoperability:** We have used industry-standard libraries such as RDkit, Scikit-learn entirely from within Julia using Conda and PythonCall.jl\n\n- **Robust Multi-Processing:** We have applied a RAM-aware parallel processing system using Julia\u2019s Distributed library allowing effective parallel screening of large numbers of natural product libraries without segmentation faults.\n\n- **Full-Stack Browser Based Interface:** We have built a responsive GUI dashboard using Genie.jl. The application, while installing, creates directories such as juDock_input and juDock_output in the user\u2019s Home directory. The researchers have to just place the .sdf files in the input directory and the results are available both in the output folder and also, in the browser interface. The interface also provides real-time progress tracking with status bars.\n\n- **Open Source project:** The uniqueness of juDock relies on its specificity towards the target protein. The interface allows the user to select the protein target and consequently, the respective model is loaded. Hence, we have created this project as an open-source project allowing researchers to contribute the model trained using our established pipeline. Currently, juDock holds two protein targets, 17 beta HSD1 (trained by us) and Aromatase (Contributed). The complete code is available at https://github.com/drbenedictpaul/judock .\n\n**Validation and Conclusion:** (2 mins: Will talk about the case studies for validation)\nThe application was validated against traditional computer aided drug design methods with various types of proteins (Human, bacterial, viral and fungal) and compounds. juDock empowers scientists who are interested in screening various phytocompounds for its therapeutic potential. Combining the power of Machine Learning and the speed of Julia, juDock provides a powerful interface for the High Throughput Virtual Screening of phytocompounds in modern natural product drug discovery.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BMTJRK", "name": "Surya Sekaran", "avatar": "https://pretalx.com/media/avatars/UVBRWR_794uwnU.webp", "biography": "Ms. Sekaran is a PhD Scholar in the Department of Biotechnology. Her research is focused on the discovery and validation of novel therapeutic agents for breast cancer. She is currently working on the identification and evaluation of potential inhibitors for the 17-beta-hydroxysteroid dehydrogenase 1 (17\u03b2-HSD1) enzyme, a critical target in cancer treatment. Her work integrates both computational (in silico) and experimental (in vitro) methods, combining computational screening and inhibitor design with laboratory-based assays for validation. She is particularly interested in applying modern programming languages and high-performance computing to accelerate the drug discovery pipeline.", "public_name": "Surya Sekaran", "guid": "128b4718-53d1-5dad-ab30-792f371a35d3", "url": "https://pretalx.com/juliacon-2026/speaker/BMTJRK/"}], "links": [{"title": "juDock_Demo", "url": "https://drive.google.com/file/d/1SiZQvOxwMOz8VYi9KUVAIijtdrCxN-8P/view?usp=drive_link", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XYQJFH/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XYQJFH/", "attachments": [{"title": "flyer-XYQJFH", "url": "/media/juliacon-2026/submissions/XYQJFH/resources/XYQJFH_CKI1mwh.png", "type": "related"}, {"title": "juDock_main", "url": "/media/juliacon-2026/submissions/XYQJFH/resources/juDock_main_w3KgJwB.png", "type": "related"}, {"title": "juDock_Results", "url": "/media/juliacon-2026/submissions/XYQJFH/resources/juDock_Resul_LGKiPdr.png", "type": "related"}]}, {"guid": "1a3276da-2fde-5865-9b41-1c7b1503db69", "code": "NWQCHH", "id": 92668, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NWQCHH/image_lRFgxpE.webp", "date": "2026-08-12T12:45:00+02:00", "start": "12:45", "end": "2026-08-12T13:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92668-effects-of-stochasticity-on-molecular-minimization", "url": "https://pretalx.com/juliacon-2026/talk/NWQCHH/", "title": "Effects of stochasticity on molecular minimization", "subtitle": "", "track": "Pharmaceutical Research in Julia", "type": "Short talk", "language": "en", "abstract": "Protein structure minimization is a crucial step before running molecular pipelines with the aim to arrive at the lowest potential energy conformation. We introduce a mini-batching strategy for ML-based optimization algorithms. Leveraging the unified framework of Optimization.jl, we present a study to systematically assess the performance of different optimization algorithms with our molecular modeling framework [BiochemicalAlgorithms.jl](https://github.com/hildebrandtlab/BiochemicalAlgorithms.jl). This work provides the framework for identifying optimal algorithms for refinement of protein structures.", "description": "Protein structures are determined experimentally by X\u2011ray crystallography, electron microscopy, or NMR, or they are predicted with AlphaFold or RoseTTAFold [1,2]. Either way, the resulting protein models often contain missing atoms, distorted bond lengths, or unrealistic side chain orientations generating severe steric clashes and steep energy gradients. Energy minimization is thus essential to achieve physically realistic conformations. While classical minimizers (e.g., conjugate gradient, quasi-Newton) are standard, we demonstrate how machine learning (ML) optimizers can be effectively adapted to this domain.   \nWe introduce a novel mini-batching strategy for ML-based minimization (e.g., SGD, ADAM), implemented in BiochemicalAlgorithms.jl, our library for molecular analysis and simulation [3]. The library provides molecular mechanic functionalities to evaluate energy gradients and corresponding forces, which are essential for energy minimization. Our mini-batching strategy partitions the force\u2011field contributions by atom\u2011pair groups, allowing the energy and gradient calculations to be performed on small batches corresponding to random selected portions of the molecule.\nFurthermore, a major contribution of this work is a systematic assessment of different optimization algorithms by leveraging the unified Optimization.jl ecosystem\u2014an interface that connects to more than 25 optimization libraries [4]. \nOur approach supports the possibility of interchanging solvers including SGD and ADAM or alternative ML solvers for energy minimization of proteins without custom code per algorithm.\nUsing a representative set of proteins from each of the five top\u2011level SCOP classes, we compared runtime and convergence precision across the solvers. The results highlight which algorithms deliver the best trade\u2011off between speed and final energy for different structural families.  \nOverall, the talk will demonstrate how the proposed mini\u2011batching scheme opens a path towards ML\u2011augmented protein\u2011structure refinement and how a single, extensible optimization interface can streamline the evaluation of diverse minimization strategies for protein structure relaxation.\n\n[1] J.\u202fJumper et\u202fal., \u201cHighly accurate protein structure prediction with AlphaFold,\u201d Nature, vol.\u202f596, pp.\u202f583\u2011589, 2021, doi:\u202f10.1038/s41586\u2011021\u201103819\u20112.  \n[2] M.\u202fBaek et\u202fal., \u201cAccurate prediction of protein structures and interactions using a three\u2011track neural network,\u201d Science, vol.\u202f373, pp.\u202f871\u2011876, 2021, doi:\u202f10.1126/science.abj8754.  \n[3] J.\u202fLeclaire et\u202fal., \u201cStructure\u2011based bioinformatics with BiochemicalAlgorithms.jl,\u201d in Proceedings of the JuliaCon Conferences, vol.\u202f7, no.\u202f78, p.\u202f188, 2025, doi:\u202f10.21105/jcon.00188.  \n[4] V.\u202fK.\u202fDixit and C.\u202fRackauckas, \u201cOptimization.jl: A unified optimization package (version\u202fv3.12.1),\u201d Zenodo, Mar.\u202f2024. [Online]. Available: https://doi.org/10.5281/zenodo.7738525.  \n[5] N.\u202fK.\u202fFox, S.\u202fE.\u202fBrenner, and J.\u202fM.\u202fChandonia, \u201cSCOPe: Structural classification of proteins\u2014extended, integrating SCOP and ASTRAL data and classification of new structures,\u201d Nucleic Acids Res., vol.\u202f42, no.\u202fD1, pp.\u202fD304\u2011D309, 2014, doi:\u202f10.1093/nar/gkt1240.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TFSKAR", "name": "Jenny Leclaire", "avatar": "https://pretalx.com/media/avatars/TFSKAR_6U6GIDB.webp", "biography": "09/2012 - B.Sc. Molecular Biology, Johannes Gutenberg University Mainz\n03/2015 - M.Sc. Applied Bioinformatics, Johannes Gutenberg University Mainz\n\n03/2016 - present Research associate in computer science Johannes Gutenberg University Mainz\n\n04/2015 - present PhD candidate in computer science Johannes Gutenberg University Mainz\n\nI am interested in structural bioinformatics and development of software for applications in this and related fields.", "public_name": "Jenny Leclaire", "guid": "b2a59686-72dc-55a0-b995-6c2749e86b8e", "url": "https://pretalx.com/juliacon-2026/speaker/TFSKAR/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NWQCHH/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NWQCHH/", "attachments": []}, {"guid": "2ae56134-ec96-5578-8249-c5e0d9174bd7", "code": "VZUPJA", "id": 92746, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VZUPJA/image_WvJSco1.webp", "date": "2026-08-12T14:30:00+02:00", "start": "14:30", "end": "2026-08-12T15:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92746-the-heartratelab-jl-a-toolkit-for-heart-beats-time-series", "url": "https://pretalx.com/juliacon-2026/talk/VZUPJA/", "title": "The HeartRateLab.jl: a toolkit for heart beats time series", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Heart rate time signals are one of the most readily available and cost effective biosignals for the study of human behavior. Their availability and ease of use make them ideal for open science. Many programming languages provide libraries that enable the manipulation of the time series generated by measuring the time in milliseconds between each heart beat: the Inter-Beat-Interval (IBI). However advanced functionality is only provided by private software, and many of opensource libraries seem to be designed as simple feature extraction libraries, and many have fallen into disuse. In this work we present the HeartRateLab: a new, powerful, and complete computational framework, written in julia, for the processing, analysis, modeling, and evaluation of human heart IBIs. The Julia language enables the ideal environment for explicit scientific management of operational, data-driven definitions of features used to define heart rate variability, and their physiological statistical relevance in several different domains.\nWith the use of Julia's rich and interconnected scientific modeling environment, and the open scientific community, the HeartRateLab enables the most complete set of processing functions for IBI timeseries. Complete sets of features can be extracted from entire recordings, or using resampling or rolling window approaches with the overpowered capabilities of the language. Using Julia's DifferentialEquations.jl, Turing.jl, and other scientific libraries, the HeartRateLab provides the tools for modeling complex systems, including data-driven models for parameter recovery.\nThe HeartRateLab showcases the stable complexity management environment that the Julia language and its community provide for scientific programming, while demonstrating intricate, applicable, but also very simple and embodied principles about the rhythms to which our hearts beat.", "description": "As a tool for his Ph.D. in Cognitive Science at the Institute for Human-Computer Interaction, Technical University of Graz, Alberto Barradas has collected many methods and standards that are now integrated and presented in this Julia computing laboratory.\nAccessible scientific tooling makes live experimentation accessible, and is presented with a general overview on the study of Heart Rate (HR) and Heart Rate Variability (HRV) and its use in sport and cognitive science. Live visualizations and demonstrations are prepared for this presentation, including suggestions for integrating biofeedback into contemplative practices.\nWe hope the community finds this contribution valuable, as we have found it for our work in cognitive and sports science, teaching and learning, and personal practice.", "recording_license": "", "do_not_record": false, "persons": [{"code": "T8E9P3", "name": "Alberto Barradas", "avatar": "https://pretalx.com/media/avatars/ABCJPU_F0vN6Jh.webp", "biography": "[Alberto Barradas]([url](s.barcha.xyz/pretalx)) is a Cognitive Scientist and Behavioral Data Analyst. His work on open and reproducible science is focused on the study of cognitive performance with the use of digital technology. Alberto is a PhD candidate at the Institute of Human-Computer Interaction TUGraz, and a statistician at the Statistical Ambulance of the Medical University of Graz.\nFind out more about public work at github.com/abcsds", "public_name": "Alberto Barradas", "guid": "23dc2494-1a58-5304-b5fe-f5b4781a901e", "url": "https://pretalx.com/juliacon-2026/speaker/T8E9P3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VZUPJA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VZUPJA/", "attachments": []}, {"guid": "c77a0891-33c7-504f-9096-1906106efcfd", "code": "WGZ9ZA", "id": 92553, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WGZ9ZA/image_wchvKcf.webp", "date": "2026-08-12T15:00:00+02:00", "start": "15:00", "end": "2026-08-12T15:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92553-radiomics-jl-a-library-for-high-performance-radiomic-features-extraction-from-medical-images", "url": "https://pretalx.com/juliacon-2026/talk/WGZ9ZA/", "title": "Radiomics.jl: a Library for High-Performance Radiomic Features Extraction from Medical Images", "subtitle": "", "track": "Health Mini Symposium", "type": "Short talk", "language": "en", "abstract": "Radiomic features extracted from medical images are fundamental for computer-aided diagnosis and treatment planning.\nRadiomics.jl is a new, pure-Julia open-source library for high-performance extraction of quantitative imaging biomarkers.\nDeveloped across multiple international institutions, it provides an efficient and scalable workflow by leveraging Julia\u2019s speed.\nThe library ensures seamless integration with machine learning pipelines for advanced clinical research and precision medicine.", "description": "**_Introduction_**\nRadiomics has emerged as a fundamental approach in precision medicine, enabling the extraction of high-throughput quantitative features from medical images for computer-aided diagnosis and personalized treatment planning [1].\nIn this work, Radiomics.jl [2], a new and open-source Julia library designed to extract radiomic features, is presented.\n\n**_Materials and Methods_**\nRadiomics.jl implements a complete set of radiomic features (106 in total) extraction capabilities, encompassing first-order and 2D/3D shape-based features, as well as sophisticated texture descriptors. These advanced texture features include the Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Dependence Matrix (GLDM), Gray-Level Run-Length Matrix (GLRLM), Gray-Level Size-Zone Matrix (GLSZM), and Neighborhood Gray-Tone Difference Matrix (NGTDM).\nThe tool is designed for convenience and user-friendliness, and it takes advantage of a multi-threading workflow, which allows efficient feature extraction from multiple tissues or organs concurrently.\n\n**_Results_**\nA Computed Tomography (CT) scan of a patient with a lung tumor [3,4] was selected for benchmarking. The CT volume dimensions were 500 x 500 x 319 voxels, with the tumor segmentation comprising 38226 voxels (38.226 cm3). Excluding the initial JIT compilation overhead, which accounted for 12.47 s (single-thread) and 5.60 s (multi-thread), the mean execution time over 14 repetitions was 4.07 s (SD=0.10 s) for single-thread and 3.00 s (SD=0.29 s) for multi-thread. Multi-threading yielded a 25% reduction in computational time (Wilcoxon rank-sum test revealed statistically significant difference between the two distributions).\n\n**_Discussion and Conclusions_**\nRadiomics.jl offers a high-performance, efficient, and user-friendly solution for quantitative medical image analysis entirely within the Julia ecosystem.\nDesigned for fast and user-friendly feature extraction from medical images, its goal is to support and advance the field of personalized medicine.\n\n**_References_**\n[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC4734157/\n[2] https://github.com/pzaffino/Radiomics.jl \n[3] Aerts HJWL, et al. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006\n[4] Aerts HJWL, et al. NSCLC-Radiomics [Data set]. TCIA, 2014. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI", "recording_license": "", "do_not_record": false, "persons": [{"code": "AULQ8R", "name": "ALDO GIULIANI", "avatar": "https://pretalx.com/media/avatars/UDJMGC_F95nQMR.webp", "biography": "My name is Aldo Giuliani. I have a master\u2019s degree in Biomedical Engineering and I am a PhD student in Artificial Intelligence and Biomedical Engineering at the \u201cMagna Graecia\u201d University of Catanzaro, Italy.", "public_name": "ALDO GIULIANI", "guid": "5a7e1972-0f20-53cb-96aa-e8e2e69e1630", "url": "https://pretalx.com/juliacon-2026/speaker/AULQ8R/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WGZ9ZA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WGZ9ZA/", "attachments": [{"title": "flyer-WGZ9ZA", "url": "/media/juliacon-2026/submissions/WGZ9ZA/resources/WGZ9ZA_qhy68HO.png", "type": "related"}]}, {"guid": "ff435baa-5c47-5487-989f-c75a91344ca0", "code": "F8GRBR", "id": 92592, "logo": "https://pretalx.com/media/juliacon-2026/submissions/F8GRBR/image_JXiPbCF.webp", "date": "2026-08-12T15:15:00+02:00", "start": "15:15", "end": "2026-08-12T15:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92592-generic-gpu-acceleration-for-medical-image-reconstruction", "url": "https://pretalx.com/juliacon-2026/talk/F8GRBR/", "title": "Generic GPU-Acceleration for Medical Image Reconstruction", "subtitle": "", "track": "Health Mini Symposium", "type": "Short talk", "language": "en", "abstract": "Medical image reconstruction for modalities such as magnetic resonance imaging (MRI) and magnetic particle imaging (MPI) involves solving computationally intensive inverse problems.  The MRIReco.jl and MPIReco.jl reconstruction packages feature a shared, modular optimisation backend that provides efficient and reusable solvers for various imaging modalities. In this talk, I will present how we extended this backend with vendor-agnostic GPU acceleration, which enables efficient reconstruction across both different imaging modalities and different GPU backends.", "description": "Medical image reconstruction for tomographic modalities such as magnetic resonance imaging (MRI) and magnetic particle imaging (MPI) involves solving ill-posed inverse problems that are typically addressed through regularized least-squares optimization. As imaging techniques advance, computational demands increase significantly, often requiring GPU acceleration for practical use. Additionally, the operators involved often become too large to store in memory, requiring or benefitting from (composable) matrix-free operator implementations.\n\nThe MRIReco.jl and MPIReco.jl reconstruction packages feature a shared optimization backend that prioritizes code reuse. LinearOperatorCollection.jl provides matrix-free implementations of common image processing operations (FFT, NFFT, DCT, Wavelet) and enables their composition through custom building blocks and the underlying LinearOperators.jl package. The collection also provides structure-aware optimizations that exploit properties of composed operators for computational efficiency. Modality-specific packages like MRIOperators.jl and operators in MPIReco.jl implement encoding operators for their respective imaging physics. RegularizedLeastSquares.jl serves as the shared optimization backend, providing reusable iterative solvers (CGNR, FISTA, ADMM) that work with any operator implementing matrix-vector products and adjoints. This architecture allows the same solver implementations to work across different imaging modalities.\n\nIn this talk, I will present our recent technical developments in extending vendor-agnostic GPU acceleration throughout this entire stack. This is achieved through a combination of Julia's features, such as multiple dispatch, parametric types, and package extensions, as well as the Julia GPU ecosystem, particularly GPUArrays.jl, KernelAbstractions.jl, and Adapt.jl. Using parametric types and Adapt.jl means that our operators and solvers remain generic over array types and work with both CPU and GPU arrays, while GPUArrays.jl and KernelAbstractions.jl allow us to write GPU kernels that are compatible with different GPU backends. Lastly, package extensions allow GPU-specific code to be loaded conditionally, i.e. only when users load their preferred GPU backend. This means that the core packages remain lightweight with no GPU dependencies. Users can enable GPU acceleration with minimal code changes by loading a GPU package and providing an array type.\n\nRelated organizations:\nhttps://github.com/JuliaImageRecon\nhttps://github.com/MagneticParticleImaging\nhttps://github.com/MagneticResonanceImaging\n\nRelated repositories:\nhttps://github.com/JuliaImageRecon/LinearOperatorCollection.jl\nhttps://github.com/JuliaImageRecon/RegularizedLeastSquares.jl\nhttps://github.com/MagneticResonanceImaging/MRIReco.jl\nhttps://github.com/MagneticParticleImaging/MPIReco.jl", "recording_license": "", "do_not_record": false, "persons": [{"code": "9H3WKD", "name": "Niklas Hackelberg", "avatar": "https://pretalx.com/media/avatars/NXJKDH_133vORq.webp", "biography": "I'm a PhD student at the Institute for Biomedical Imaging at Hamburg University of Technology (TUHH), Germany. My research focuses on parallel computing for medical imaging, particularly magnetic particle imaging. Since 2025, I have also worked as a software engineer at the Fraunhofer Research Institution for Individualised Medical Technology and Engineering (IMTE), Germany.", "public_name": "Niklas Hackelberg", "guid": "d4fd6349-7feb-5652-8aae-feece9181d91", "url": "https://pretalx.com/juliacon-2026/speaker/9H3WKD/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/F8GRBR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/F8GRBR/", "attachments": []}, {"guid": "f9ea17f1-e1fd-5d2e-b685-1a323092a89b", "code": "THM7WY", "id": 92587, "logo": "https://pretalx.com/media/juliacon-2026/submissions/THM7WY/image_ZP51INP.webp", "date": "2026-08-12T15:45:00+02:00", "start": "15:45", "end": "2026-08-12T16:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92587-application-of-sphericalharmonicexpansions-jl-representation-and-handling-of-magnetic-fields", "url": "https://pretalx.com/juliacon-2026/talk/THM7WY/", "title": "Application of SphericalHarmonicExpansions.jl: Representation and Handling of Magnetic Fields", "subtitle": "", "track": "Health Mini Symposium", "type": "Short talk", "language": "en", "abstract": "Magnetic fields are vital in numerous applications, including the medical imaging modality magnetic particle imaging, where field-related artifacts affect image quality. A good approximation of these fields is essential and can be done via spherical harmonic expansions derived from calibration measurements. The Julia package SphericalHarmonicExpansions.jl facilitates representation, transformations, and fast numerical evaluation, providing efficient tools for accurate magnetic field analysis.", "description": "The Julia package [SphericalHarmonicExpansions.jl](https://github.com/IBIResearch/SphericalHarmonicExpansions.jl) provides a flexible and efficient framework for working with spherical harmonics and their expansions. By expanding general solutions to Laplace\u2019s equation inside a sphere into spherical harmonics, the package enables a broad range of applications. It offers easy manipulation of the coefficients, including coordinate transformations such as translation, rotation, and point reflection, as well as fast numerical evaluations of the expansion.\n\nThe focus of the talk is the application of the package to magnetic fields, which play a crucial role in medical imaging techniques such as magnetic particle imaging (MPI). In MPI, static and dynamic fields excite iron-oxide nanoparticles to achieve spatial encoding. Field-related imperfections can cause artifacts that degrade image quality, making precise knowledge and characterization of these fields essential. Spherical harmonic expansions offer a compact and robust representation of the magnetic fields within a spherical region, which can be obtained by calibration measurements on a spherical surface. \n\nWhile the package itself is application-independent, it serves as the core component to handle magnetic fields using spherical harmonics in Julia. Example code demonstrating its application to magnetic field representation and evaluation is available in the [SphericalHarmonicExpansionOfMagneticFields repository](https://github.com/IBIResearch/SphericalHarmonicExpansionOfMagneticFields). Complementary, in-development packages like [MPISphericalHarmonics.jl](https://github.com/MagneticParticleImaging/MPISphericalHarmonics.jl), which provides interfaces tailored to magnetic field handling in MPI, and [MPIUI.jl](https://github.com/MagneticParticleImaging/MPIUI.jl), with an interactive GUI for visualization, extend this foundation with practical tools for research and application. \n\nThe focus of this talk will be\n* Structure and functionality of SphericalHarmonicExpansions.jl\n* Application to magnetic fields\n* Usage example in MPI", "recording_license": "", "do_not_record": true, "persons": [{"code": "CWXCVN", "name": "Marija Boberg", "avatar": null, "biography": "Marija Boberg is a doctoral candidate associated with the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology (Germany). Her academic background is in mathematics, and her research is focused on magnetic fields and image reconstruction in magnetic particle imaging.", "public_name": "Marija Boberg", "guid": "7a76f1be-a0e0-5e68-ba42-073e7f70e55e", "url": "https://pretalx.com/juliacon-2026/speaker/CWXCVN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/THM7WY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/THM7WY/", "attachments": [{"title": "flyer-THM7WY", "url": "/media/juliacon-2026/submissions/THM7WY/resources/THM7WY_io5RtiQ.png", "type": "related"}]}, {"guid": "5f09db13-30d0-524f-9e61-ea11fca2278d", "code": "PZSLRD", "id": 92539, "logo": "https://pretalx.com/media/juliacon-2026/submissions/PZSLRD/image_7DlDwcT.webp", "date": "2026-08-12T16:00:00+02:00", "start": "16:00", "end": "2026-08-12T16:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92539-how-i-drew-the-julia-logo-using-spins-in-an-mri-machine", "url": "https://pretalx.com/juliacon-2026/talk/PZSLRD/", "title": "How I Drew the Julia Logo Using Spins in an MRI Machine", "subtitle": "", "track": "Health Mini Symposium", "type": "Long talk", "language": "en", "abstract": "In MRI, radiofrequency (RF) pulses steer spins and shape the signal they produce. In this talk, I will show how we used RF pulses to \u201cdraw\u201d the Julia logo inside a water bottle using a real MRI scanner. Behind this demo is a faster approach to RF-pulse design. By combining reverse-mode automatic differentiation (AD) with GPU-accelerated MRI simulations, we reduced a 2D RF-pulse optimization from ~10 minutes to ~1 second, making subject-specific RF design clinically feasible for applications such as imaging near metal and fat suppression.", "description": "Radio frequency (RF) and magnetic field gradient waveforms are the controls an MRI scanner uses to excite spins. By shaping these waveforms, we can go beyond simple slice selection and excite a chosen 2D pattern within a slice.\n\nIn this talk, I will first build intuition with minimal prerequisites: resonance, the rotating frame, how gradients turn frequency into a spatial label, and how time-varying gradients combined with a shaped RF waveform produce a 2D excitation pattern.\n\nI then frame pulse design as an inverse problem. We seek an RF waveform x that minimizes the mismatch between a desired transverse magnetization pattern b and the simulated pattern A(x), where A(x) is computed by integrating the spin dynamics under the applied RF and gradient waveforms. The simulation is implemented in Julia using KomaMRI.jl, accelerated with GPU kernels via KernelAbstractions.jl, and differentiated using reverse-mode AD with Enzyme.jl/Reactant.jl.\n\nThe key contribution is speed. With CPU finite-difference gradients (FiniteDiff.jl), optimizing the Julia-logo pulse took about 5 hours. Using GPU execution and Enzyme-based reverse-mode AD, we reduced the same optimization to under 5 seconds.\n\nAs a concrete demonstration, we designed a 2D pulse that imprints the Julia logo in a water-bottle phantom and validated it by executing the pulse on a real scanner via Pulseq. The measured excitation pattern closely matches the simulation.\n\nI will close by explaining why this matters clinically. If pulse design becomes fast enough, subject-specific RF tailoring becomes practical. Instead of one-size-fits-all pulses, we can restrict excitation to specific anatomical regions, improve imaging near metal implants, and optimize fat suppression, while staying within hardware limits such as RF power and gradient strength.", "recording_license": "", "do_not_record": false, "persons": [{"code": "VK8HJK", "name": "Carlos Castillo Passi", "avatar": "https://pretalx.com/media/avatars/VK8HJK_ATMqIDe.webp", "biography": "Carlos Castillo-Passi began his academic journey at Pontificia Universidad Catolica de Chile (PUC), where he earned both a degree and an MSc in Electrical Engineering in 2018. He then pursued a PhD in Biological and Medical Engineering through a joint program between PUC and King\u2019s College London (KCL), completing it with maximum distinction in 2024. His research focused on the design of low-field cardiac MRI sequences using open-source MRI simulations. In 2023, his work on open-source MRI simulations was highlighted by the editor of Magnetic Resonance in Medicine (MRM). Furthermore, his application of this work to low-field cardiac MRI earned him the Early Career Award in Basic Science from the Society for Cardiovascular Magnetic Resonance (SCMR) in 2024. In addition to his research, Carlos is an active member of JuliaHealth, contributing to the development of high-performance, reproducible tools for health and medicine. In 2025, he joined Stanford University as a postdoctoral researcher, where he continues his work in cardiac MRI and open-source technologies.", "public_name": "Carlos Castillo Passi", "guid": "4d1ae917-5346-5770-af3e-fa8ec4395929", "url": "https://pretalx.com/juliacon-2026/speaker/VK8HJK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/PZSLRD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/PZSLRD/", "attachments": [{"title": "flyer-PZSLRD", "url": "/media/juliacon-2026/submissions/PZSLRD/resources/PZSLRD_rTpsRfq.png", "type": "related"}]}, {"guid": "3d3cbc2e-a566-5b74-b617-8008cad5743a", "code": "TDWGHB", "id": 92631, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TDWGHB/image_KK4y4RB.webp", "date": "2026-08-12T16:30:00+02:00", "start": "16:30", "end": "2026-08-12T17:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92631-improving-juliahealth-documentation-accessibility-for-community-onboarding", "url": "https://pretalx.com/juliacon-2026/talk/TDWGHB/", "title": "Improving JuliaHealth Documentation Accessibility for Community Onboarding", "subtitle": "", "track": "Health Mini Symposium", "type": "Long talk", "language": "en", "abstract": "As JuliaHealth continues to expand, maintaining clarity and reliability across packages becomes essential. Supported by a NumFOCUS Small Development Grant, this project advances three goals: attracting new contributors, highlighting JuliaHealth workflows, and strengthening ecosystem robustness. A reproducible audit of documentation and CI pipelines led to repository improvements, website updates, structured workflow examples and modernized documentation deployment.", "description": "This project was developed under a NumFOCUS Small Development Grant to strengthen the JuliaHealth ecosystem through three main goals.\n\n- **(Goal 1) Attracting New Community Members and Contributors**: By centralizing and making our documentation more broadly accessible, we provided clearer on-ramps for new JuliaHealth community members to get started with JuliaHealth tools and understand how they can contribute.\n- **(Goal 2) Highlighting JuliaHealth Workflows**: Through the development of explanatory guides and structured examples, including the JuliaHealthZoo initiative, we demonstrated how JuliaHealth and broader Julia tools can be used to meet practical needs within real health informatics workflows.\n- **(Goal 3) Strengthening Community Robustness:** A reproducible JuliaHealthAudit was conducted across core packages to evaluate documentation, CI pipelines and maintenance signals. Based on its findings, targeted improvements were opened across repositories and package specific documentation and CI standards were established to improve consistency and reliability.\n\nBeyond technical changes, this work has made JuliaHealth easier to approach and more sustainable in the long term. By combining ecosystem analysis with improvements and clearer documentation practices, the project reduces barriers for new contributors while strengthening the stability of the existing packages. The lessons learned may serve as a reference for improving documentation and maintenance practices in other Julia organizations as well.\n\nSome highlights of this work are:\n\n- JuliaHealth Audit: https://github.com/JuliaHealth/JuliaHealthAudit\n- Documentation improvements: https://github.com/JuliaHealth/OMOPCDMFeasibility.jl , https://juliahealth.org/KomaMRI.jl/previews/PR725/\n- JuliaHealthZoo: https://github.com/JuliaHealth/JuliaHealthZoo\n\nAcknowlegments: This work was supported by a NumFOCUS Small Development Grant. I am grateful to the JuliaHealth community, and especially to Carlos Castillo Passi and Jacob S Zelko, for their guidance and support throughout the project. For additional details, see the grant proposal issue: https://github.com/numfocus/small-development-grant-proposals/issues/59", "recording_license": "", "do_not_record": false, "persons": [{"code": "VAFSEH", "name": "Kosuri Lakshmi Indu", "avatar": "https://pretalx.com/media/avatars/ZR8V3Y_Q2fJ2oN.webp", "biography": "Kosuri Lakshmi Indu is a recent Computer Science graduate and an open-source contributor in the JuliaHealth ecosystem. As a Google Summer of Code 2025 contributor with The Julia Language (JuliaHealth), she worked on supporting patient-level data pipelines by contributing to HealthBase.jl and developing OMOPCDMFeasibility.jl. She has also led ecosystem-level improvements in JuliaHealth through a NumFOCUS Small Development Grant, focusing on reproducible ecosystem audits, documentation standardization, and CI infrastructure to improve onboarding and long-term sustainability. She is particularly interested in open-source development, exploring emerging technologies and continuously learning across diverse domains.", "public_name": "Kosuri Lakshmi Indu", "guid": "476ed71e-a1d2-5cba-ba8a-9d7cce9f4ada", "url": "https://pretalx.com/juliacon-2026/speaker/VAFSEH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TDWGHB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TDWGHB/", "attachments": [{"title": "flyer-TDWGHB", "url": "/media/juliacon-2026/submissions/TDWGHB/resources/TDWGHB_32S8FVd.png", "type": "related"}]}, {"guid": "3d0f4103-9dce-50da-9b07-3f00cb8deca2", "code": "FYD7SD", "id": 92500, "logo": "https://pretalx.com/media/juliacon-2026/submissions/FYD7SD/image_3RkXsL1.webp", "date": "2026-08-12T17:00:00+02:00", "start": "17:00", "end": "2026-08-12T17:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92500-state-of-juliahealth", "url": "https://pretalx.com/juliacon-2026/talk/FYD7SD/", "title": "State of JuliaHealth", "subtitle": "", "track": "Health Mini Symposium", "type": "Long talk", "language": "en", "abstract": "Since 2020, the JuliaHealth community has experienced steady growth. This talk will highlight the continued development of the JuliaHealth ecosystem, reflecting on where the community has been, where it is today, and where it is headed.", "description": "We will provide an overview of the JuliaHealth ecosystem, including its structure, ongoing efforts, and recent milestones. In particular, the talk will cover:\n\n- History and evolution of JuliaHealth, including the growth of the community and its sub-ecosystems\n\n- Ecosystem governance, including current leadership, and community goals\n\n- Projects, collaborations, and research highlights across the JuliaHealth ecosystem\n\n- Current initiatives and support, including the recent NumFOCUS grant and the JuliaHealth bounty program\n\n- Future goals and direction for the community", "recording_license": "", "do_not_record": false, "persons": [{"code": "MHPQPV", "name": "Hetarth Shah", "avatar": "https://pretalx.com/media/avatars/RAZWKV_SowpTQ0.webp", "biography": "A software developer and an open source contributor.\n\nGithub: https://github.com/Hetarth02", "public_name": "Hetarth Shah", "guid": "f266b127-4e84-5154-884d-f2edd064f9e8", "url": "https://pretalx.com/juliacon-2026/speaker/MHPQPV/"}, {"code": "VK8HJK", "name": "Carlos Castillo Passi", "avatar": "https://pretalx.com/media/avatars/VK8HJK_ATMqIDe.webp", "biography": "Carlos Castillo-Passi began his academic journey at Pontificia Universidad Catolica de Chile (PUC), where he earned both a degree and an MSc in Electrical Engineering in 2018. He then pursued a PhD in Biological and Medical Engineering through a joint program between PUC and King\u2019s College London (KCL), completing it with maximum distinction in 2024. His research focused on the design of low-field cardiac MRI sequences using open-source MRI simulations. In 2023, his work on open-source MRI simulations was highlighted by the editor of Magnetic Resonance in Medicine (MRM). Furthermore, his application of this work to low-field cardiac MRI earned him the Early Career Award in Basic Science from the Society for Cardiovascular Magnetic Resonance (SCMR) in 2024. In addition to his research, Carlos is an active member of JuliaHealth, contributing to the development of high-performance, reproducible tools for health and medicine. In 2025, he joined Stanford University as a postdoctoral researcher, where he continues his work in cardiac MRI and open-source technologies.", "public_name": "Carlos Castillo Passi", "guid": "4d1ae917-5346-5770-af3e-fa8ec4395929", "url": "https://pretalx.com/juliacon-2026/speaker/VK8HJK/"}, {"code": "LWS9DJ", "name": "Jacob Zelko", "avatar": "https://pretalx.com/media/avatars/WNALCB_cXhlXu9.webp", "biography": "My name is Jacob Scott Zelko! I am currently pursuing my MS in Applied Mathematics at Northeastern University (NEU) and am a trainee of NEU's Roux Institute.\n\nMy research career has focused primarily and broadly on population health. In particular, chronic mental illness (i.e. depression, suicidality, and bipolar disorder), social determinants of health and health disparities within intersectional populations, chronic illness, and neurocognitive disabilities. As a convergence of my interests, I am very interested in how we can use mathematical structures (such as categories) to establish meaningful relationships between non-traditional health data sources to gain greater insights into population health. To bridge these worlds, I have been heavily involved with observational health research methods using \"Real World Data\" and am an active member of both the OHDSI and Category Theory communities.", "public_name": "Jacob Zelko", "guid": "5c0f0191-fb5b-57cc-aa20-9ce03b84e9cb", "url": "https://pretalx.com/juliacon-2026/speaker/LWS9DJ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/FYD7SD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/FYD7SD/", "attachments": [{"title": "flyer-FYD7SD", "url": "/media/juliacon-2026/submissions/FYD7SD/resources/FYD7SD_e9nnHht.png", "type": "related"}]}], "Muschel \u2014 N2": [{"guid": "7012aa93-1f32-5065-9532-38bdc144c7bd", "code": "FCV33L", "id": 89576, "logo": "https://pretalx.com/media/juliacon-2026/submissions/FCV33L/image_JeewOV5.webp", "date": "2026-08-12T10:00:00+02:00", "start": "10:00", "end": "2026-08-12T10:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-89576-the-agentic-ai-maintenance-bots-of-the-sciml-organization", "url": "https://pretalx.com/juliacon-2026/talk/FCV33L/", "title": "The Agentic AI Maintenance Bots of the SciML Organization", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "The Julia SciML ecosystem is a collection of hundreds of packages. Keeping the whole system up to date can be quite the task, with dependencies releasing breaking versions weekly and having to track down CI failures. Over the last year a multi-agent system was developed to help with a lot of the maintenance burden. The goal of this talk is to share the details of this system so that other Julia package ecosystems can iterate on the idea and adopt similar mechanisms.", "description": "Right now the system is kept private, but it will be opened sourced before the talk. It needs an audit to ensure no secret keys are leaked.\n\nThe core system has 48 concurrent agents which trigger individual bots and cycle through different behaviors to cover many standard maintenance problems. Right now the system has the following bot profiles that are orchestrated with specific purposes:\n\n* CI Health,        Check\tTests master branch CI, diagnoses and fixes failures\n* Random Issue Solver,\tInvestigates open issues (prioritizes bug label)\n* Dependency Update,\tHandles dependency update PRs\n* Min Version Bump,\tBumps minimum versions in compat\n* Docs Improvement,\tImproves documentation\n* Static Improvement,\tStatic analysis improvements\n* Performance Improvement,\tPerformance optimizations\n* Interface Check,\tChecks package interfaces\n* Precompilation, Improvement\tImproves precompilation\n* Version Bump,\tChecks for version releases\n* Explicit Imports,\tAdds explicit imports\n* Deprecation Fix,\tFixes deprecation warnings\n* Benchmark Check,\tChecks SciMLBenchmarks.jl", "recording_license": "", "do_not_record": false, "persons": [{"code": "WUWQQ3", "name": "Chris Rackauckas", "avatar": "https://pretalx.com/media/avatars/WUWQQ3_otHw1Wk.webp", "biography": "Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.", "public_name": "Chris Rackauckas", "guid": "5ecf5886-9c68-55ca-8dcf-c4f85742c1ca", "url": "https://pretalx.com/juliacon-2026/speaker/WUWQQ3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/FCV33L/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/FCV33L/", "attachments": []}, {"guid": "98cfb817-52b2-549d-92c8-8f8f24843456", "code": "8CDNV9", "id": 92836, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8CDNV9/image_g0ZtA0t.webp", "date": "2026-08-12T10:30:00+02:00", "start": "10:30", "end": "2026-08-12T11:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92836-agentif-jl-ai-agent-primitives-for-julia", "url": "https://pretalx.com/juliacon-2026/talk/8CDNV9/", "title": "Agentif.jl: AI agent primitives for Julia", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "AI Agent software has proliferated in the last year. What started out as simple chatbots has evolved into deeply capable personal AI assistants and agent \"swarms\".\n\nAgentif.jl provides a set of agent \"primitives\" to enable building and configuring agent harnesses in pure Julia.\n\n- LLMProviders.jl: model provider abstraction; unifies Anthropic, OpenAI, OpenRouter, and other LLM providers under common models/\"stream\" functionality\n- Agentif.jl: core Agent, Tool, Channel, Session, Compaction, and middleware definitions that form the core \"agent loop\" functionality\n- LLMTools.jl: Sets of predefined tools that can be provided to agents, including: subagents, pty sessions, web search, Julia worker processes, and basic bash tools (ls, grep, read, write, edit, etc.)\n- Vo.jl: an example \"personal assistant\" setup using above primitives bundled together", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "WENVTF", "name": "Jacob Quinn", "avatar": "https://pretalx.com/media/avatars/MQ3SJP_Z0yuEym.webp", "biography": "Worked with Julia for a long time. Involved in many \"fundamental\" packages across the ecosystem, web or data related.", "public_name": "Jacob Quinn", "guid": "7bc45afc-d149-5fc1-a5b3-a2c178529ca3", "url": "https://pretalx.com/juliacon-2026/speaker/WENVTF/"}], "links": [{"title": "Agentif.jl GitHub Repository", "url": "https://github.com/quinnj/Agentif.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8CDNV9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8CDNV9/", "attachments": []}, {"guid": "775e47c8-aea5-53c9-913b-36c118a451d5", "code": "RGVXMP", "id": 92920, "logo": "https://pretalx.com/media/juliacon-2026/submissions/RGVXMP/image_ZaPUxs1.webp", "date": "2026-08-12T11:15:00+02:00", "start": "11:15", "end": "2026-08-12T11:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92920-gradients-aren-t-always-great-a-case-study-with-mixedmodels-jl", "url": "https://pretalx.com/juliacon-2026/talk/RGVXMP/", "title": "Gradients aren't always great -- a case study with MixedModels.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "The advent of convenient automatic differentiation has made gradient-based optimization the default strategy for many challenging problems and has revolutionized statistical practice.\nAt the same time, MixedModels.jl uses a gradient-free approach to optimization and remains best in class for linear mixed models.\nUsing MixedModels.jl as a case study, we will explore the tradeoffs of using the gradient and why gradient-free approaches remain relevant even in a world of easy autodiff.", "description": "The developers of MixedModels.jl are often asked why they don't use gradient-based optimization and GPUs, which have fueled many recent advances in statistics and machine learning, to make the package even faster. \nIn this talk, we'll focus on the first aspect: the use of the gradient in the optimization and use MixedModels.jl as a case study to discuss why gradients, even with modern automatic differentiation, may not provide much benefit or even be slower than gradient-free approaches. We'll look at why the evaluation of the gradient itself can be expensive enough that gradient-based optimization suffers from dramatically slower step speed.\nWe'll also discuss how particular objective functions can be \"compatible\" with particular gradient-free optimizers in a way that results in very rapid convergence, such that gradient-free approaches may not require substantially more iteration steps than gradient-based approaches.\nWe'll explore these properties with examples from our attempts to take advantage of the gradient in MixedModels.jl.\nWe'll see why we haven't (yet) moved to gradient-based approaches and, more generally, that gradient-free approaches still have a role to play, even in a world of convenient, accessible autodiff.", "recording_license": "", "do_not_record": false, "persons": [{"code": "U9SFCL", "name": "Phillip Alday", "avatar": null, "biography": "Phillip is a neuroscientist and contributor to the MixedModels.jl ecosystem.", "public_name": "Phillip Alday", "guid": "b466e92f-1e17-5821-9e7c-d547cc9fffe7", "url": "https://pretalx.com/juliacon-2026/speaker/U9SFCL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/RGVXMP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/RGVXMP/", "attachments": [{"title": "Slides", "url": "/media/juliacon-2026/submissions/RGVXMP/resources/juliacon2026_efBlfD0.pdf", "type": "related"}]}, {"guid": "8b0baa50-36fe-5667-b719-682f9c7f168a", "code": "RXG7AD", "id": 92550, "logo": "https://pretalx.com/media/juliacon-2026/submissions/RXG7AD/image_g7bqTfx.webp", "date": "2026-08-12T11:30:00+02:00", "start": "11:30", "end": "2026-08-12T12:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92550-what-s-new-with-herb-jl-teaching-programs-how-to-program-with-program-synthesis", "url": "https://pretalx.com/juliacon-2026/talk/RXG7AD/", "title": "What\u2019s new with Herb.jl: Teaching Programs how to Program with Program Synthesis", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Wouldn\u2019t it be great if Julia could program itself? \nYou simply tell it what you want, Julia magic happens, and you get correct-by-construction code.\nIn this talk, we introduce `Herb.jl`, a unifying program synthesis library written in Julia, that gets us closer to this goal.\nWhile we are not fully there yet, we have significantly progressed since our last talk at JuliaCon 2024.", "description": "`Herb.jl` is a toolbox and framework for program synthesis: Automatically generating (not just) programs from specifications. \nSince our last talk at JuliaCon 2024, `Herb.jl` has grown substantially, combining both reasoning about programs and LLM-guided code generation.\n\nFirst, we introduce new constraint-solving techniques to `Herb.jl` and program synthesis: \nConstraints allow us to prune large regions of possible programs, e.g., by removing symmetries and useless programs, leading to significantly faster solving times.\n\nSecond, we introduce provable LLM guidance:\nIn `Herb.jl`, large language models (LLMs) can propose candidates or search heuristics, while `Herb.jl` retains formal guarantees and reasoning capabilities.\n\nIn this talk, we give a brief overview of our architecture, which unifies these ideas into a single extendable pipeline. \nWe demonstrate how Julia\u2019s metaprogramming and composability enable implementing new and old synthesizer ideas, and show how `Herb.jl` helps to synthesize not just programs.", "recording_license": "", "do_not_record": false, "persons": [{"code": "RAAER3", "name": "Tilman Hinnerichs", "avatar": "https://pretalx.com/media/avatars/RAAER3_C9S3vEx.webp", "biography": "I am a PhD student for Computer Science within the PONY lab with Sebastijan Dumancic and Neil Yorke-Smith, researching in the field of program synthesis, neuro-symbolic proving and reasoning, and their application to bioinformatics.\n\nCheck out [my website](tilman.hinnerichs.com) for more information.", "public_name": "Tilman Hinnerichs", "guid": "65c32dc1-9bb8-57e1-9bda-bb107cd6658d", "url": "https://pretalx.com/juliacon-2026/speaker/RAAER3/"}, {"code": "WGRKMC", "name": "Reuben Gardos Reid", "avatar": "https://pretalx.com/media/avatars/UNH8GG_QXil9zG.webp", "biography": "I am a PhD candidate at TU Delft, where I focus on program synthesis and applying it to scientific discovery. Currently, I am focusing on methods for synthesizing dynamical systems, with applications in biology. Alongside my own research, I work on Herb.jl, a program synthesis framework developed here at TU Delft (see: https://herb-ai.github.io/).", "public_name": "Reuben Gardos Reid", "guid": "5f3f84b1-9474-52eb-b11d-ef96ef5e8d61", "url": "https://pretalx.com/juliacon-2026/speaker/WGRKMC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/RXG7AD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/RXG7AD/", "attachments": [{"title": "flyer-RXG7AD", "url": "/media/juliacon-2026/submissions/RXG7AD/resources/RXG7AD_XFuhxxV.png", "type": "related"}]}, {"guid": "a2040b28-ff59-5784-8b7a-fe45dadf7584", "code": "A9PDA7", "id": 92636, "logo": "https://pretalx.com/media/juliacon-2026/submissions/A9PDA7/image_UzLLeCT.webp", "date": "2026-08-12T12:00:00+02:00", "start": "12:00", "end": "2026-08-12T12:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92636-optuna-jl-hyperparameter-optimization-with-optuna-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/A9PDA7/", "title": "Optuna.jl - Hyperparameter optimization with Optuna in Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Hyperparameter optimization is a core workflow in machine learning and scientific computing, yet the Julia ecosystem has lacked a mature, production-ready framework comparable to the robust, battle-tested tools available in other languages. In order to bridge this gap, we present `Optuna.jl` a package that brings the full functionality of `Optuna` (by Preferred Networks, Inc.), one of the most widely adopted hyperparameter optimization frameworks, into Julia.", "description": "Hyperparameter optimization is essential for improving model performance, robustness, and generalization across machine learning, simulation, and data-driven applications. While Python users have long benefited from mature frameworks like Optuna [1], Google Vizier [2], and Weights & Biases [3], Julia's native offerings remain limited: the most established package, `Hyperopt.jl`, is in maintenance mode and no longer accepts new features, and to our knowledge no existing Julia package supports distribution across multiple machines.\n\nThis talk presents `Optuna.jl`, which can be used to seamlessly integrate the `Optuna` framework into your Julia code via `CondaPkg.jl` and `PythonCall.jl`, and optimize hyperparameters directly in Julia.\nWe support all samplers, pruners and database backends, with native single-threaded, multi-threaded and multi-process execution and took special care to make sure high-performance Julia-Code doesn't get slowed down from Python-Calls.\nWe will also introduce `OptunaDashboard.jl`, a wrapper for the `optuna-dashboard` package for visualizing and managing optimization studies.\n\n\u201cOptuna, the Optuna logo and any related marks are trademarks of Preferred Networks, Inc.\u201d\n\n[1] Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.\n[2] Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., Sculley, D. (2017). Google Vizier: A Service for Black-Box Optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017 (pp. 1487\u20131495). ACM.\n[3] Biewald, L. (2020). Experiment Tracking with Weights and Biases. https://www.wandb.com/", "recording_license": "", "do_not_record": false, "persons": [{"code": "NF3ABA", "name": "Julian Trommer", "avatar": "https://pretalx.com/media/avatars/BW9CJL_Xt5G385.webp", "biography": "Research scientist @ University of Augsburg, chair of mechatronics\nGithub:\n- [JulianTrommer](https://github.com/JulianTrommer)\n- [Chair of Mechatronics](https://github.com/una-auxme)", "public_name": "Julian Trommer", "guid": "ace786c9-899a-58bf-a7ed-ba0621e9f379", "url": "https://pretalx.com/juliacon-2026/speaker/NF3ABA/"}, {"code": "8U88RU", "name": "Lars Mikelsons", "avatar": null, "biography": "Lars Mikelsons holds a diploma in Mathematics and a Ph.D. in Mechatronics. He began his professional career at Bosch Corporate Research before transitioning to academia. Currently, he is the Head of the Chair for Mechatronics at the University of Augsburg. His research focuses on Scientific Machine Learning and Mechatronic Systems Engineering, contributing to the advancement of intelligent, data-driven approaches in engineering applications.", "public_name": "Lars Mikelsons", "guid": "fb4a744b-7a9c-5616-ae25-9c7d65453f28", "url": "https://pretalx.com/juliacon-2026/speaker/8U88RU/"}], "links": [{"title": "Optuna.jl on GitHub", "url": "https://github.com/una-auxme/Optuna.jl", "type": "related"}, {"title": "OptunaDashboard.jl on GitHub", "url": "https://github.com/una-auxme/OptunaDashboard.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/A9PDA7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/A9PDA7/", "attachments": []}, {"guid": "2cce9179-4297-523a-805c-096f77a69b8c", "code": "CYWCXK", "id": 92728, "logo": "https://pretalx.com/media/juliacon-2026/submissions/CYWCXK/image_5hy8DNr.webp", "date": "2026-08-12T12:15:00+02:00", "start": "12:15", "end": "2026-08-12T12:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92728-basemodelica-jl-a-julia-interface-for-basemodelica", "url": "https://pretalx.com/juliacon-2026/talk/CYWCXK/", "title": "BaseModelica.jl: A Julia Interface for BaseModelica", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "BaseModelica is a subset of the Modelica standard that defines a simpler and more portable intermediate representation of full Modelica models. BaseModelica.jl brings this standard to Julia, enabling models from industry-standard Modelica tools to be imported into the SciML ecosystem and used with its rich set of solvers and analysis tools.", "description": "BaseModelica is a newly proposed standard that defines a subset of the Modelica standard to be used as a \"flattened\" representation of full Modelica models. Engineers and scientists often have large, carefully validated Modelica models built up over years in tools like Dymola, OpenModelica, or Modelon Impact. However, these models are oftentimes dependent on the exact toolchain that was used to create them. BaseModelica aims to side step portability issues by defining a common intermediate representation of models that can be imported and exported from any Modelica tool. BaseModelica.jl translates BaseModelica models in to ModelingToolkit models, bringing the full capabilities of the SciML and Julia ecosystem to preexisting Modelica models. \n\nThis talk will explain exactly what the BaseModelica standard is and what its goals are, explore the design and implementation of BaseModelica.jl, and discuss some of the difficulties that come with developing a package around an unfinished and evolving standard. Finally, we'll show off some of the capabilities of the package by parsing some BaseModelica models and using the SciML stack.", "recording_license": "", "do_not_record": false, "persons": [{"code": "XBN7BQ", "name": "Jadon Clugston", "avatar": null, "biography": "Software Engineer and Modeling and Simulation Consultant at JuliaHub.", "public_name": "Jadon Clugston", "guid": "2f9e5cdf-06e5-5127-a582-fd1a5cff1119", "url": "https://pretalx.com/juliacon-2026/speaker/XBN7BQ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/CYWCXK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/CYWCXK/", "attachments": []}, {"guid": "d45937f1-7ddb-5fbf-890c-77bc1af8738c", "code": "8ETRSY", "id": 92854, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8ETRSY/image_nqDFGLK.webp", "date": "2026-08-12T12:30:00+02:00", "start": "12:30", "end": "2026-08-12T13:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92854-pointcloudregistration-jl-rigid-and-non-rigid-registration-of-point-clouds", "url": "https://pretalx.com/juliacon-2026/talk/8ETRSY/", "title": "PointCloudRegistration.jl: Rigid and non-rigid registration of point clouds", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "_Rigid registration_ seems like a simple task, on the first glance: If you have two objects, described as sets of points, how should you rotate and translate one to optimally match the other? However, it can be quite involved to do it reliably and efficiently. In this talk, I will present a new Julia package that provides a go-to solution to tackle not only rigid point cloud registration, but also _non-rigid registration_ and other typical point cloud tasks.", "description": "The talk will contain aspects of the design, implementation, and usage of PointCloudRegistration.jl\n\n## Why is this interesting?\n\nBe it the molecular structure of a therapeutic protein or the surface of a sculpture in an art gallery, point clouds are a convenient model to describe real world objects. If you have two of them, you might want to combine them into a bigger object, find their differences, or compare them in a more complex way. For any of these operations, you first have to find a common frame of reference by rotating and translating one point cloud (the _source_) to match the other (the _target_). This is called _rigid registration._\n\nTo align the two point clouds beyond rigid transformations, you might assign an individual displacement to every point of the source such that the displaced source matches the target. Different methods of such _non-rigid registration_ vary in what kind of displacement they consider admissible and/or plausible.\n\n## About the package\n\nThe package aims at offering a comprehensive set of tools for all point cloud registration needs, focusing on performance, a convenient interface, and broad applicability. While some implementations already exist in the Julia ecosystem, they mostly cover the simplest case of known point-to-point correspondences and no outliers (Kabsch algorithm). Our package not only has a more time and memory efficient implementation of the Kabsch algorithm but can register point clouds in more adverse settings as well. For non-rigid registration, multiple algorithms are available, letting users of the package choose between different notions of optimal matching and plausible displacement. Finally, PointCloudRegistration.jl comes with a small amount of helper functions that can be useful when dealing with point clouds, such as thinning or conversion from density maps.", "recording_license": "", "do_not_record": false, "persons": [{"code": "XWYEK9", "name": "Andreas Kr\u00f6pelin", "avatar": "https://pretalx.com/media/avatars/N3GGEL_rHf8XbX.webp", "biography": "[Website](https://a5s.eu/)\n[Codeberg](https://codeberg.org/a5s)\n[Mastodon](https://bayes.club/@andreask)\n\nHi! I'm a PhD student at University Hospital Jena, Germany, conducting research on computational structural biology. Let's chat about point cloud registration, Bayesian statistics, algorithm engineering, science communication, or running!", "public_name": "Andreas Kr\u00f6pelin", "guid": "7e3b5ff2-893b-5508-b58e-914cd12d59f9", "url": "https://pretalx.com/juliacon-2026/speaker/XWYEK9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8ETRSY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8ETRSY/", "attachments": [{"title": "flyer-8ETRSY", "url": "/media/juliacon-2026/submissions/8ETRSY/resources/8ETRSY_TmuYCLK.png", "type": "related"}]}, {"guid": "13e25753-cda9-5b98-9206-b84e2017865c", "code": "QJYSLE", "id": 92832, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QJYSLE/image_QGBZOcC.webp", "date": "2026-08-12T15:00:00+02:00", "start": "15:00", "end": "2026-08-12T15:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92832-reseau-jl-platform-native-async-io-primitives-for-julia", "url": "https://pretalx.com/juliacon-2026/talk/QJYSLE/", "title": "Reseau.jl: Platform-Native Async IO Primitives for Julia", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Reseau.jl is a modern, pure Julia IO package that provides platform-native primitives for multithreaded event loops, unified socket interfaces, and TLS. It can be a drop-in replacement for the Sockets stdlib while providing a breadth of functionality and native platform integration (apple, linux, windows) for TLS mechanisms and trust stores. The async IO primitives can serve as foundational building blocks for a number of higher-layer application protocols, all in a multithread-friendly, performant package.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "WENVTF", "name": "Jacob Quinn", "avatar": "https://pretalx.com/media/avatars/MQ3SJP_Z0yuEym.webp", "biography": "Worked with Julia for a long time. Involved in many \"fundamental\" packages across the ecosystem, web or data related.", "public_name": "Jacob Quinn", "guid": "7bc45afc-d149-5fc1-a5b3-a2c178529ca3", "url": "https://pretalx.com/juliacon-2026/speaker/WENVTF/"}], "links": [{"title": "Reseau.jl GitHub Repository", "url": "https://github.com/JuliaServices/Reseau.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QJYSLE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QJYSLE/", "attachments": []}, {"guid": "82a3b67e-9fdf-59fb-8f47-1f3f1545d0d5", "code": "3W9MAF", "id": 92705, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3W9MAF/image_O11oIFj.webp", "date": "2026-08-12T15:45:00+02:00", "start": "15:45", "end": "2026-08-12T16:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92705-making-the-cut-norm-practical-a-julia-ecosystem-approach", "url": "https://pretalx.com/juliacon-2026/talk/3W9MAF/", "title": "Making the Cut Norm Practical: A Julia Ecosystem Approach", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "The *cut norm* of a matrix measures the largest imbalance, given by the maximum absolute sum of its entries over any choice of row and column subsets. Computing this norm amounts to a combinatorial optimization problem that is *NP-hard*. It originates in graph theory and it plays a central role in the theory of graph limits, where it defines a notion of distance between large networks.\n\nWe first present an integer program, modeled in *JuMP.jl*, and highlight the limitations in scalability. We then introduce a heuristic based on repeatedly solving a bilinear relaxation of the cut norm problem. Along the way, we utilize *NLPModels.jl* to efficiently represent the problem and solve the resulting nonlinear program using TRON from *JSOSolvers.jl*.  To explore the solution space effectively, we employ a multi-start strategy initialized via a Sobol sequence generated with *Sobol.jl*.\n\nThrough benchmarks, we show that the heuristic recovers optimal solutions on small instances and scales to significantly larger matrices than the exact formulation. We also illustrate how the algorithm can be used in the context of graph limit theory to measure the distance between large networks.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"code": "KXUETT", "name": "Martin K\u00f6hler", "avatar": null, "biography": "I am a PhD student at TU Braunschweig, Germany, specializing in nonlinear optimization and scientific computing in Julia. My research combines functional analysis and large-scale optimization, with a focus on continuous models of complex networks and dynamical systems. I am interested in developing mathematical tools that are also computationally practical.", "public_name": "Martin K\u00f6hler", "guid": "15225c19-9f96-5303-aeb4-f33ed15d180a", "url": "https://pretalx.com/juliacon-2026/speaker/KXUETT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3W9MAF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3W9MAF/", "attachments": []}, {"guid": "59bbc3f1-0497-597e-9409-714f5f5a865f", "code": "VAB77G", "id": 92876, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VAB77G/image_AFSDe5i.webp", "date": "2026-08-12T16:00:00+02:00", "start": "16:00", "end": "2026-08-12T16:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92876-convolutioninterpolations-jl-high-order-interpolation-differentiation-integration-and-smoothing-on-discrete-grids-in-arbitrary-dimensions", "url": "https://pretalx.com/juliacon-2026/talk/VAB77G/", "title": "ConvolutionInterpolations.jl: High-order interpolation, differentiation, integration and smoothing on discrete grids in arbitrary dimensions", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "ConvolutionInterpolations.jl offers methods for high-order interpolation, differentiation, integration and smoothing on discrete grids. Query times are similar to those of popular Julia packages for interpolation. Grids can be uniform, non-uniform, or a mixture. Supports mixed per-dimension interpolation, differentiation and integration. Extends naturally to multi-dimensional applications.", "description": "This talk will be a practical demonstration of the features of ConvolutionInterpolations.jl.\nAfter a brief introduction of the package and its author, this talk will proceed live in the Julia REPL.\nFeatures which will be demonstrated include:\n\n- A selection of the available interpolation methods, from simple to high accuracy.\n- Performance comparison with other interpolation packages.\n- Non-uniform grids.\n- High-order smooth derivatives from discrete samples.\n- Integrals.\n- Dimension-wise kernel and derivative selections.\n- Multi-dimensional application (2D).\n\nFuture plans include: a Python port, writing a paper, PDE applications, more documentation.\nAttendees will leave with the intuition of how, when and why to use ConvolutionInterpolations.jl.", "recording_license": "", "do_not_record": false, "persons": [{"code": "AUBBH9", "name": "Nikolaj Maack Bielefeld", "avatar": "https://pretalx.com/media/avatars/BQMRUD_Qu9Fxhy.webp", "biography": "I work as an engineer with model-based energy planning. In my sparetime I enjoy researching, developing and programming in Julia.", "public_name": "Nikolaj Maack Bielefeld", "guid": "2a1231ec-b26b-5bb8-bfe1-663aff232de1", "url": "https://pretalx.com/juliacon-2026/speaker/AUBBH9/"}], "links": [{"title": "GitHub package repository", "url": "https://github.com/NikoBiele/ConvolutionInterpolations.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VAB77G/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VAB77G/", "attachments": [{"title": "flyer-VAB77G", "url": "/media/juliacon-2026/submissions/VAB77G/resources/VAB77G_Qw25V5r.png", "type": "related"}]}, {"guid": "8fca9f4a-8c36-519e-ab33-9fcebab17ed8", "code": "SFWUKP", "id": 92375, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SFWUKP/image_q6MaszL.webp", "date": "2026-08-12T16:30:00+02:00", "start": "16:30", "end": "2026-08-12T17:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92375-deep-adaptive-experimental-design-for-sciml", "url": "https://pretalx.com/juliacon-2026/talk/SFWUKP/", "title": "Deep Adaptive Experimental Design for SciML", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Real-time adaptive experimental design for ODE models is hard: each step requires costly posterior inference and optimization. We train a neural network policy offline to amortize this cost. The Julia SciML stack makes this practical: Enzyme.jl differentiates through ODEs, Lux.jl defines the policy network, and Reactant.jl compiles everything to a single GPU program. On a bioreactor benchmark, the learned adaptive policy beats Bayesian D-optimal static designs with a 99.5% win rate.", "description": "Model-based design of experiments (MbDoE) is a fundamental methodology in engineering and the sciences: given a mechanistic model with unknown parameters, choose experimental conditions that yield the most informative data [1]. Adaptive designs use the information in the already gathered experiments to guide the remainder of the experiment. However, conventional adaptive MbDoE requires solving a computationally expensive optimization problem between every measurement, which is infeasible when experiments run in real-time [2].\n\nDeep Adaptive Design (DAD) addresses this by training a neural network policy offline to map experimental histories to optimal designs [3]. Once trained, the policy requires only a forward pass at deployment, enabling real-time adaptive decisions. We apply DAD on a differentiable mechanistic model, i.e., dynamical systems described by ODEs with known structure but uncertain parameters.\n\n## The SciML stack: Lux + Enzyme + Reactant\n\nThe core contribution of this talk is showing how three pillars of the Julia ecosystem compose to solve a problem that would be difficult in any other framework.\n\nEnzyme.jl -- differentiating through ODE solvers.\nLux.jl -- defining the policy network.\nReactant.jl -- GPU compilation of the full training loop.\n\nThe key insight is that none of these packages needed special adaptation to work together. Writing the ODE solver, the neural network, and the loss function in plain Julia was sufficient for Enzyme to differentiate through all of it and for Reactant to compile the result to GPU.\n\n## Application and results\n\nWe primarily demonstrate the approach on a fed-batch bioreactor with Monod growth kinetics. The goal is to estimate the maximum growth rate and substrate affinity constant by adaptively choosing feed rates over a 14-hour experiment based on noisy substrate concentration measurements.\nThe trained policy is compared against a Bayesian D-optimal static design. The adaptive policy achieves a 99.5% win rate over this optimized static baseline.\n\nBesides the bioreactor with Monod kinetics, an overview of several other applications is showcased.\n\n## Who should attend\n\nThis talk is relevant to anyone interested in differentiable programming in Julia, scientific machine learning, Bayesian experimental design, or composing the SciML ecosystem for non-standard workloads. No prior knowledge of experimental design is assumed.\n\n## References\n\n[1] Franceschini, G. & Macchietto, S. (2008). Model-based design of experiments for parameter precision: State of the art. Chemical Engineering Science, 63(19), 4846-4872.\n[2] Ryan, E.G., Drovandi, C.C., McGree, J.M., & Pettitt, A.N. (2016). A review of modern computational algorithms for Bayesian optimal design. International Statistical Review, 84(1), 128-154.\n[3] Foster, A., Ivanova, D.R., Malik, I., & Rainforth, T. (2021). Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. International Conference on Machine Learning, 3384-3395.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7FDTLY", "name": "Arno Strouwen", "avatar": null, "biography": "Arno Strouwen is a statistician specializing in optimal experimental design for dynamical systems. He holds a PhD from KU Leuven and teaches experimental design there. He works at PumasAI on noncompartmental analysis and in vitro-in vivo correlation, and previously worked at JuliaHub on quantitative systems pharmacology and consulting for SciML applications. His industry experience includes designing experiments for vaccines and pharmaceuticals at Johnson & Johnson.", "public_name": "Arno Strouwen", "guid": "f6392527-712a-50a2-9275-8c256aebfa39", "url": "https://pretalx.com/juliacon-2026/speaker/7FDTLY/"}, {"code": "C7BG78", "name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "avatar": null, "biography": "Software Eng. at JuliaHub & PhD student at University of Bucharest.", "public_name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "guid": "3e31b822-d61c-5033-b95f-3f20ed7eccbc", "url": "https://pretalx.com/juliacon-2026/speaker/C7BG78/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SFWUKP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SFWUKP/", "attachments": []}, {"guid": "19948a50-92b1-59f7-86eb-7edab7a00416", "code": "D8SJCG", "id": 93336, "logo": "https://pretalx.com/media/juliacon-2026/submissions/D8SJCG/image_HYQ13dA.webp", "date": "2026-08-12T17:00:00+02:00", "start": "17:00", "end": "2026-08-12T17:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93336-what-s-new-in-bestietemplate-jl", "url": "https://pretalx.com/juliacon-2026/talk/D8SJCG/", "title": "What's new in BestieTemplate.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "[BestieTemplate.jl](https://github.com/JuliaBesties/BestieTemplate.jl) is a template for creating packages following opinionated package development practices, first presented at [JuliaCon 2024](https://pretalx.com/juliacon2024/talk/9AMPUD/). This time, I'll talk about what has changed since then and try to attract some collaborators by talking about the process of adding a new question.", "description": "I will present updates to [BestieTemplate.jl](https://github.com/JuliaBesties/BestieTemplate.jl) since [JuliaCon 2024](https://pretalx.com/juliacon2024/talk/9AMPUD/), including:\n\n- Strictness levels Tiny, Light, Moderate, Robust;\n- `new_pkg_quick` for non-interactive package creation;\n- Better UX for application to existing packages;\n- New additions such as TestItem-based testing, and dependabot.\n\nI will also talk about the process of creating a new question, explaining a bit the inner workings of the package and of copier.", "recording_license": "", "do_not_record": false, "persons": [{"code": "GV3WWT", "name": "Abel Soares Siqueira", "avatar": "https://pretalx.com/media/avatars/3RVYDP_IkHlTJQ.webp", "biography": "Research Software Engineer at the Netherlands eScience Center.", "public_name": "Abel Soares Siqueira", "guid": "edf7edb8-1222-564b-8e52-641611da1882", "url": "https://pretalx.com/juliacon-2026/speaker/GV3WWT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/D8SJCG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/D8SJCG/", "attachments": [{"title": "flyer-D8SJCG", "url": "/media/juliacon-2026/submissions/D8SJCG/resources/D8SJCG_GRFoMU8.png", "type": "related"}]}, {"guid": "2030c3cc-b616-57af-890b-3e92ff5ca1e4", "code": "8A73NK", "id": 93462, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8A73NK/image_ur6VD7k.webp", "date": "2026-08-12T17:15:00+02:00", "start": "17:15", "end": "2026-08-12T17:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93462-leveraging-go-in-julia-a-story-of-interop", "url": "https://pretalx.com/juliacon-2026/talk/8A73NK/", "title": "Leveraging Go in Julia: a story of interop", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "We\u2019ll explore the interoperability of the Go programming language and Julia, from the angle of filling Julia\u2019s capability gaps in Julia\u2019s web and networking stacks by calling out to Go. The talk is based on the experience of leveraging Go\u2019s gRPC client library within a Julia-based web server, and will focus on the nuances of the interoperability of the languages (e.g. how to pass data between the languages, handling Go\u2019s asynchronous programming features, garbage collector).", "description": "Go is a statically typed, compiled programming language, widely used for web backend development, microservices, and highly concurrent applications. It has a very mature and robust support for networking, HTTP servers, and related technologies, both as part of the standard library and in the broader Go package ecosystem. Julia, on the other hand, often has limited or lacking support in these areas.\n\nIn this talk, I explore a recent project where we filled a Julia capability gap \u2014 the lack of support for gRPC \u2014 by leveraging Go. Via the Julia-C interop and Go\u2019s cgo C interoperability, we can link a Julia process to a Go shared library and use ccalls to offload the complicated bits to the robust and battle tested Go implementation of gRPC, while keeping the business logic in Julia. Go\u2019s runtime introspection also allowed us to keep the glue code quite simple and generic, without resorting to any form of code generation.\n\nThe talk will focus on the challenges and lessons from the Go-Julia interoperability, such as how to manage types, Go\u2019s garbage collector & asynchronous programming capabilities, and potential pitfalls.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ARFWGZ", "name": "Morten Piibeleht", "avatar": null, "biography": "Morten is a physicist & software engineer based in Tallinn, Estonia. He's one of the maintainers Documenter.jl and the JuliaDocs package ecosystem, and works as a software engineer at JuliaHub.\n\nGitHub: [@mortenpi](http://github.com/mortenpi)", "public_name": "Morten Piibeleht", "guid": "d2e06d5c-bbaa-50c1-a353-c61f671b1589", "url": "https://pretalx.com/juliacon-2026/speaker/ARFWGZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8A73NK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8A73NK/", "attachments": []}], "Muschel \u2014 N3": [{"guid": "1b58240d-4512-56bd-a38f-27fecf9522b3", "code": "VMYLLT", "id": 88884, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VMYLLT/image_WvO9gY7.webp", "date": "2026-08-12T10:00:00+02:00", "start": "10:00", "end": "2026-08-12T10:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-88884-data-analysis-on-global-grid-systems", "url": "https://pretalx.com/juliacon-2026/talk/VMYLLT/", "title": "Data Analysis on Global Grid Systems", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "Global grid systems (DGGS), or non-planar grids, are the new hotness - but how can you get data onto them and analyze?  In this talk, we'll show the high-level way to do this, which packages you'll need, as well as how to handle issues like geometries on the boundaries of faces, regridding error, and more.  \n\nIn all likelihood you are familiar with DGGS already - in simulation, tripolar and cubed-sphere grids are common, as are HEALPIX and other formulations.", "description": "Julia now has an up-and-coming ecosystem for global grid systems (commonly called DGGS).  The question of getting data to and from such a grid is mostly solved.  But once data is on that grid, how do you interpret and analyse it?\n\nOne of the most interesting applications here, especially for earth scientists, might be analysing data on the grid it's simulated on - thus removing regridding error, and allowing easier debugging at the simulation level.\n\nBy \"data analysis\", we mean both \"traditional\" zonal statistics and similar methods as well as more interesting things like applying stencil operations, movement analysis and more.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RQHPG", "name": "Anshul Singhvi", "avatar": "https://pretalx.com/media/avatars/7RQHPG_OxNT4Gf.webp", "biography": "Product engineer for Dyad, the new modeling and simulation language from JuliaHub.  Also heavily involved in geospatial (via JuliaGeo and GeometryOps.jl) and Makie.jl, as well as the Documenter.jl ecosystem.", "public_name": "Anshul Singhvi", "guid": "5b6d2c3a-d127-5c27-8672-a0d779e40d95", "url": "https://pretalx.com/juliacon-2026/speaker/7RQHPG/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VMYLLT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VMYLLT/", "attachments": []}, {"guid": "34580189-2a4a-5907-ab51-c109d54f475a", "code": "XFLETV", "id": 92050, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XFLETV/image_gUIGjWb.webp", "date": "2026-08-12T10:15:00+02:00", "start": "10:15", "end": "2026-08-12T10:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92050-mapmaths-jl-leveraging-julia-for-flexible-and-fast-coordinate-transformations", "url": "https://pretalx.com/juliacon-2026/talk/XFLETV/", "title": "MapMaths.jl - Leveraging Julia for Flexible and Fast Coordinate Transformations", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "Points on or near the Earth can be represented in many coordinate systems - e.g. LatLonAlt, ECEF Cartesian or WebMercator to name just three. Without careful design, the amount of code required to convert between these systems scales quadratically and therefore quickly becomes unmanageable. In this talk, I will show how MapMaths.jl leverages Julia's type system and metaprogramming features to eliminate this and related sources of combinatorial explosions in coordinate conversion libraries.", "description": "The most straightforward way to implement a coordinate conversion library is to define functions like `cartesian_from_latlonalt()` or `webmercator_from_cartesian()`, but this approach requires manually implementing a new pair of conversion functions for every pair of coordinate systems and therefore puts a substantial cost on adding new systems. Furthermore, we frequently want to convert not just between full three-dimensional coordinate systems but also partial coordinate systems like latitude-longitude or WebMercator without altitude, and this forces us to either define even more conversion functions between even more start- and endpoints, or else to sacrifice readability and potentially performance by introducing \"phantom coordinates\" whose only purpose is to fill in the holes imposed by the API. Finally, every start and end coordinate system comes with a potentially distinct geodetic datum (a mathematical reference model for the true geometry of the earth), and this adds yet another layer of combinatorial complexity. \n\nJulia has two fairly decent coordinate conversion packages, namely Geodesy.jl and CoordRefSystems.jl, but both of these packages solve the combinatorial explosion problem largely by force rather than clever software design. In this talk, I will show how MapMaths.jl leverages Julia features like multiple dispatch, Holy traits and generated functions so human programmers can implement just a spanning tree of coordinate conversions and then rely on the Julia compiler to fill in its transitive closure. As we will see, this conversion generator unlocks a new level of API flexibility and completeness, and it does that without sacrificing even an arcsecond of performance.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RZKKP", "name": "Simon Etter", "avatar": null, "biography": "Mathematician and software engineer who has been coding in Julia for over ten years. I currently work on underwater acoustic communication systems and have been working in numerical linear algebra and electronic structure calculations in the past.", "public_name": "Simon Etter", "guid": "7b7c087d-b452-540d-aaf4-7f7b1b4ce3f7", "url": "https://pretalx.com/juliacon-2026/speaker/7RZKKP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XFLETV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XFLETV/", "attachments": []}, {"guid": "6455ae91-65bd-5388-8d97-672f580b66bd", "code": "NEXMM8", "id": 92439, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NEXMM8/image_QFZrcjH.webp", "date": "2026-08-12T10:30:00+02:00", "start": "10:30", "end": "2026-08-12T10:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92439-dggs-jl-discrete-global-grid-system-native-data-cubes", "url": "https://pretalx.com/juliacon-2026/talk/NEXMM8/", "title": "DGGS.jl: Discrete Global Grid System Native Data Cubes", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "Discrete Global Grid Systems (DGGS) allow minimizing spatial distortions in geospatial image processing, among others. Here we present DGGS.jl, a Julia package to work with DGGS native data cubes using Zarr.jl and YAXArrays.jl. It transforms any raster image from a traditional projection into memory-efficient N-dimensional arrays, following one unified global coordinate system without overlapping tiles, e.g., for bounding box queries, spatial aggregation, or visualization in QGIS.", "description": "Discrete Global Grid Systems (DGGS) tessellate the earth\u2019s surface into zones of equal area and very similar shape, minimizing spatial distortions in geospatial data processing. DGGS are not only used for geocoding but also offer a highly efficient data structure by eliminating tile overlap compared to traditional grids like UTM used in many satellite missions.\n\nThe performance of operations on DGGS native data cubes is intrinsically linked to the cell index. Many real-world applications, such as visualization or convolutions, require efficient handling of higher-order neighbor queries based on spatial distances, motivating a multidimensional spatial index as coordinates in the n-dimensional array.\n\nIn response to these challenges, we introduce DGGS.jl (https://danlooo.github.io/DGGS.jl), a Julia package specifically developed to create and utilize DGGS native data cubes optimized for neighbor queries. Our package employs the DGGRID Q2DI index to store data on a grid based on the Icosahedral Snyder Equal Area projection, enabling compact and efficient data cube arrays. We have implemented methods to seamlessly convert raster data between geographic and Q2DI coordinates, access neighbor disks around a given cell, and visualize these data on a global scale. In addition, we developed an XYZ tile server, allowing us to view DGGS native data cubes in QGIS and in the browser. Finally, we applied DGGS.jl on a subset of the Sentinel-2 archive.", "recording_license": "", "do_not_record": false, "persons": [{"code": "L33FHU", "name": "Daniel Loos", "avatar": "https://pretalx.com/media/avatars/WXQCHZ_OlMcQ4X.webp", "biography": "I'm a postdoctoral researcher at the Max Planck Institute for Biogeochemistry in Jena, Germany. Originally coming from a bioinformatics background, I now work on software and data formats making geospatial data like satellite imagery less distorted.", "public_name": "Daniel Loos", "guid": "45eb0afc-81d4-50a6-b62a-d074391fac67", "url": "https://pretalx.com/juliacon-2026/speaker/L33FHU/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NEXMM8/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NEXMM8/", "attachments": [{"title": "flyer-NEXMM8", "url": "/media/juliacon-2026/submissions/NEXMM8/resources/NEXMM8_px2cuPU.png", "type": "related"}]}, {"guid": "a433bcb1-4757-5c84-9e03-c990b3dfb182", "code": "YKG9N9", "id": 92568, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YKG9N9/image_fxJqkYP.webp", "date": "2026-08-12T10:45:00+02:00", "start": "10:45", "end": "2026-08-12T11:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92568-exploring-meteorological-satellite-observations-with-metopdatasets-jl", "url": "https://pretalx.com/juliacon-2026/talk/YKG9N9/", "title": "Exploring Meteorological Satellite Observations with MetopDatasets.jl", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "Earth system science needs user-friendly access to global observations. MetopDatasets.jl is a package to read native-format products from the Metop satellites, which play a key role in current global weather models through data assimilation. MetopDatasets.jl also demonstrates how the CommonDataModel.jl interface can be used to build a user-friendly reader for custom binary formats common in Earth sciences.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "BMSMEH", "name": "Simon Kok Lupemba", "avatar": null, "biography": "Remote sensing scientist at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). He works in the field of scatterometry, maintaining the scientific software used to process data from the current ASCAT instrument. He is also involved in the development of calibration and validation software for Europe\u2019s next scatterometer, SCA, scheduled for launch in 2026. In addition to his scientific work, Simon actively promotes open-source development within EUMETSAT and is the author of MetopDataset.jl, EUMETSAT\u2019s first Julia package.", "public_name": "Simon Kok Lupemba", "guid": "2daf0cdf-180b-50e7-9319-1654f4956bdd", "url": "https://pretalx.com/juliacon-2026/speaker/BMSMEH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YKG9N9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YKG9N9/", "attachments": []}, {"guid": "bc011004-717b-5306-addb-f86a2b82ccc2", "code": "33JU7Y", "id": 92667, "logo": "https://pretalx.com/media/juliacon-2026/submissions/33JU7Y/image_xIyEkRY.webp", "date": "2026-08-12T11:15:00+02:00", "start": "11:15", "end": "2026-08-12T11:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92667-fast-geospatial-lookups-across-projections-using-sphericalspatialtrees-jl", "url": "https://pretalx.com/juliacon-2026/talk/33JU7Y/", "title": "Fast geospatial lookups across projections using SphericalSpatialTrees.jl", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "The SpatialTreeInterface defined in GeometryOps.jl provides an efficient way for geometrical queries of polygons that share a crs by relying on search trees whose branches are characterized by rectangular bounding boxes. However, when working across different projections, for example when trying to identify intersecting polygons from different projections on the sphere, rectangles in one projection do not translate into rectangles in another projection, so the tree traversal will not be accurate. \n\nTo solve this problem, we extended the SpatialTreeInterface in SphericalSpatialTrees.jl by replacing rectangular bounding boxes with SphericalCaps, using these to characterize the extent of all branches in a spatial tree. This enables users to do combined tree searches across polygons based on different projections, as is common e.g. in Discrete Global Grid System. The presentation will demonstrate the basic concept of the SphericalSpatialTrees.jl package as well as a few downstream applications. \n\nI order to solve the problem of", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "LW7UQZ", "name": "Fabian Gans", "avatar": null, "biography": "I am a physicist by training and am currently studying Global Biogeochemical Cycles in the Earth System using Remote Sensing, Meteorological and other data sets based at the Max-Planck-Institute for Biogeochemistry, Jena, Germany.\nMy first commit to my first Julia package dates back to the year 2012 and since then I have authored and contributed to packages in the Julia Geodata and processing ecosystem, examples are NetCDF.jl, Zarr.jl, DiskArrays.jl, YAXArrays.jl EarthDataLab.jl and others. Some may know me under my github tag @meggart", "public_name": "Fabian Gans", "guid": "8d983e03-6d93-5fd0-9ae5-1c22a78be090", "url": "https://pretalx.com/juliacon-2026/speaker/LW7UQZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/33JU7Y/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/33JU7Y/", "attachments": []}, {"guid": "d4645f82-7439-58e4-a80c-8d8ab46dadbc", "code": "8RGCRS", "id": 92743, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8RGCRS/image_lj8c0IX.webp", "date": "2026-08-12T11:30:00+02:00", "start": "11:30", "end": "2026-08-12T11:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92743-spatial-machine-learning-for-digital-soil-mapping", "url": "https://pretalx.com/juliacon-2026/talk/8RGCRS/", "title": "Spatial Machine Learning for Digital Soil Mapping", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "Spatial machine learning has become increasingly crucial for environmental prediction tasks. Yet, current workflows in R and Python face challenges when scaling to high\u2011resolution, national\u2011level mapping and when integrating modern uncertainty\u2011aware methods. In this talk, I present a new Julia\u2011based spatial machine learning framework for digital soil mapping, focusing on national soil organic carbon (SOC) prediction in Estonia. The approach combines Random Forest models, stacked meta\u2011learning, and conformal prediction through the MLJ ecosystem, while developing an integration port to Julia of the IGEO7 discrete global grid system (DGGS) to impose a hierarchical spatial structure.\nThis approach targets persistent issues in spatial ML, such as autocorrelation, multi\u2011scale dependencies, and computational efficiency. It implements DGGS\u2011based multi\u2011resolution covariate aggregation, spatially aware cross\u2011validation, Shapley values, and area\u2011of\u2011applicability (AOA) assessment using the Dissimilarity Index method. Initial results demonstrate improved spatial fidelity, scalable high-resolution prediction, and more transparent communication of uncertainty.\nThis work showcases how Julia\u2019s speed and composability enable a modern, reproducible, and scalable approach to spatial machine learning in comparison to what conventional Python/R workflows currently offer.", "description": "Digital soil mapping increasingly relies on spatial machine learning techniques that must balance predictive accuracy, spatial fidelity, and computational scalability. Recent research in the Python and R ecosystems highlights the advances in explicit spatial structure, multi\u2011scale context, and robust uncertainty quantification. However, for experimenting with large\u2011area, high\u2011resolution prediction tasks, Python felt inefficient. Building on earlier national\u2011scale experiments in Estonia and leveraging the increasingly mature MLJ ecosystem, this project explores a fully Julia\u2011based spatial ML pipeline to improve the modelling of soil organic carbon (SOC).\n\nThe workflow integrates three components:\n\n- Random Forest (RF) models implemented via MLJ / DecisionTree for nonlinear, interaction\u2011rich prediction;\n- Stacked ML meta\u2011learning to combine predictions across multiple model families and spatial resolutions (DGGS-based neighbourhood kernels and parent-relationship to model spatial structure and tele-connections);\n- Conformal prediction to generate calibrated, spatially explicit uncertainty intervals.\n\nA key innovation is the integration of the IGEO7 discrete global grid system (DGGS). DGGS are increasingly used, knowingly and unknowingly (HEALPix, H3, etc). IGEO7 originates from the DGGRID tool (Sahr, K., https://github.com/allixender/DggridRunner.jl, a native CxxWrap binding is still under development). We made it available to Julia as the fundamental spatial scaffold. Several core functionalities, such as Z7-indexing-based neighbourhoods, are now natively implemented in Julia (https://github.com/allixender/Z7.jl), as is the required authalic conversion from the spherical DGGS to the WGS84 ellipsoid. IGEO7\u2019s equal\u2011area, multi\u2011resolution hierarchy provides a principled alternative to traditional spatial ML approaches based on k\u2011nearest neighbours, coordinate distances, or buffer\u2011based metrics. Environmental covariates (climate, terrain, land cover, and Estsoil profile data) are aggregated at several DGGS resolutions, allowing the models to capture hierarchical spatial dependencies similar to those of multi\u2011mesh graph structures proposed in current spatial ML research (e.g., Google GraphCast).\nVariable preparation follows the literature to enhance the role of predictors most relevant to SOC modelling, including various terrain and geomorphological indices (e.g., via Geomorphometry.jl) and remote sensing indices, such as NDVI and more. These predictors form the basis for both model training and the dissimilarity index (DI) used in the area of applicability (AOA) framework, which is an increasingly standard requirement in spatial ML for assessing extrapolation risk.\nTraining samples are drawn within the DGGS structure to ensure spatial representativeness, and spatial cross\u2011validation is performed using DGGS\u2011consistent blocking schemes to counter overoptimistic estimates caused by autocorrelation. The resulting models can generate predictions at multiple IGEO7 levels, supporting both fine\u2011resolution mapping and aggregated, scalable national\u2011level assessments.\n\nUncertainty assessment combines two complementary approaches:\n\n- Conformal prediction, offering distribution\u2011free uncertainty intervals calibrated to the empirical error structure;\n- AOA\u2011based spatial validity masks, identifying regions where model predictions are reliable based on weighted distances in predictor space.\n\nIn a future research paper, we aim to explore and discuss in more detail the similarities and differences in the uncertainty results between the two methods. Additionally, Quantile Random Forest would be great, but it is not yet available in Julia.\n\nBy building this workflow almost entirely in Julia, the project benefits from Julia\u2019s performance for large\u2011scale raster/grid operations, MLJ\u2019s flexible model composition, and the ease of integrating custom spatial data structures such as IGEO7. The talk will present the modelling pipeline, computational aspects, evaluation results, and lessons learned about implementing advanced spatial ML methods in the Julia ecosystem.\nThis work demonstrates how Julia can serve as a powerful platform for modern spatial machine learning, offering performance, composability, and extensibility in comparison to what is typically feasible in Python/R\u2011based workflows.", "recording_license": "", "do_not_record": false, "persons": [{"code": "SR3W37", "name": "Alexander Kmoch", "avatar": "https://pretalx.com/media/avatars/GAZBJW_7WYtNa8.webp", "biography": "Alex is an Associate Professor in Geoinformatics and a Distributed Spatial Systems Researcher with many years of experience in geospatial data management and web- and cloud-based geoprocessing with a particular focus on land use, soils, hydrology, hydrogeology and water quality data. His interests include Discrete Global Grid Systems (DGGS), OGC standards and web-services for environmental and geo-scientific data sharing, modelling workflows and interactive geo-scientific visualisation. He is also the European co-chair of the OGC DGGS working group.", "public_name": "Alexander Kmoch", "guid": "9b45f6f3-fe5e-58d5-9e66-0ff6cd7819bf", "url": "https://pretalx.com/juliacon-2026/speaker/SR3W37/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8RGCRS/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8RGCRS/", "attachments": []}, {"guid": "d7b3f8e2-13fd-5eec-9265-5dbe66b5c056", "code": "TRBDEP", "id": 92781, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TRBDEP/image_jWLeT7P.webp", "date": "2026-08-12T11:45:00+02:00", "start": "11:45", "end": "2026-08-12T12:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92781-state-of-geodataframes-jl", "url": "https://pretalx.com/juliacon-2026/talk/TRBDEP/", "title": "State of GeoDataFrames.jl", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "Reading and writing geospatial vector data is the bread and butter of any spatial ecosystem. In this short talk we present GeoDataFrames.jl, the default entrypoint for spatial vector data in the JuliaGeo organisation. We discuss recent and planned updates, such as support for native drivers and metadata passthrough, but also spatial indexing and lazy reading of datasets in the cloud.", "description": "Every spatial ecosystem needs packages to read and write vector data. In the JuliaGeo ecosystem, we've done so with GDAL, ArchGDAL and GeoDataFrames packages for a long time, slowly introducing more native file formats (ShapeFile, GeoJSON, GeoArrow). In the past (and current) year, GeoDataFrames.jl has seen an uptick in developments, as the community considers the package (should become) an entrypoint for the whole ecosystem.\n\nRecent developments include:\n- Native Julia driver support when loaded using extensions\n- Metadata passthrough support, keeping metadata intact when possible\n- GeometryOps integration, deprecating ArchGDAL operations\n- GeometryVector (column) to make some handling of spatial data easier\n\nFuture planned developments include:\n- Native GeoDataFrame <: AbstractDataFrame type so dispatch becomes easier\n- Spatial index support (from GeometryOps) on GeometryVector\n- Lazy reading mode\n\nThe above two developments (ideally with the new FilePaths design proposals) should enable lazily reading and subsetting of cloud native datasets.", "recording_license": "", "do_not_record": false, "persons": [{"code": "Q33DZA", "name": "Maarten Pronk", "avatar": "https://pretalx.com/media/avatars/Q33DZA_3OUcpRU.webp", "biography": "Maarten Pronk is a researcher at Deltares and an external PhD candidate at the Delft University of Technology. He holds a MSc in Geomatics and a BSc in Architecture, both from the Delft University of Technology (NL). His research concerns elevation modelling, especially in lowlands prone to coastal flooding. Currently, he works on applying data from ICESat-2, a LiDAR satellite, to global elevation models.", "public_name": "Maarten Pronk", "guid": "48948f2e-0be4-5708-b9b3-efc0aceb55fe", "url": "https://pretalx.com/juliacon-2026/speaker/Q33DZA/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TRBDEP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TRBDEP/", "attachments": []}, {"guid": "c674a056-daef-5d17-ac9c-3bb822048d70", "code": "9TXRNM", "id": 93402, "logo": "https://pretalx.com/media/juliacon-2026/submissions/9TXRNM/image_pO6hf4V.webp", "date": "2026-08-12T12:00:00+02:00", "start": "12:00", "end": "2026-08-12T12:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93402-geometryops-jl-finally-on-the-sphere", "url": "https://pretalx.com/juliacon-2026/talk/9TXRNM/", "title": "GeometryOps.jl: finally on the sphere!", "subtitle": "", "track": "Geospatial minisymposium", "type": "Short talk", "language": "en", "abstract": "I've been talking about this for the last two years - but GeometryOps.jl is now finally on the sphere!  This talk will give a brief update about GeometryOps with a focus on how the spherical capabilities have materialized, both in native Julia and by calling Google's s2 library.  We'll also mention some new cool downstream applications, like ConservativeRegridding.jl and SphericalSpatialTrees.jl, and specifically how they relate to GeometryOps.", "description": "The conventional idea of geospatial geometry processing is that it happens on a 2-dimensional plane.  Given the rise of global grid systems as new storage formats, a focus on the north and south poles as the harbingers of climate change, and increased computational budgets, in many modern usecases it may not be desirable to compute in 2D anymore.\n\nWe've been talking about this for a while, but GeometryOps now finally has 2-dimensional polygon intersection natively available in Julia, along with some other utility functions like area, perimeter, etc.  It also has a connection to Google's s2 library via the [s2geography](https://github.com/paleolimbot/s2geography) library, which is the same wrapper library used in R and Python.  GeometryOps should thus be capable of performing most, if not all, necessary operations on the sphere.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RQHPG", "name": "Anshul Singhvi", "avatar": "https://pretalx.com/media/avatars/7RQHPG_OxNT4Gf.webp", "biography": "Product engineer for Dyad, the new modeling and simulation language from JuliaHub.  Also heavily involved in geospatial (via JuliaGeo and GeometryOps.jl) and Makie.jl, as well as the Documenter.jl ecosystem.", "public_name": "Anshul Singhvi", "guid": "5b6d2c3a-d127-5c27-8672-a0d779e40d95", "url": "https://pretalx.com/juliacon-2026/speaker/7RQHPG/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/9TXRNM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/9TXRNM/", "attachments": []}, {"guid": "7e6fe688-358f-56f5-b879-3ea929a1f085", "code": "T99BXJ", "id": 104238, "logo": "https://pretalx.com/media/juliacon-2026/submissions/T99BXJ/image_oZcDrmD.webp", "date": "2026-08-12T12:15:00+02:00", "start": "12:15", "end": "2026-08-12T13:00:00+02:00", "duration": "00:45", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-104238-julia-geospatial-community-meeting", "url": "https://pretalx.com/juliacon-2026/talk/T99BXJ/", "title": "Julia Geospatial Community Meeting", "subtitle": "", "track": "Geospatial minisymposium", "type": "Long talk", "language": "en", "abstract": "The Julia ecosystem for handling geospatial data and analysis is growing and improving. This is a session for anyone interested in geo in Julia to meet package contributors and users to discuss future direction and collaboration efforts.\n\nIn this community building session we aim to:\n\n- Hear what people are doing in Julia and Geo\n- Develop group goals and common understanding for ecosystem coherence\n- Uncover gaps and rough edges in the ecosystem\n- Lower the barrier to getting involved\n\nA rough program:\n\n- Introductions: what are you using Julia for, in three words\n- Show and tell: share your geo packages and projects that don't have their own JuliaCon talks\n- The good, the bad and the ugly in Julia geospatial: a constructive session of gripe-driven development\n- Slido poll with questions on datasets, pain points, nice things, integrations, major wishes for the ecosystem", "description": "The Julia ecosystem for handling geospatial data and analysis is growing and improving. This is a session for anyone interested in geo in Julia to meet package contributors and users to discuss future direction and collaboration efforts.\n\nIn this community building session we aim to:\n\n- Hear what people are doing in Julia and Geo\n- Develop group goals and common understanding for ecosystem coherence\n- Uncover gaps and rough edges in the ecosystem\n- Lower the barrier to getting involved\n\nA rough program:\n\n- Introductions: what are you using Julia for, in three words\n- Show and tell: share your geo packages and projects that don't have their own JuliaCon talks\n- The good, the bad and the ugly in Julia geospatial: a constructive session of gripe-driven development\n- Slido poll with questions on datasets, pain points, nice things, integrations, major wishes for the ecosystem", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RQHPG", "name": "Anshul Singhvi", "avatar": "https://pretalx.com/media/avatars/7RQHPG_OxNT4Gf.webp", "biography": "Product engineer for Dyad, the new modeling and simulation language from JuliaHub.  Also heavily involved in geospatial (via JuliaGeo and GeometryOps.jl) and Makie.jl, as well as the Documenter.jl ecosystem.", "public_name": "Anshul Singhvi", "guid": "5b6d2c3a-d127-5c27-8672-a0d779e40d95", "url": "https://pretalx.com/juliacon-2026/speaker/7RQHPG/"}, {"code": "Q33DZA", "name": "Maarten Pronk", "avatar": "https://pretalx.com/media/avatars/Q33DZA_3OUcpRU.webp", "biography": "Maarten Pronk is a researcher at Deltares and an external PhD candidate at the Delft University of Technology. He holds a MSc in Geomatics and a BSc in Architecture, both from the Delft University of Technology (NL). His research concerns elevation modelling, especially in lowlands prone to coastal flooding. Currently, he works on applying data from ICESat-2, a LiDAR satellite, to global elevation models.", "public_name": "Maarten Pronk", "guid": "48948f2e-0be4-5708-b9b3-efc0aceb55fe", "url": "https://pretalx.com/juliacon-2026/speaker/Q33DZA/"}, {"code": "RWMFJM", "name": "Felix Cremer", "avatar": null, "biography": null, "public_name": "Felix Cremer", "guid": "6917a484-c35b-5c54-8572-a5b5019dfec7", "url": "https://pretalx.com/juliacon-2026/speaker/RWMFJM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/T99BXJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/T99BXJ/", "attachments": []}, {"guid": "ee34fa1d-e07f-54e5-bcf8-f69fa6bfcfc1", "code": "UVWQCA", "id": 92250, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UVWQCA/image_0KddAud.webp", "date": "2026-08-12T14:30:00+02:00", "start": "14:30", "end": "2026-08-12T14:40:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92250-atlas-a-global-atmospheric-chemistry-and-transport-model-written-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/UVWQCA/", "title": "ATLAS: A global atmospheric chemistry and transport model written in Julia", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "ATLAS is a global atmospheric chemistry and transport model with a focus on stratospheric chemistry, and in particular anthropogenic ozone depletion (the \"ozone hole\"). It was originally written in Matlab (starting in 2009) and has been rewritten in Julia in the last 5 years, resulting in a 10x speedup. We give an overview of the implementation and of the scientific results published in the last 15 years.", "description": "ATLAS is a global atmospheric chemistry and transport model (commonly known as CTM). In contrast to many other models, it is not based on a Eulerian approach (\"grid boxes\"), but on a Lagrangian approach (\"air parcel trajectories\"). It has a focus on stratospheric chemistry (in particular anthropogenic ozone depletion), but also has seen some extensions in the last years. These include, e.g., tropospheric convection on individual trajectories and the chemistry of sulfur dioxide.\n\nWe give a short overview over the implementation of the model and the algorithms. ATLAS is based on a modular approach, and several of the components, like the air parcel trajectory model or the chemistry \"box\" model can also be used as stand-alone components.\n\nIn contrast to a climate model, ATLAS does not have a \"dynamical core\". Temperatures and winds are obtained from external meteorological data (e.g. ECMWF ERA5). Transport and mixing (diffusion) are simulated. In contrast to an Eulerian model, atmospheric diffusion can be tuned to observations in the real atmosphere.\n\nATLAS has been used in numerous scientific studies and publications in the last 15 years, and we will show some examples of the application of the model.\n\nGitLab repository (not public): https://gitlab.awi.de/iwohltmann/atlas-julia\n\nSnapshot of the code: https://doi.org/10.5281/zenodo.21103974", "recording_license": "", "do_not_record": false, "persons": [{"code": "UZAEXN", "name": "Ingo Wohltmann", "avatar": "https://pretalx.com/media/avatars/BG8TTW_Vb3RVzJ.webp", "biography": "I am a senior scientist at the Alfred Wegener Institute for Polar and Marine Research in Potsdam and in Bremerhaven, Germany. I have obtained my PhD in 2003 at the University of Bremen, Germany, and have been working at the Alfred Wegener Institute in the area of atmospheric science with a focus on the stratosphere since then. Since 2009, I am the developer, maintainer and scientist behind the ATLAS chemistry and transport model, and I am focussing my research on the model.", "public_name": "Ingo Wohltmann", "guid": "74cc73a3-b260-5ac3-82d1-6fc1613bef04", "url": "https://pretalx.com/juliacon-2026/speaker/UZAEXN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UVWQCA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UVWQCA/", "attachments": [{"title": "flyer-UVWQCA", "url": "/media/juliacon-2026/submissions/UVWQCA/resources/UVWQCA_c2sKtl9.png", "type": "related"}]}, {"guid": "23ef0a85-e566-5a6f-9a61-d7b74fbe3cfc", "code": "LFPLJT", "id": 92549, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LFPLJT/image_LzdsWdv.webp", "date": "2026-08-12T14:40:00+02:00", "start": "14:40", "end": "2026-08-12T14:50:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92549-helmut-a-modular-and-extensible-snow-cover-model", "url": "https://pretalx.com/juliacon-2026/talk/LFPLJT/", "title": "Helmut: A Modular and Extensible Snow Cover Model", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "The snow cover plays a central role in many Earth system processes, for example influencing climate feed-back, the hydrological cycle, as well as glacier and ice sheet mass balance. Widely used snow cover models such as SNOWPACK (C++) and Crocus (Fortran) show strong performance in operational forecasting and scientific modeling.  However, their still tightly entangled code bases make it difficult for the community to modify model components efficiently. This is due to technical dept as well as limitations of the programming languages itself.  Recent advances in snow physics parameterizations have highlighted structural limitations in those models, underscoring the need for a more flexible modelling framework. \n\nTo address these challenges, we present Helmut, a modular snow cover model implemented in Julia. Helmut combines a state\u2011of\u2011the\u2011art snow process equation solver within a design that emphasizes flexibility, and quick experimentation. Julia\u2019s multiple dispatch allows Helmut users to extend or replace individual components\u2014such as parameterizations, boundary\u2011conditions, or physical process formulations - efficiently. This enables domain scientists to contribute easily and test new advances with minimal friction. \n\nHelmut successfully reproduces simulations from existing snow models while offering a much more accessible framework for modifying model physics, testing new parameterizations, and experimenting with alternative numerical formulations. We further demonstrate how the structure enables exploration of the impacts of different parameterizations and physical configurations, making such investigations considerably easier.", "description": "Helmut was developed in collaboration between the Snow Studies Centre in Grenoble, France, the developers of Crocus and the WSL Institute for Snow and Avalanche research SLF in Davos, Switzerland, the developers of SNOWPACK. It therefore benefited from the combined knowledge, with the goal of having a unified snow model that can be adjusted as needed. Such adjustments can be as simple as introducing a new type of material layer, for example glacier ice, or a new parameterziation for thermal conductivity, to as complex as a new physics-based process that needs to be solved in a coupled fashion with other processes. As of now, most parametrizations for snow follow the Crocus model, while the integrated soil model closely follows the implementation in SNOWPACK. The goal is to have parametrizations that are like SNOWPACK\u2019s and Crocus\u2019s in the model and as a starting point for community-driven future model developments. In this contribution, we show the general model structure and show examples of how the flexible model structure can be leveraged to explore different physics configurations and add new parametrizations. One of these examples demonstrate how soil layers can be defined as extension of the general layer type, and the inclusion of a new detailed radiative transfer process by implementing the Tartes model for albedo.", "recording_license": "", "do_not_record": false, "persons": [{"code": "MRDN9B", "name": "Patrick  Leibersperger", "avatar": null, "biography": "Background in physics and complex systems, transitioned to scientific software engineering at the Institute for Snow and Avalanches.", "public_name": "Patrick  Leibersperger", "guid": "d4628f5a-205a-5a76-abbb-8a2799a2377e", "url": "https://pretalx.com/juliacon-2026/speaker/MRDN9B/"}, {"code": "JZ3L8D", "name": "de Fleurian Basile", "avatar": null, "biography": null, "public_name": "de Fleurian Basile", "guid": "57e0f654-b168-5637-9fa0-dc2eb57540d7", "url": "https://pretalx.com/juliacon-2026/speaker/JZ3L8D/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LFPLJT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LFPLJT/", "attachments": [{"title": "flyer-LFPLJT", "url": "/media/juliacon-2026/submissions/LFPLJT/resources/LFPLJT_cRsJmKX.png", "type": "related"}]}, {"guid": "53110e0c-743a-5050-918c-cc8727262c8e", "code": "VB7AEV", "id": 92672, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VB7AEV/image_gBYDLwc.webp", "date": "2026-08-12T14:50:00+02:00", "start": "14:50", "end": "2026-08-12T15:00:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92672-idealized-atmospheric-flow-and-gravity-wave-modeling-with-pincflow-jl", "url": "https://pretalx.com/juliacon-2026/talk/VB7AEV/", "title": "Idealized Atmospheric Flow and Gravity-Wave Modeling with PinCFlow.jl", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "[PinCFlow.jl](https://github.com/Atmospheric-Dynamics-GUF/PinCFlow.jl) is a fully Julia-based idealized atmospheric flow solver, primarily developed for gravity wave research. The model is highly efficient and integrates the Boussinesq, pseudo-incompressible, and compressible equations over arbitrary topography. Either high-resolution, wave-resolving simulations can be conducted, or simulations with parameterized gravity waves using the parameterization scheme MS-GWaM, where both transience and horizontal propagation of gravity waves are accounted for.", "description": "[PinCFlow.jl](https://github.com/Atmospheric-Dynamics-GUF/PinCFlow.jl) (Pseudo-inCompressible Flow solver), developed for conducting idealized atmospheric simulations, integrates the Boussinesq, pseudo-incompressible, and compressible equations using a finite volume approach and a semi-implicit time-stepping scheme. Due to MPI parallelization in all spatial dimensions, high-resolution, wave-resolving simulations can be conducted efficiently. The use of a terrain-following coordinate system allows for arbitrary surface topography.\n\nA unique feature of [PinCFlow.jl](https://github.com/Atmospheric-Dynamics-GUF/PinCFlow.jl) is its numerical realization of multiple-scale WKB theory for the parameterization of gravity-wave impacts on mean-flow dynamics using MS-GWaM (Multi-Scale Gravity-Wave Model). Here, gravity waves are approximated using a ray-tracing technique, where so-called ray volumes propagate through a six-dimensional phase-space. Unlike conventional single-column, steady-state parameterizations, MS-GWaM accounts for wave transience and horizontal propagation.\n\nCurrent research and development focuses on the generation of orographic gravity waves, the interaction between gravity waves and turbulence, and their impact on ice cloud formation and tracer transport.\n\nOriginally developed in Fortran 90, the solver has been fully translated and optimized as a Julia package. Now officially registered and accompanied by extensive documentation, [PinCFlow.jl](https://github.com/Atmospheric-Dynamics-GUF/PinCFlow.jl) provides a modern, high-performance framework for research in computational fluid dynamics.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TEFTJL", "name": "Irmgard Steiger", "avatar": null, "biography": ".", "public_name": "Irmgard Steiger", "guid": "eb6459c6-a7b5-5d82-bb0f-7b8cdd94a740", "url": "https://pretalx.com/juliacon-2026/speaker/TEFTJL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VB7AEV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VB7AEV/", "attachments": []}, {"guid": "87569daf-d0f7-5fd9-b76a-a8506de2942d", "code": "TGHPQ9", "id": 92693, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TGHPQ9/image_LBD4jKG.webp", "date": "2026-08-12T15:00:00+02:00", "start": "15:00", "end": "2026-08-12T15:10:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92693-online-calibration-of-a-neural-network-parameterization-in-shallowwaters-jl", "url": "https://pretalx.com/juliacon-2026/talk/TGHPQ9/", "title": "Online calibration of a Neural Network Parameterization in ShallowWaters.jl", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "Physical effects in oceans span a vast range of both temporal and spatial scales, making it difficult to ensure that a single numerical model resolves all relevant processes within realistic computational limits. To capture effects that occur outside of the resolved scales, models need to include parameterizations that approximate the missing physics. In this work, we explore the use of online learning for parameterizations in a Julia based shallow water model. \n\nHere we use ShallowWaters.jl, a single layer ocean model, to test the capabilities of online learning for eddy backscatter parameterizations. Backscatter parameterizations represent the influence of geostrophic eddies, small-scale turbulent processes that play a large role in ocean energy dynamics. In particular, eddies facilitate the transfer of kinetic energy from small to large scales, an essential process for general ocean circulation. In the online learning framework our backscatter parameterization is given by a neural network (NN), converting ShallowWaters into a hybrid ocean model based on both physics and machine learning. To train the NN parameterization to capture sub-grid scale physics we use data assimilation techniques, and in particular this relies on the automatic differentiation (AD) tool Enzyme.jl to compute full-model gradients. This further expands ShallowWaters to be a fully differentiable shallow water model. Different loss functions are implemented, both spectral and state, to determine which most effectively improves the parameterization, and the resulting NN parameterizations are compared to an equation-discovery closure. This work expands on prior online learning research and further advances hybrid approaches for gradient-based model calibration in comprehensive, differentiable, ocean general circulation models.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"code": "FSAGLX", "name": "Sarah Williamson", "avatar": "https://pretalx.com/media/avatars/FSAGLX_OPLqip8.webp", "biography": "I'm a PhD candidate at the University of Texas at Austin. My research lives in the realm of computational oceanography where I broadly work on utilizing differentiable ocean models for training subgrid-scale parameterizations.", "public_name": "Sarah Williamson", "guid": "c229f315-3b5c-5fe0-9f36-e177770cc528", "url": "https://pretalx.com/juliacon-2026/speaker/FSAGLX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TGHPQ9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TGHPQ9/", "attachments": []}, {"guid": "35e7eb69-08fd-565f-9117-47c51a499280", "code": "9RKTV9", "id": 92819, "logo": "https://pretalx.com/media/juliacon-2026/submissions/9RKTV9/image_UmKqooE.webp", "date": "2026-08-12T15:10:00+02:00", "start": "15:10", "end": "2026-08-12T15:20:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92819-snow-modelling-for-operational-and-research-applications", "url": "https://pretalx.com/juliacon-2026/talk/9RKTV9/", "title": "Snow modelling for operational and research applications", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "When snow falls, the landscape transforms into a sparkling white marvel. Beyond its beauty, snow is essential to human sustenance across large regions: it replenishes drinking water supplies, moderates our planet's temperature, drives hydropower production, and feeds irrigation systems. Yet snow also brings hazards. Avalanches pose a persistent threat in mountainous terrain, rapid snowmelt combined with heavy rainfall can trigger devastating floods, and intense snowfall events regularly disrupt road and air traffic at considerable economic cost. Preparing effectively for such events demands reliable forecasts of snow conditions. In Switzerland, where a substantial fraction of precipitation falls as snow, the WSL Institute for Snow and Avalanche Research (SLF) provides such forecasts using a physics-based snow modelling system recently implemented in the Julia programming language. These forecasts support avalanche and flood forecasting as well as weather-related hazard alerts. Our model is also used across a range of research projects, including efforts to improve inflow forecasts for Norwegian hydropower reservoirs and to better characterize snow dynamics on glaciers in high-mountain regions. Here, we present a brief overview of the operational use cases of this newly developed system alongside its research applications, together with a more detailed account of our technical implementation and the challenges encountered so far. We welcome feedback on our technical implementation and are eager to explore potential collaborations in which the model could be coupled with other Earth system models.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"code": "CZ8LUZ", "name": "Jan Magnusson", "avatar": null, "biography": "https://www.slf.ch/en/staff/magnusso/", "public_name": "Jan Magnusson", "guid": "859ee47a-3b89-5c0e-9606-5fa841a6e05b", "url": "https://pretalx.com/juliacon-2026/speaker/CZ8LUZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/9RKTV9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/9RKTV9/", "attachments": []}, {"guid": "0ec5eab7-886c-5eeb-b78c-a797952d5780", "code": "GB8WXW", "id": 92721, "logo": "https://pretalx.com/media/juliacon-2026/submissions/GB8WXW/image_9x34A1C.webp", "date": "2026-08-12T15:20:00+02:00", "start": "15:20", "end": "2026-08-12T15:30:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92721-speedyweather-jl-towards-a-differentiable-and-gpu-capable-general-circulation-model", "url": "https://pretalx.com/juliacon-2026/talk/GB8WXW/", "title": "SpeedyWeather.jl: Towards a differentiable and GPU-capable general circulation model", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "Traditionally, climate models are difficult to run for end users, and even harder to customize or interface with machine learning. We want to change that. Here, we present the ongoing development of SpeedyWeather.jl: A general circulation model that\u2019s differentiable, GPU-capable and ready for machine learning integration. SpeedyWeather.jl is a spectral atmospheric general circulation model with an everything-flexible attitude. In this talk, we will give an overview of SpeedyWeather.jl\u2019s development of the last year, in which we worked towards differentiability with Enzyme, GPU-capability with KernelAbstractions and Reactant and rewrote our parametrizations for better performance and more customisability.", "description": "The current generation of usually Fortran-based climate models presents a high entry barrier to climate modelling. Julia on the other hand gives us the tools to write climate models that are performant, but at the same time easy to use for both end users and developers. This extends to differentiable programming and GPU programming as well. In the past year, we spent a considerable effort on making SpeedyWeather.jl differentiable with Enzyme, GPU-capable with KernelAbstractions and Reactant. We also extended our process implementations by adding new parameterizations of radiation, and simple sea ice, land and snow models to run climate simulations with SpeedyWeather.jl. In this talk, we will give an overview on the changes we had to make for this. These changes also enabled us to redesign parts of our model for even better customisability and composability, as demonstrated for example by a new parametrization system. Furthermore, we will give an outlook on using SpeedyWeather.jl differentiability for sensitivity analysis and ongoing work on including machine-learning-based parametrizations and coupling SpeedyWeather.jl to other Earth system component models. \n\n**Authors** \n\nMaximilian Gelbrecht (1,2), Milan Kl\u00f6wer (3), and SpeedyWeather.jl contributors*\n\n1. Potsdam Institute for Climate Impact Research, Germany\n2. Technical University of Munich, Germany\n3. University of Oxford, UK\n\n*SpeedyWeather.jl is a community project with valuable contributions from a large group of contributors, we are sorry that we can\u2019t list everyone here by name", "recording_license": "", "do_not_record": false, "persons": [{"code": "FRX3ZM", "name": "Maximilian Gelbrecht", "avatar": null, "biography": "Researching differentiable programming and machine learning for Earth system models and dynamical systems", "public_name": "Maximilian Gelbrecht", "guid": "73255bbe-2284-5620-b0a1-2c3519a50514", "url": "https://pretalx.com/juliacon-2026/speaker/FRX3ZM/"}, {"code": "A9SQSW", "name": "Milan Kl\u00f6wer", "avatar": "https://pretalx.com/media/avatars/A9SQSW_nKoqOOA.webp", "biography": "Milan Kl\u00f6wer is a NERC Independent Research Fellow at the University of Oxford. He did his postdoc at the Massachusetts Institute of Technology (MIT) working on climate model development in Julia. He started SpeedyWeather.jl, a global atmospheric model designed as a research playground to develop prototype ideas on machine-learned representations of climate processes and computationally efficient climate models. He also works on low precision computing, data compression and information theory, predictability of weather and climate, and software engineering.", "public_name": "Milan Kl\u00f6wer", "guid": "da34690e-e394-5c01-a2f8-6876c357d29c", "url": "https://pretalx.com/juliacon-2026/speaker/A9SQSW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/GB8WXW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/GB8WXW/", "attachments": []}, {"guid": "38899d55-2863-5e82-a63d-5326277da790", "code": "D8AMAJ", "id": 90426, "logo": "https://pretalx.com/media/juliacon-2026/submissions/D8AMAJ/image_FG9KQz1.webp", "date": "2026-08-12T15:50:00+02:00", "start": "15:50", "end": "2026-08-12T16:00:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-90426-pagos-jl-play-ice-sheet-modelling-like-it-s-lego", "url": "https://pretalx.com/juliacon-2026/talk/D8AMAJ/", "title": "Pagos.jl - play ice-sheet modelling like it\u2019s Lego", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "The Antarctic and Greenland Ice Sheets have experienced significant volume loss over the last decades, with a contribution of 0.1 m to sea-level rise that is bound to become significantly larger over the coming millennia. Under high-emission scenarios, they could contribute by as much as 18 mSLE by the year 3000, with dramatic consequences for coastal livelihood. Ice-sheet models are the central tool to produce such projections but generally present many limitations: they are not easily extensible by the user, they are difficult to couple to other Earth System model components, they lack interactivity and easy visualisation, they are sparsely documented, and they are often incompatible with modern software and hardware advancements, like automatic differentiation and GPU computing. To address this, we develop Pagos.jl, a continental ice-sheet model written in Julia that follows the philosophy paved by Oceananigans.jl and SpeedyWeather.jl: running simulations should be as easy and fun as playing Lego. Besides reducing the time to first plot compared to traditional models, this also offers a privileged framework to develop and test new physics and parameterisations. The user-friendliness of Pagos.jl allows a simple coupling to other Earth System model components and goes hand in hand with its computational efficiency. This allows the user to run simulations at the continental scale with resolutions of a few kilometres and address important scientific questions around grounding-line dynamics.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "H3ZQVM", "name": "Jan Swierczek-Jereczek", "avatar": "https://pretalx.com/media/avatars/H3ZQVM_SYgguku.webp", "biography": "I study the stability of ice sheets in the past, present and future, focusing on their interaction with the solid Earth and the sea level. To this end, I use and develop numerical models of ice-sheet evolution and glacial isostatic adjustment.", "public_name": "Jan Swierczek-Jereczek", "guid": "ebbc0b63-ccfd-5be7-86cf-127329b8a7e9", "url": "https://pretalx.com/juliacon-2026/speaker/H3ZQVM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/D8AMAJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/D8AMAJ/", "attachments": []}, {"guid": "b1dbb0b1-faca-5c61-b747-910472962e85", "code": "UETBSG", "id": 92845, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UETBSG/image_Mt0eKNC.webp", "date": "2026-08-12T16:00:00+02:00", "start": "16:00", "end": "2026-08-12T16:10:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92845-terrarium-jl-fully-differentiable-and-gpu-accelerated-land-modeling-at-all-scales-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/UETBSG/", "title": "Terrarium.jl: Fully differentiable and GPU-accelerated land modeling at all scales in Julia", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "Global land surface and hydrological models are crucial components of Earth System Models (ESMs). In addition to providing realistic boundary conditions for the atmosphere and ocean components, they also play a key role in understanding Earth\u2019s changing energy imbalance and the response of the terrestrial carbon and water cycles to anthropogenic climate change. Unlike atmosphere and ocean models, however, land models lack a fluid dynamical core and rely heavily on empirical parameterizations to represent many key processes. As such, there is a continued need for a new generation of land models which can facilitate the incorporation of data-driven components. Here we present Terrarium.jl, a Julia-based land modeling framework for GPU-accelerated and automatically differentiable simulations of soil, snow, and vegetation dynamics, along with their corresponding land-atmosphere exchange fluxes. We highlight how Julia\u2019s key features enable unprecedented modularity in the model design and seamless GPU parallelization through KernelAbstractions.jl. We further demonstrate the value of GPU acceleration and differentiability through a series of performance benchmarks and sensitivity analyses. We also detail our initial experiments in achieving stable coupling to a reduced-complexity atmosphere model, SpeedyWeather.jl.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "KCHMD8", "name": "Brian Groenke", "avatar": "https://pretalx.com/media/avatars/KCHMD8_GHddQ9c.webp", "biography": "I am a postdoctoral researcher at the Potsdam Institute for Climate Impact Research in Potsdam, Germany. My primary research interests are in applications of differentiable and probabilistic programming, uncertainty quantification, and scientific machine learning to geophysical modeling of Earth systems.\n\nIn my PhD, I worked on probabilistic inverse modeling of subsurface heat transfer in terrestrial permafrost. Prior to that, I worked on the application generative deep learning to statistical downscaling of climate and weather variables from coarse scale model outputs.\n\nMy industry background consists primarily of software engineering, both front-end and back-end development, with a wide range of frameworks and languages.", "public_name": "Brian Groenke", "guid": "69b61b03-ee37-57b3-b183-914b8eb4f18b", "url": "https://pretalx.com/juliacon-2026/speaker/KCHMD8/"}, {"code": "FRX3ZM", "name": "Maximilian Gelbrecht", "avatar": null, "biography": "Researching differentiable programming and machine learning for Earth system models and dynamical systems", "public_name": "Maximilian Gelbrecht", "guid": "73255bbe-2284-5620-b0a1-2c3519a50514", "url": "https://pretalx.com/juliacon-2026/speaker/FRX3ZM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UETBSG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UETBSG/", "attachments": []}, {"guid": "63bc9c67-a45c-5aec-a400-600a1a0b2f21", "code": "V337P8", "id": 92633, "logo": "https://pretalx.com/media/juliacon-2026/submissions/V337P8/image_MwtMunb.webp", "date": "2026-08-12T16:10:00+02:00", "start": "16:10", "end": "2026-08-12T16:20:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92633-trixiatmo-jl-advanced-numerical-schemes-for-atmospheric-flows", "url": "https://pretalx.com/juliacon-2026/talk/V337P8/", "title": "TrixiAtmo.jl: Advanced numerical schemes for atmospheric flows", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "`TrixiAtmo.jl` is there: a `Trixi.jl` spin-off, bringing Discontinuous Galerkin methods and Adaptive Mesh Refinement to Earth system modeling. We aim at kilometer-scale resolutions to resolve key physical processes and address historical stability concerns using entropy-conserving split-forms. Currently, we are on our way to extend `TrixiAtmo.jl` to handle realistic applications, and integrate with Julia's rich geoscience ecosystem.", "description": "We have recently released `TrixiAtmo.jl`, a specialized extension of `Trixi.jl` that brings state-of-the-art numerical methods, drastically increased resolution, and high physical fidelity to Earth system models.\n\nCurrent global climate models typically operate at horizontal resolutions of tens of kilometers. Increasing this resolution towards the kilometer scale would enable a substantially improved representation of key physical and chemical processes, which are currently treated by parametrizations and constitute a major source of uncertainty.\n\nWe address this challenge with two tightly coupled methodological advances: Discontinuous Galerkin (DG) methods and dynamic Adaptive Mesh Refinement (AMR). DG, as a high order method, offers high accuracy while maintaining a low memory footprint, thereby reaching unmatched efficiency. Historically, high-order methods have been susceptible to numerical instabilities and have struggled with unresolved physical processes like turbulence. However, recent advancements, particularly the development of entropy-conserving schemes, have significantly improved this situation. By incorporating a physics-based concept of stability, spurious disturbances in the numerical solution are effectively suppressed.  Simultaneously, AMR allows us to concentrate computational effort in regions of interest, such as sharp gradients in prognostic quantities or chemically active zones. Consequently, overall computational and storage costs are drastically reduced, and simulations governed by local and regional processes achieve substantial gains in efficiency. Despite their undisputed potential, both DG and AMR, have seen limited adoption in global atmospheric and chemistry\u2013climate simulations to date. \n\nWith `TrixiAtmo.jl`, we tailor `Trixi.jl`'s numerical schemes to the requirements of atmospheric dynamical cores. So far, we have implemented the compressible Euler equations, including moist air and rain, on hex-based cubed sphere grids, and the shallow water equations on prism-based icosahedral grids, where the latter closely resembles the `ICON` model setup. We have added well-balanced, and entropy-conserving and dissipating split-form schemes. For idealized atmospheric flows, such as the barotropic and baroclinic instability, we observe stable simulations even on coarse meshes.\n\nWe now aim to extend `TrixiAtmo.jl` towards more realistic applications to put our methods to  a compelling test. Among other tasks, this requires flexible import and remapping methods of reanalysis-based input data and easy to use analysis and visualization work flows for the resulting data. Julia already boasts an exceptionally rich geoscience community and software stack, which we plan to leverage. Several projects such as `CliMA`, `SpeedyWeather.jl`, and `Breeze.jl`, to name but a few, have already demonstrated Julia's viability for high-performance computing in the Earth and climate sciences.", "recording_license": "", "do_not_record": false, "persons": [{"code": "LCQUFJ", "name": "Benedict Geihe", "avatar": null, "biography": "Studies in Mathematics and Computer Science, University of Bonn, Germany\nDissertation, Institute for Numerical Simulation, University of Bonn, Germany\nResearch Assistant, Institute of Propulsion Technology, German Aerospace Center (DLR)\nPostdoctoral Researcher, Division of Mathematics, University of Cologne, Germany", "public_name": "Benedict Geihe", "guid": "018b578d-e6d7-5743-90a8-b3307dabc261", "url": "https://pretalx.com/juliacon-2026/speaker/LCQUFJ/"}], "links": [{"title": "TrixiAtmo.jl", "url": "https://github.com/trixi-framework/TrixiAtmo.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/V337P8/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/V337P8/", "attachments": []}, {"guid": "3b58d0f3-027f-5bdd-ab1e-deebc8960701", "code": "QSV7XN", "id": 92788, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QSV7XN/image_yDXIJ9o.webp", "date": "2026-08-12T16:20:00+02:00", "start": "16:20", "end": "2026-08-12T16:30:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92788-strategies-to-integrate-data-and-biogeochemical-models-sindbad-julia-framework-for-terrestrial-ecosystem-model-data-integration", "url": "https://pretalx.com/juliacon-2026/talk/QSV7XN/", "title": "Strategies to Integrate Data and Biogeochemical Models: SINDBAD Julia Framework for Terrestrial Ecosystem Model Data Integration", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "The SINDBAD framework, with Sindbad.jl, SindbadTEM, OmniTools.jl, TimeSamplers.jl, and ErrorMetrics.jl packages offers a user\u2011friendly, Julia\u2011based system for terrestrial model\u2013data integration. It enables scalable, differentiable experiments across spatial and temporal scales, supporting next\u2011generation understanding of vegetation\u2013water\u2013carbon interactions.", "description": "The Julia ecosystem continues to expand with powerful, composable tools for scientific computing, data analysis, and simulation. In this talk, we introduce the Strategies to Integrate Data and Biogeochemical Models (SINDBAD) model\u2013data\u2011integration framework, which comprises five complementary packages that together form a lightweight but versatile foundation for the terrestrial ecosystem modeling community. The packages are intentionally designed to be user\u2011friendly for low\u2011tech, high Earth\u2011system domain expertise, while fully leveraging the computational excellence of the Julia programming language. This lowers the barrier of entry for the next generation of Earth\u2011system modellers.\n\nSindbad.jl provides the umbrella framework for building and executing terrestrial ecosystem modeling experiments. It emphasizes modularity, reproducibility, and clarity, enabling users to carry out scientific analyses across spatial and temporal scales. SindbadTEM implements the core formulations for major ecosystem processes of the water and carbon cycles, and can be independently integrated into other Earth\u2011system modeling systems.\n\nOmniTools.jl complements this by offering a curated collection of general\u2011purpose utilities ranging from filesystem helpers to data\u2011structure conveniences and helper functions, that extend beyond modeling applications. TimeSamplers.jl implements allocation\u2011free resampling of N\u2011dimensional arrays using a date\u2011indexed view. ErrorMetrics.jl provides a small but robust and extensible set of performance and accuracy metrics commonly used in model\u2013data\u2011integration approaches.\n\nTogether, this set of SINDBAD packages enables the construction of modeling experiments that span spatial and temporal scales, especially benefiting from full differentiability enabled by Julia. Traditionally, such models have been limited to narrow ranges of scales; SINDBAD in Julia helps overcome this constraint.\n\nWe use this opportunity to demonstrate how SINDBAD can be applied to understand interactions among vegetation, water, and carbon cycle processes across scales. To do so, we create different realizations of the framework with varying levels of process complexity and coupling. These realizations are parameterized with different assumptions that lead to distinct model formulations and responses, each constrained by observational data appropriate to its scale:\n\n- a global scale model focused on vegetation\u2019s structural influence on the water cycle;\n- a regional scale model with physiological coupling of water and carbon cycles, emphasizing interannual variability of vegetation fraction;\n- an ecosystem scale model with a prognostic carbon cycle that allows additional constraints from Earth\u2011observation data;\n- a hybrid machine\u2011learning\u2013physically\u2011based modeling approach toward a global parameterization that links local ecosystem properties to global parameter fields using neural\u2011network\u2011based prediction of spatial parameter variability in an end\u2011to\u2011end learning system.\n\nAt the global scale, we find that incorporating observation\u2011based vegetation indices into a simple hydrological model improves simulations of monthly runoff and terrestrial water storage variations. At the regional scale, using vegetation\u2011fraction data from geostationary satellites in a photosynthetically coupled water\u2013carbon model significantly improves simulations of gross primary productivity variability. At the ecosystem scale, a model linking C\u2013H\u2082O fluxes and states, by prognostically coupling primary productivity, transpiration, and root allocation, benefits further from remote\u2011sensing observations of carbon states, even beyond eddy\u2011covariance constraints. These examples demonstrate that, with appropriate observational constraints, an across\u2011scale approach supports hypothesis testing for terrestrial C\u2013H\u2082O processes.\n\nHowever, direct comparisons across scales reveal that model\u2013observation discrepancies at a given scale are often quantitatively comparable to differences among observational products themselves. To address this, we implement the hybrid modeling experiment and show that such workflows perform comparably to in\u2011situ parameter inversions, though their ability to generalize parameters remains limited, in particular for ecosystem processes with sparse observational constraints.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NFSFMD", "name": "Sujan Koirala", "avatar": "https://pretalx.com/media/avatars/3TNCSQ_ygzBcm4.webp", "biography": "Dr. Sujan Koirala is a Research Project Group Leader at the Max Planck Institute for Biogeochemistry in Jena, where he leads work on data\u2011driven and process\u2011based modeling of the global carbon and water cycles. His research spans machine learning, terrestrial ecosystem modeling, climate extremes, groundwater resources, and large\u2011scale hydrology. He leads the development of the SINDBAD model\u2013data\u2011integration framework and contributes to major community efforts such as FLUXCOM and ESMValTool. Dr. Koirala holds a PhD in Civil Engineering from The University of Tokyo and has extensive experience in high\u2011performance computing, scientific software development, and mentoring early\u2011career researchers.", "public_name": "Sujan Koirala", "guid": "46f9179c-091c-54d9-83f2-b28f5d4cb0e4", "url": "https://pretalx.com/juliacon-2026/speaker/NFSFMD/"}, {"code": "LW7UQZ", "name": "Fabian Gans", "avatar": null, "biography": "I am a physicist by training and am currently studying Global Biogeochemical Cycles in the Earth System using Remote Sensing, Meteorological and other data sets based at the Max-Planck-Institute for Biogeochemistry, Jena, Germany.\nMy first commit to my first Julia package dates back to the year 2012 and since then I have authored and contributed to packages in the Julia Geodata and processing ecosystem, examples are NetCDF.jl, Zarr.jl, DiskArrays.jl, YAXArrays.jl EarthDataLab.jl and others. Some may know me under my github tag @meggart", "public_name": "Fabian Gans", "guid": "8d983e03-6d93-5fd0-9ae5-1c22a78be090", "url": "https://pretalx.com/juliacon-2026/speaker/LW7UQZ/"}, {"code": "RWMFJM", "name": "Felix Cremer", "avatar": null, "biography": null, "public_name": "Felix Cremer", "guid": "6917a484-c35b-5c54-8572-a5b5019dfec7", "url": "https://pretalx.com/juliacon-2026/speaker/RWMFJM/"}, {"code": "WWPN8K", "name": "Lazaro Alonso", "avatar": "https://pretalx.com/media/avatars/WWPN8K_vPFugHw.webp", "biography": "A scientist at the Max Planck Institute for Biogeochemistry, advancing earth system models through hybrid modeling, integrating process-based models with machine learning. Through open, reproducible research and compelling visualizations, I bridge the gap between cutting-edge research and societal impact.", "public_name": "Lazaro Alonso", "guid": "88694f5b-0ceb-5087-9819-cf98bfdac2b0", "url": "https://pretalx.com/juliacon-2026/speaker/WWPN8K/"}, {"code": "VMNM98", "name": "Nuno Carvalhais", "avatar": null, "biography": "Scientist/Leader [Model-Data Integration]([url](https://www.bgc-jena.mpg.de/en/bgi/mdi)) Group at the[ MPI-BGC]([url](https://www.bgc-jena.mpg.de/en)). PhD in Env. Sci. Eng.", "public_name": "Nuno Carvalhais", "guid": "4f1e23c3-5dbb-59cd-9c50-14c677247e13", "url": "https://pretalx.com/juliacon-2026/speaker/VMNM98/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QSV7XN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QSV7XN/", "attachments": [{"title": "flyer-QSV7XN", "url": "/media/juliacon-2026/submissions/QSV7XN/resources/QSV7XN_4hWoY1R.png", "type": "related"}]}, {"guid": "c5af1647-bbfd-57a7-a79b-450f8088c146", "code": "AFFXGE", "id": 93334, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AFFXGE/image_78bUETj.webp", "date": "2026-08-12T16:30:00+02:00", "start": "16:30", "end": "2026-08-12T16:40:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93334-multi-physics-geophysical-flow-simulations-using-justrelax-jl", "url": "https://pretalx.com/juliacon-2026/talk/AFFXGE/", "title": "Multi-physics geophysical flow simulations using JustRelax.jl", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "[JustRelax.jl](https://github.com/PTsolvers/JustRelax.jl) ([de Montserrat et al., (2026)](https://joss.theoj.org/papers/10.21105/joss.09365))  is an open-source, highly portable, and high-performance package designed for geodynamic modeling. It employs the Accelerated Pseudo-Transient (APT) method to solve the Stokes and diffusion equations, making it well-suited to exploit Graphics Processing Units (GPUs). It incorporates a wide range of features critical to computational geodynamics, including complex and highly non-linear rheologies, free surface, and a particle-in-cell method to advect material information.", "description": "Simulating the solid Earth's thermo-mechanical evolution requires solving coupled, non-linear Stokes and heat-diffusion problems across large domains with sharp material contrasts,complex nonlinear visco-elasto-plastic rheologies, and long time scales. Traditionally, these simulations run on High-Performance Computing (HPC) machines due to their substantial memory and computational demands. As HPC centres transition away from CPU-only architectures toward GPU-accelerated systems, large legacy codes written originally for CPU architectures face extensive rewriting and optimisation efforts.\n\nHere, we present JustRelax.jl, an open-source, highly portable Julia package for geodynamic modelling. It employs the matrix-free Accelerated Pseudo-Transient (APT) method to solve the Stokes and heat-diffusion equations, making it well-suited to exploit modern GPU hardware while remaining fully functional on CPUs.  JustRelax.jl exposes a high-level API while remaining highly modular, building upon a suite of packages developed within the (\u2202)[GPU4GEO project](https://gpu4geo.org). GeoParams.jl handles solver-agnostic rheology calculations, while JustPIC.jl manages advection via a multi-XPU Particles-in-Cell scheme specialised for staggered grids. Backend portability is achieved through ParallelStencil.jl's architecture-agnostic kernel abstractions, and distributed-memory communication is managed by ImplicitGlobalGrid.jl via MPI. JustRelax.jl demonstrates near-perfect algorithmic weak scaling in both 2D and 3D up to 512 GPUs, enabling us to perform large-scale geodynamic simulations on modern HPC machines.  \n\nRecently, the code has been utilised for a domain specific benchmark of plastic shear band localization using 64 Nvidia Grace-Hopper GPUs achieving a global resolution of 41'000 x 41'000. This resolution opens the door to simulate a lithospheric scale geodynamic model at the metre scale. \n\nReference\nde Montserrat et al., (2026). JustRelax.jl: A Julia package for geodynamic modeling with matrix-free solvers. Journal of Open Source Software, 11(118), 9365, https://doi.org/10.21105/joss.09365", "recording_license": "", "do_not_record": false, "persons": [{"code": "8VF9S8", "name": "Pascal Aellig", "avatar": "https://pretalx.com/media/avatars/DERQMZ_gyqrCD3.webp", "biography": "Pascal Aellig is a PhD student in computational geosciences at the Johannes Gutenberg University Mainz. He co-develops JustRelax.jl and other packages in the framework of geodynamics with the focus on the evolution of magmatic systems of various scales.", "public_name": "Pascal Aellig", "guid": "4d93d4b5-b815-5319-8a2d-8b4a6d35643a", "url": "https://pretalx.com/juliacon-2026/speaker/8VF9S8/"}, {"code": "LJYNTP", "name": "Christian", "avatar": null, "biography": "I am a Computational Geoscientist at the University of Lausanne developing scalable high-performance solvers for geodynamic modelling. My previous work explored the dynamics of interacting subduction zones and associated plate motion in the Mediterranean, while contributing to the development of computational tools for geodynamic applications using automatic differentiation and Julia.", "public_name": "Christian", "guid": "9b9e2509-d74a-5bf0-abbd-6add01dcb945", "url": "https://pretalx.com/juliacon-2026/speaker/LJYNTP/"}, {"code": "ALZZZK", "name": "Albert de Montserrat Navarro", "avatar": null, "biography": null, "public_name": "Albert de Montserrat Navarro", "guid": "2ddee126-7e2b-5b47-beba-808ac420fd79", "url": "https://pretalx.com/juliacon-2026/speaker/ALZZZK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AFFXGE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AFFXGE/", "attachments": []}, {"guid": "2028d867-70b5-5011-8f8f-960dda8f99e7", "code": "DTEEQC", "id": 92606, "logo": "https://pretalx.com/media/juliacon-2026/submissions/DTEEQC/image_AcI5RD8.webp", "date": "2026-08-12T16:40:00+02:00", "start": "16:40", "end": "2026-08-12T16:50:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92606-hybrid-flux-partitioning-in-julia-learning-temperature-sensitivity-of-ecosystem-respiration-with-easyhybrid-jl", "url": "https://pretalx.com/juliacon-2026/talk/DTEEQC/", "title": "Hybrid Flux Partitioning in Julia: Learning Temperature Sensitivity of Ecosystem Respiration with EasyHybrid.jl", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "Scientific modeling often forces a choice between flexible but opaque neural networks and interpretable process-based models that can be too rigid for real-world data. Hybrid modeling bridges this gap by combining mechanistic structure with machine-learning flexibility. In this talk we introduce EasyHybrid.jl, a user-friendly Julia package that makes hybrid modeling accessible across disciplines. We demonstrate the approach on a concrete problem: partitioning eddy-covariance net carbon fluxes into photosynthesis (a CO\u2082 sink) and ecosystem respiration (a CO\u2082 source), while estimating how strongly respiration responds to temperature. Temperature sensitivity is summarized by Q10, the factor by which respiration changes for a 10 K warming (e.g., Q10 = 2 means doubles per 10 K). We present cross-site results across hundreds of FLUXNET eddy covariance towers and show that, even when inferred jointly with hybrid flux partitioning, the learned temperature sensitivity exhibits a relatively narrow convergence across ecosystems.", "description": "The talk shows how to use EasyHybrid.jl to define a process-based model, turn it into a hybrid model, and train it end-to-end on eddy-covariance data. We start from typical tabular data (meteorology and fluxes) and show how EasyHybrid.jl connects predictors, mechanistic forcings, and targets using named variables, while relying on Lux.jl for the neural components.\nWe then build a hybrid flux-partitioning model in which ecosystem respiration follows the mechanistic Q10 temperature response, but the base respiration term and radiation-use efficiency for photosynthesis are learned as functions of environmental drivers. The key design choice is explicit: which parameters are learned globally (one constant temperature-sensitivity parameter Q10), which are fixed, and which vary in time and/or space through neural networks. Training uses standard optimizers such as Adam or RMSProp.\nIn an outlook we explore an LSTM-based variant of the hybrid model. Instead of predicting base respiration from instantaneous drivers only, the LSTM takes sequences of past environmental drivers to test whether ecosystem memory changes flux partitioning and the inferred temperature sensitivity.\n\nThe research presented in this talk is the result of joint efforts by L\u00e1zaro Alonso, Kilian Hochholzer, Laura van der Poel, Lukas Schirren, Andr\u00e9s Tangarife-Escobar, and Ritesh Moon. EasyHybrid.jl was started by Markus Reichstein. We thank Jake Nelson and the FLUXCOM team for providing access to curated FLUXNET data.", "recording_license": "", "do_not_record": false, "persons": [{"code": "U3WR8C", "name": "Bernhard Ahrens", "avatar": null, "biography": "Bernhard Ahrens leads the \u201c[Modeling Interactions in Soil Systems](https://www.bgc-jena.mpg.de/en/bgi/miss)\u201d group at the Max Planck Institute for Biogeochemistry in Jena and works in the Biogeochemical Integration Department under Prof. Dr. Markus Reichstein. He is a geoecologist and global change ecologist working at the interface of process-based soil organic matter modeling, data integration, and machine learning. Methodologically, he codevelops the software package [EasyHybrid.jl](https://github.com/EarthyScience/EasyHybrid.jl) to embed neural networks into process-based models. He (co-)developed the soil organic matter turnover models COMISSION v1.0 and v2.0 as well as the Jena Soil Model. He supervises several PhD and postdoctoral projects, including within the [AI4SoilHealth ](https://ai4soilhealth.eu/) and WETSCAPES2.0 consortia.", "public_name": "Bernhard Ahrens", "guid": "2cc92da5-09ac-5c30-be51-69aed0003867", "url": "https://pretalx.com/juliacon-2026/speaker/U3WR8C/"}, {"code": "LYTKXG", "name": "RITESH MOON", "avatar": "https://pretalx.com/media/avatars/H7VW3R_2VEpiF9.webp", "biography": "Ritesh is a PhD researcher working at the intersection of hydrology and machine learning, developing physically informed machine learning (PIML) frameworks that integrate process-based hydrological models with deep learning to improve streamflow prediction in complex and regulated catchments. Using large-sample datasets such as CAMELS-GB, he incorporates hydrological signatures and process-based insights to enhance both predictive accuracy and model interpretability. His work focuses on bridging physics-based understanding with modern AI to build robust, scalable, and transparent tools for water resource management.", "public_name": "RITESH MOON", "guid": "3703929c-96a2-57fa-9a50-aa8f048db404", "url": "https://pretalx.com/juliacon-2026/speaker/LYTKXG/"}, {"code": "WWPN8K", "name": "Lazaro Alonso", "avatar": "https://pretalx.com/media/avatars/WWPN8K_vPFugHw.webp", "biography": "A scientist at the Max Planck Institute for Biogeochemistry, advancing earth system models through hybrid modeling, integrating process-based models with machine learning. Through open, reproducible research and compelling visualizations, I bridge the gap between cutting-edge research and societal impact.", "public_name": "Lazaro Alonso", "guid": "88694f5b-0ceb-5087-9819-cf98bfdac2b0", "url": "https://pretalx.com/juliacon-2026/speaker/WWPN8K/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/DTEEQC/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/DTEEQC/", "attachments": []}, {"guid": "53938865-031b-5643-bc7f-6aca7d5edc7f", "code": "Z8Y8GP", "id": 91954, "logo": "https://pretalx.com/media/juliacon-2026/submissions/Z8Y8GP/image_vtetleg.webp", "date": "2026-08-12T16:50:00+02:00", "start": "16:50", "end": "2026-08-12T17:00:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-91954-movingboundaryminerals-jl-modelling-diffusion-limited-growth-in-diffusion-couples", "url": "https://pretalx.com/juliacon-2026/talk/Z8Y8GP/", "title": "MovingBoundaryMinerals.jl: Modelling diffusion-limited growth in diffusion couples", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "[MovingBoundaryMinerals.jl](https://github.com/AnStroh/MovingBoundaryMinerals.jl) ([Stroh et al., 2025](https://gmd.copernicus.org/articles/18/10203/2025/)) is a Finite Element package to model diffusion-limited growth. The package combines grid refinement together with a moving boundary in order to resolve sharp gradients next to the interface of growing crystals. The moving boundary is treated via a thermodynamically-consistent diffusion solver. We provide benchmarks and examples that can be used in petrology and materials science.", "description": "Compositional concentration profiles across individual crystals or diffusion couples are largely determined by diffusion and growth processes. These two processes are particularly important during the formation of high-temperature rocks such as igneous and metamorphic rocks. The numerical simulation of concentration profiles in crystals is a widely used technique in various fields such as geospeedometry or diffusion chronometry. Compared to single crystals, coupled diffusion pairs yield tighter constraints on the experienced temperature and pressure ranges and thus provide additional information for our models. However, the numerical description of concentration profiles within diffusion couples is challenging due to the sharp compositional gradients. Discontinuities in concentration, which are related to the different mineral properties, commonly occur at the interface of two minerals and lead to technical implementation issues.\n\nTo address these issues, we have developed the Finite Element (FE) package [MovingBoundaryMinerals.jl](https://github.com/AnStroh/MovingBoundaryMinerals.jl) ([Stroh et al., 2025](https://gmd.copernicus.org/articles/18/10203/2025/)) that can calculate the evolution of concentration profiles in diffusion couples with moving interfaces. Within this package, we consider diffusion-limited growth. Grid refinement allows to resolve changes near the interface with a higher resolution. An adaptive grid enables the accurate reproduction of rapid concentration changes and discontinuities. Our code can be applied to various examples of single crystals or diffusion couples, integrating any combination of growth, diffusion, and temperature dependency. Additionally, it is possible to calculate concentration profiles based on the thermodynamically-constrained, Stefan-Interface condition. Our code is benchmarked against analytical solutions for limiting cases. Results from our models can be used in petrology and geodynamic applications to provide tighter constraints concerning the pressure and temperature evolution of magmatic and metamorphic mineral assemblages.\n\nReference\nStroh, A., Aellig, P. S., and Moulas, E.: Numerical modelling of diffusion-limited mineral growth for geospeedometry applications, EGUsphere, 1\u201337, https://doi.org/10.5194/egusphere-2025-2511, 2025.", "recording_license": "", "do_not_record": true, "persons": [{"code": "LNVT8P", "name": "Annalena Stroh", "avatar": null, "biography": "Annalena Stroh is a PhD student in the Metamorphic Processes group (Geosciences) at JGU Mainz. Her research focuses on evaluating timescales in crystal growth and diffusion processes using numerical models.", "public_name": "Annalena Stroh", "guid": "f330227b-bc13-508e-b242-b88e1b93184a", "url": "https://pretalx.com/juliacon-2026/speaker/LNVT8P/"}, {"code": "8VF9S8", "name": "Pascal Aellig", "avatar": "https://pretalx.com/media/avatars/DERQMZ_gyqrCD3.webp", "biography": "Pascal Aellig is a PhD student in computational geosciences at the Johannes Gutenberg University Mainz. He co-develops JustRelax.jl and other packages in the framework of geodynamics with the focus on the evolution of magmatic systems of various scales.", "public_name": "Pascal Aellig", "guid": "4d93d4b5-b815-5319-8a2d-8b4a6d35643a", "url": "https://pretalx.com/juliacon-2026/speaker/8VF9S8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/Z8Y8GP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/Z8Y8GP/", "attachments": []}, {"guid": "44b0b3f8-6cba-556a-a264-2cdb725c575b", "code": "U9ZWXZ", "id": 92837, "logo": "https://pretalx.com/media/juliacon-2026/submissions/U9ZWXZ/image_oun3O91.webp", "date": "2026-08-12T17:00:00+02:00", "start": "17:00", "end": "2026-08-12T17:10:00+02:00", "duration": "00:10", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92837-a-learned-surface-roughness-scheme-for-climate-prediction-in-speedyweather-jl", "url": "https://pretalx.com/juliacon-2026/talk/U9ZWXZ/", "title": "A learned surface roughness scheme for climate prediction in SpeedyWeather.jl", "subtitle": "", "track": "Earth system science in Julia", "type": "Short talk", "language": "en", "abstract": "Hybrid climate modelling combines numerical models with machine-learned components. We present the development of multiple machine-learned surface climate processes and their integration into the climate model SpeedyWeather.jl using PyTorch and Lux.jl. Despite the offline training, the hybrid model is designed to generalise in space and to different climates. We address speed vs. accuracy tradeoffs using SymbolicRegression.jl and discuss online learning with Enzyme.jl.", "description": "In weather and climate models, momentum, heat, humidity and tracer fluxes between the Earth\u2019s surface and atmosphere strongly depend on surface roughness. The roughness length depends on space and time-dependent surface properties over ocean, sea-ice and land. For example, surface winds impact wave height over sea-ice free oceans; vegetation and orography determine roughness length over land, where its effect on near-surface turbulence strongly impacts the surface fluxes. Here, we present a set of machine learning models trained on reanalysis data to predict surface roughness over both land and ocean grid cells in SpeedyWeather, a Julia-based climate model. More accurately representing the surface roughness has been shown to significantly improve model bias against observations over a range of variables such as surface air temperatures and near-surface wind speed. We explore the downstream impacts of using this parameterisation in the climate model, and test the generalisability of an offline-learned surface roughness scheme in future climates with reduced sea ice and land-use change. Spatial generalisation is implemented through surface roughness being a function of local variables only. We discuss efficient inference on CPU and GPU for every grid cell on each integration time-step, and use so-called model distillation via SymbolicRegression.jl which minimises the trade-off between speed versus accuracy. Neural networks are trained offline using PyTorch, but loaded in SpeedyWeather as Lux.jl networks, with opportunities to use the Reactant compiler for rapid inference. Separating the land and ocean surface roughness parameterisations, we use XAI techniques to interpret what the models have learned and evaluate their performance against baseline model types. We generally propose machine-learned schemes of individual climate processes towards interpretable, data-driven climate modelling.\n\nAuthors: Gregory Munday (1), Laura Mansfield (1), Maximilian Gelbrecht (2, 3), Niklas Viebig (1, 4), and Milan Kl\u00f6wer (1)\n\n1. University of Oxford, UK\n2. Potsdam Institute for Climate Impact Research, Germany\n3. Technical University of Munich, Germany\n4. ETH Z\u00fcrich, Switzerland", "recording_license": "", "do_not_record": false, "persons": [{"code": "3HQE7H", "name": "Greg Munday", "avatar": "https://pretalx.com/media/avatars/WTKEQU_VoAJ6Yz.webp", "biography": "DPhil Research Student at the University of Oxford. Interested in all things hybrid climate modelling.", "public_name": "Greg Munday", "guid": "08798aac-997c-5346-b964-4cc6dc6aac6b", "url": "https://pretalx.com/juliacon-2026/speaker/3HQE7H/"}, {"code": "FRX3ZM", "name": "Maximilian Gelbrecht", "avatar": null, "biography": "Researching differentiable programming and machine learning for Earth system models and dynamical systems", "public_name": "Maximilian Gelbrecht", "guid": "73255bbe-2284-5620-b0a1-2c3519a50514", "url": "https://pretalx.com/juliacon-2026/speaker/FRX3ZM/"}, {"code": "A9SQSW", "name": "Milan Kl\u00f6wer", "avatar": "https://pretalx.com/media/avatars/A9SQSW_nKoqOOA.webp", "biography": "Milan Kl\u00f6wer is a NERC Independent Research Fellow at the University of Oxford. He did his postdoc at the Massachusetts Institute of Technology (MIT) working on climate model development in Julia. He started SpeedyWeather.jl, a global atmospheric model designed as a research playground to develop prototype ideas on machine-learned representations of climate processes and computationally efficient climate models. He also works on low precision computing, data compression and information theory, predictability of weather and climate, and software engineering.", "public_name": "Milan Kl\u00f6wer", "guid": "da34690e-e394-5c01-a2f8-6876c357d29c", "url": "https://pretalx.com/juliacon-2026/speaker/A9SQSW/"}, {"code": "UWLWET", "name": "Niklas Viebig", "avatar": "https://pretalx.com/media/avatars/787YWJ_j6AIbZs.webp", "biography": "Master\u2019s student in Physics at ETH Zurich, currently completing my Master\u2019s thesis in the [climate modeling group ](https://climate-modelling.github.io)at AOPP, University of Oxford. Im researching differentiable programming and systematic parameter calibration for Earth system models, with interests in exoplanet climates, high-performance computing, and scientific software engineering.", "public_name": "Niklas Viebig", "guid": "6da790fe-d52e-57e6-9775-5fda4d6081c0", "url": "https://pretalx.com/juliacon-2026/speaker/UWLWET/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/U9ZWXZ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/U9ZWXZ/", "attachments": [{"title": "flyer-U9ZWXZ", "url": "/media/juliacon-2026/submissions/U9ZWXZ/resources/U9ZWXZ_WD3uM8T.png", "type": "related"}]}], "Alte Mensa \u2014 Audi Max": [{"guid": "e6484ec5-1a3c-5f5e-8db0-0a78734f4de2", "code": "WDZ3AN", "id": 93316, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WDZ3AN/image_gNir7lN.webp", "date": "2026-08-12T10:00:00+02:00", "start": "10:00", "end": "2026-08-12T10:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93316-amplitude-analysis-of-exotic-multiquark-states-with-julia-in-the-lhcb-experiment", "url": "https://pretalx.com/juliacon-2026/talk/WDZ3AN/", "title": "Amplitude Analysis of Exotic Multiquark States with Julia in the LHCb Experiment", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Short talk", "language": "en", "abstract": "The spectroscopy of exotic hadrons enables probing the dynamics of the strong interaction beyond the conventional quark\u2013antiquark and three\u2013quark picture. Measurements at the LHCb experiment revealed the doubly charmed tetraquark Tcc\u207a and provided new high-precision data relevant for understanding the structure of the X(3872). Interpreting such near-threshold systems requires accurate modeling of threshold effects, multi-body dynamics, and technically advanced statistical inference.\n\nWe present a Julia-based analysis ecosystem developed for studies of these states using LHCb data. The workflow combines established Julia packages for statistical modeling and minimization with specialized tools tailored for hadron spectroscopy. Core functionality relies on packages such as Distributions.jl, NumericalDistributions.jl and Minuit2.jl, extended by domain-specific probability distributions implemented in DistributionsHEP.jl.\n\nSeveral dedicated physics packages provide modular building blocks for amplitude models and line-shape calculations. These include X3872Flatte.jl, X3872ThreeBodyLineshape.jl, and xDDPhaseSpace.jl, implementing threshold line shapes, three-body kinematics, and phase-space constructions relevant for near-threshold charm systems. Complete analyses are developed in separate private repositories operating on collaboration-internal data and likelihood models; the software is released publicly once the corresponding analyses are published.\n\nA key component of the workflow is BuildConstructors.jl, a framework providing long-awaited granular control over complex fitting strategies. It enables structured construction of likelihood fits with systematic parameter management, including controlled fixing and releasing of parameters during iterative block likelihood fits.\n\nTogether these packages support complex spectroscopy analyses while extending the Julia ecosystem with tools for amplitude modeling and likelihood-based inference. They simplify the development of advanced analysis strategies and make Julia more accessible for future analyses in experimental particle physics.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "AQSTUL", "name": "Mikhail Mikhasenko", "avatar": "https://pretalx.com/media/avatars/7LX7TF_Vfl5Oxt.webp", "biography": "Mikhail Mikhasenko is a professor of experimental particle physics at Ruhr University Bochum and a member of the LHCb and COMPASS collaborations. His research focuses on hadron spectroscopy, with particular expertise in amplitude analysis and the study of exotic multiquark states.\n\nHe develops open scientific software and analysis tools for the computational and phenomenological study of exclusive hadronic processes, including methods for amplitude analysis and partial-wave modeling. A Julia user for about a decade.\n\nProminent julia projects:\n- [NumericalDistributions.jl](https://github.com/mmikhasenko/NumericalDistributions.jl), [DistributionsHEP.jl](https://github.com/JuliaHEP/DistributionsHEP.jl), [RooFitLite.jl](https://github.com/JuliaHEP/RooFitLite.jl)\n- [LorentzVectorBase.jl](https://github.com/JuliaHEP/LorentzVectorBase.jl), [FourVectors.jl](https://github.com/JuliaHEP/FourVectors.jl)\n- [BuildConstructors.jl](https://github.com/RUB-EP1/BuildConstructors.jl)\n- [HadronicLineshapes.jl](https://github.com/mmikhasenko/HadronicLineshapes.jl), [ThreeBodyDecays.jl](https://github.com/mmikhasenko/ThreeBodyDecays.jl), [InstructionalDecayTrees.jl](https://github.com/RUB-EP1/InstructionalDecayTrees.jl), [CascadeDecays.jl](https://github.com/RUB-EP1/CascadeDecays.jl)", "public_name": "Mikhail Mikhasenko", "guid": "81b7ce48-ca1e-5911-9984-14a1ef7b157d", "url": "https://pretalx.com/juliacon-2026/speaker/AQSTUL/"}, {"code": "QDRUFW", "name": "Robert Hentges", "avatar": null, "biography": null, "public_name": "Robert Hentges", "guid": "ded2878d-70a3-5f01-958b-6ef2bfa21fcd", "url": "https://pretalx.com/juliacon-2026/speaker/QDRUFW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WDZ3AN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WDZ3AN/", "attachments": []}, {"guid": "0a45f634-f171-5f1b-a536-430933efbcab", "code": "3RXM3H", "id": 93472, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3RXM3H/image_aVOnvbt.webp", "date": "2026-08-12T10:15:00+02:00", "start": "10:15", "end": "2026-08-12T10:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93472-experiences-of-julia-versus-other-languages-as-educational-targets-for-undergraduate-physicists", "url": "https://pretalx.com/juliacon-2026/talk/3RXM3H/", "title": "Experiences of Julia (versus other languages) as educational targets for undergraduate physicists", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Short talk", "language": "en", "abstract": "Whilst the majority of Universities have pivoted increasingly to Python as the primary (and in some cases, only) teaching language for Undergraduate Physics, there are compelling reasons to introduce Julia as a target language.\nThis is not without precedent: Engineering departments in multiple countries (Germany, USA...) have used Julia as a teaching language for some years, due to the ease of porting mathematical expressions to it, and its similarity to MATLAB.\nI discuss the motivations for doing so in Physics, and some experiences of introducing Julia in a structured way to undergraduate students at the University of Glasgow (although not in a formal course).", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "DHBLPU", "name": "Sam Skipsey", "avatar": null, "biography": "Sam Skipsey is a Research Fellow at the University of Glasgow, School of Physics and Astronomy. As well as acting as Project Manager for the GridPP project, they also teach undergraduate computing-for-physics courses and are a member of the University's Community of Practice for Decolonising the Curriculum.", "public_name": "Sam Skipsey", "guid": "1ac8dc90-0b37-5f3f-bf02-c2c0cc7d60b0", "url": "https://pretalx.com/juliacon-2026/speaker/DHBLPU/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3RXM3H/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3RXM3H/", "attachments": []}, {"guid": "1b7d7ac3-c807-52f5-93e6-a78922b2ed24", "code": "F3FBUY", "id": 93442, "logo": "https://pretalx.com/media/juliacon-2026/submissions/F3FBUY/image_vfcGK6F.webp", "date": "2026-08-12T10:30:00+02:00", "start": "10:30", "end": "2026-08-12T11:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93442-parallel-processing-in-jetreconstruction-jl", "url": "https://pretalx.com/juliacon-2026/talk/F3FBUY/", "title": "Parallel Processing in JetReconstruction.jl", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Long talk", "language": "en", "abstract": "The Julia JetReconstruction.jl package was released in 2024 and provides a\nhigh-performance native Julia implementation of serial jet reconstruction in\nhigh-energy physics, inspired by the FastJet C++ package. In serial mode the\ncode outperforms FastJet by 14-40%, depending on the exact parameters used.\n\nUntil now, no systematic testing and benchmarking of the code had been done\nwhen running in a parallel, multi-threaded, mode. In this paper we present the\nfirst results of doing this. The initial parallel performance of\nJetReconstruction.jl, compared to an equivalent multi-threaded version of\nFastJet, showed increasingly poor performance, with the advantage of\nJetReconstruction.jl quickly evaporating and becoming about a x10 deficit at\nhigh thread counts in the worst case.\n\nWe performed an investigation of the underlying reasons for this poor\nperformance, looking at thread scheduling parameters, physics code scaling, and\nmemory allocations and garbage collection issues. We review this performance\ninvestigation, and the profiling tools which we used, and how we finally\nunderstood the source of the problem: memory allocations and garbage collection.\n\nWe have now designed a new approach to reuse already allocated memory, with\nmuch better parallel scaling. We discuss ergonomics of this new interface, which\nnow allows users to run in a highly parallel mode with excellent performance,\ntested up to 64 parallel threads, which consistently maintains the advantage of\nJetReconstruction.jl over FastJet.", "description": "The Julia JetReconstruction.jl package was released in 2024 and provides a\nhigh-performance native Julia implementation of serial jet reconstruction in\nhigh-energy physics, inspired by the FastJet C++ package. In serial mode the\ncode outperforms FastJet by 14-40%, depending on the exact parameters used.\n\nUntil now, no systematic testing and benchmarking of the code had been done\nwhen running in a parallel, multi-threaded, mode. In this paper we present the\nfirst results of doing this. The initial parallel performance of\nJetReconstruction.jl, compared to an equivalent multi-threaded version of\nFastJet, showed increasingly poor performance, with the advantage of\nJetReconstruction.jl quickly evaporating and becoming about a x10 deficit at\nhigh thread counts in the worst case.\n\nWe performed an investigation of the underlying reasons for this poor\nperformance, looking at thread scheduling parameters, physics code scaling, and\nmemory allocations and garbage collection issues. We review this performance\ninvestigation, and the profiling tools which we used, and how we finally\nunderstood the source of the problem: memory allocations and garbage collection.\n\nWe have now designed a new approach to reuse already allocated memory, with\nmuch better parallel scaling. We discuss ergonomics of this new interface, which\nnow allows users to run in a highly parallel mode with excellent performance,\ntested up to 64 parallel threads, which consistently maintains the advantage of\nJetReconstruction.jl over FastJet.", "recording_license": "", "do_not_record": false, "persons": [{"code": "RTQSQN", "name": "Harshit Nagpal", "avatar": null, "biography": "-", "public_name": "Harshit Nagpal", "guid": "566323bc-65c2-54fb-a454-de60b39b9a23", "url": "https://pretalx.com/juliacon-2026/speaker/RTQSQN/"}, {"code": "RBNVZC", "name": "Graeme Stewart", "avatar": "https://pretalx.com/media/avatars/ZBJRN7_QROAcnY.webp", "biography": "_", "public_name": "Graeme Stewart", "guid": "eccbf754-34a3-5a1f-9841-cdc9172ceb99", "url": "https://pretalx.com/juliacon-2026/speaker/RBNVZC/"}, {"code": "WXQ9M8", "name": "Mateusz Fila", "avatar": null, "biography": "-", "public_name": "Mateusz Fila", "guid": "6bac6058-aa1c-5b80-b9d1-da56567ee956", "url": "https://pretalx.com/juliacon-2026/speaker/WXQ9M8/"}], "links": [{"title": "GitHub", "url": "https://github.com/JuliaHEP/JetReconstruction.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/F3FBUY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/F3FBUY/", "attachments": [{"title": "Slides", "url": "/media/juliacon-2026/submissions/F3FBUY/resources/Parallel_Jet_Qg8lPPx.pdf", "type": "related"}]}, {"guid": "56d5bade-352c-5d7b-95fc-5e220b2b0777", "code": "DAY8TS", "id": 93465, "logo": "https://pretalx.com/media/juliacon-2026/submissions/DAY8TS/image_teg3OpC.webp", "date": "2026-08-12T11:00:00+02:00", "start": "11:00", "end": "2026-08-12T11:30:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93465-performance-portable-random-sampling-in-julia-event-generation-and-particle-transport-on-gpus", "url": "https://pretalx.com/juliacon-2026/talk/DAY8TS/", "title": "Performance-Portable Random Sampling in Julia: Event Generation and Particle Transport on GPUs", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Long talk", "language": "en", "abstract": "In this talk, we present techniques for implementing efficient GPU-based random sampling\nalgorithms, including rejection sampling and multi-stage sampling pipelines using\nKernelAbstractions.jl. We discuss strategies to avoid costly synchronizations between host\nand device, manage divergent execution flows, and schedule heterogeneous\nworkloads entirely on device. Particular attention is given to structuring rejection\nsampling and sequential transport algorithms in ways that preserve GPU occupancy while\nmaintaining statistical correctness.\n\nTo demonstrate the applicability of these techniques, we present results from two Julia\npackages developed for plasma and high-energy physics applications. The first,\nQEDevents.jl, focuses on Monte-Carlo event generation for high-multiplicity\nscattering processes in quantum electrodynamics, building on the generic sampling\nframework RejectionSamplers.jl and the QuantumElectrodynamics.jl ecosystem. The second,\nPhotonTransport.jl implements a Monte-Carlo photon transport code for warm-dense matter.\nTogether, these case studies illustrate how Julia enables both the expressive\nimplementation of parallel sampling algorithms and near-peak accelerator performance,\nwhile maintaining portability across hardware backends.", "description": "Monte-Carlo techniques are central for many applications in physics simulations, such as\nevent generation in high-energy physics or particle transport in matter. A common\ntheme in these applications is the random sampling of complex probability\ndistributions, often using techniques such as rejection sampling or multi-stage sampling\nschemes. While these algorithms are simple and robust, the inherent randomness\nposes challenges to modern accelerators.\n\nIn applications such as Monte-Carlo event generation, the target distributions are usually\nhigh-dimensional and expensive to evaluate. Similarly, in Monte-Carlo particle transport,\nthe involved sequence of stochastic events that determine the particle's\npropagation, interaction channels, and momentum updates within a spatially resolved geometry.\nAlthough both classes of algorithms are often described as embarrassingly parallel, since\nthe events and particle histories are independent, the random nature of the execution path\nleads to highly heterogeneous workloads. This irregularity complicates the efficient scheduling on GPUs and significantly limits the achievable throughput.\n\nIn the presented work. we highlight how Julia's GPU tooling can support stochastic simulation workflows and provides practical patterns for implementing scalable Monte-Carlo algorithms on\nheterogeneous systems.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7D3TNW", "name": "Uwe Hernandez Acosta", "avatar": "https://pretalx.com/media/avatars/7D3TNW_8KuuBWA.webp", "biography": "I am a particle physicist by training, currently working on the theoretical side of strong laser interactions and matter under extreme conditions at the Helmholtz-Zentrum Dresden-Rossendorf. For the past five years, I have conducted all my research in Julia, and I am the primary author of several Julia packages, including the QuantumElectrodynamics.jl framework, JuliaXRTS, and a maintainer at the JuliaHEP GitHub organization. In addition, for the past three years, I have served as one of the conveners of the JuliaHEP working group within the HEP Software Foundation, making me an active member of the Julia community in high-energy physics.", "public_name": "Uwe Hernandez Acosta", "guid": "87698a99-3fb9-5a95-9a8c-fdf5d1282599", "url": "https://pretalx.com/juliacon-2026/speaker/7D3TNW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/DAY8TS/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/DAY8TS/", "attachments": []}, {"guid": "1d13e9b8-fefb-506d-b1e7-fdf5f68f1317", "code": "AZM7B7", "id": 93396, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AZM7B7/image_PknlVvw.webp", "date": "2026-08-12T11:30:00+02:00", "start": "11:30", "end": "2026-08-12T11:45:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93396-makie-s-new-raytracing-backend", "url": "https://pretalx.com/juliacon-2026/talk/AZM7B7/", "title": "Makie's new Raytracing backend", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Short talk", "language": "en", "abstract": "A quick overview of the new Raytracing backend, with live demos, technical insights and how it can be used for JuliaHEP.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "JHFESW", "name": "Simon Danisch", "avatar": "https://pretalx.com/media/avatars/JSJDXE_fZrtTfz.webp", "biography": "Simon Danisch is the creator of Makie.jl, Bonito.jl, GPUArrays.jl, and BonitoBook.jl. With a background in cognitive science and computer vision, he has spent the last decade building out Julia's visualization, interactive UI, and GPU computing ecosystem.", "public_name": "Simon Danisch", "guid": "9fe14921-7cb7-55ed-abe4-d22427bd2b37", "url": "https://pretalx.com/juliacon-2026/speaker/JHFESW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AZM7B7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AZM7B7/", "attachments": []}, {"guid": "0389f2a8-d57d-5d28-b592-21d462d49c50", "code": "HXMUHB", "id": 93481, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HXMUHB/image_eRbSNwS.webp", "date": "2026-08-12T11:45:00+02:00", "start": "11:45", "end": "2026-08-12T12:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93481-julia-for-data-analysis-in-lhcb-experiment", "url": "https://pretalx.com/juliacon-2026/talk/HXMUHB/", "title": "Julia for Data Analysis in LHCb experiment", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Short talk", "language": "en", "abstract": "We present a Julia-based workflow for the analysis of exclusive multibody processes in collider data with the LHCb experiment. The pipeline covers ROOT I/O with UnROOT.jl, transformation of event data into tabular representations, construction of physics-motivated kinematic variables for multibody final states such as invariant masses and helicity angles, and statistical fitting. We also demonstrate reproducible analysis infrastructure in Julia, including composable scripts, environment and dependency management, artifact handling, and systematic reruns of the full analysis chain.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "JTBPLX", "name": "Ilya Segal", "avatar": "https://pretalx.com/media/avatars/ZNZHHY_LVxGs8Z.webp", "biography": "I am a third-year PhD student in the LHCb Collaboration and Ruhr-University Bochum. My research focuses on the angular analysis of systems consisting of two vector mesons. This includes the study of all-charmed tetraquark candidates in the double-J/\u03c8 spectrum, the analysis of central exclusive production of \u03c6-meson pairs with potential glueball candidates, and investigations of charmonium states produced in b-hadron decays via their decays into \u03c6-meson pairs. I am doing the whole analysis based on the Julia language, converting the produced tools into packages within the JuliaHEP environment.", "public_name": "Ilya Segal", "guid": "c81874a7-59dc-5067-a2ea-29044409d457", "url": "https://pretalx.com/juliacon-2026/speaker/JTBPLX/"}], "links": [{"title": "GitHub repo with notebook", "url": "https://github.com/RUB-EP1/HEP-analysis-with-Julia", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HXMUHB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HXMUHB/", "attachments": [{"title": "flyer-HXMUHB", "url": "/media/juliacon-2026/submissions/HXMUHB/resources/HXMUHB_pu8zPCz.png", "type": "related"}, {"title": "Talk", "url": "/media/juliacon-2026/submissions/HXMUHB/resources/juliacon-202_uFSzie4.pdf", "type": "related"}, {"title": "Talk (pptx)", "url": "/media/juliacon-2026/submissions/HXMUHB/resources/juliacon-20_MIADH66.pptx", "type": "related"}]}, {"guid": "9e3b7480-0a99-5f14-b946-cd7744df038b", "code": "HVZZQM", "id": 93474, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HVZZQM/image_b27DZq0.webp", "date": "2026-08-12T12:00:00+02:00", "start": "12:00", "end": "2026-08-12T12:30:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93474-the-legend-experiment-how-to-run-an-entire-experiment-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/HVZZQM/", "title": "The LEGEND Experiment: How to run an entire experiment in Julia", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Long talk", "language": "en", "abstract": "The **L**arge **E**nriched **Ge**rmanium Experiment for **N**eutrinoless $\\beta\\beta$ **D**ecay (LEGEND) experimental program is dedicated to the search for the neutrinoless double-beta ($0\\nu\\beta\\beta$) decay of $^{76}\\mathrm{Ge}$ with isotopically enriched high-purity germanium (HPGe) detectors and a discovery sensitivity beyond a half-life of $10^{28}$ years. The project's first phase, LEGEND-200, has stably accumulated physics data at the Laboratori Nazionali del Gran Sasso (LNGS). The first unblinding revealed a new best global limit of $T_{0\\nu}^{1/2}>1.9\\cdot{10}^{26}\\, \\mathrm{yr}$ (90% confidence level). We present an update on our ongoing end-to-end analysis of the experiment with the Julia LEGEND Software (JuLeS). This session will focus on the advancements of JuLeAna (Julia LEGEND Analysis) and its application to the current LEGEND data. Key subjects will include performance evaluation, data management, the GPU-accelerated Digital Signal Processing (DSP) framework, calibration and spectral fitting routines, event reconstruction, as well as IO performance. Additionally, we will provide a brief demonstration highlighting the enhancements within our customized SLURM-based parallel processing environment.\n\nThis work is supported by the U.S. DOE, and the NSF, the LANL, ORNL and LBNL LDRD programs; the European ERC and Horizon programs; the German DFG, BMBF, and MPG; the Italian INFN; the Polish NCN and MNiSW; the Czech MEYS; the Slovak RDA; the Swiss SNF; the UK STFC; the Canadian NSERC and CFI; the LNGS and SURF facilities.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "WRBDBA", "name": "Florian Henkes", "avatar": "https://pretalx.com/media/avatars/TRVW9S_29dchMw.webp", "biography": "I am research scientist pursuing my PhD at the Technical University of Munich and the Max-Planck for Nuclear Physics working for the LEGEND Experiment. My research focuses on the search for the Neutrinoless double-beta decay in $^{76}\\mathrm{Ge}$ using semiconductor detectors. I am an active developer and maintainer of several open source julia packages for Digital Signal Processing, Statistical inference and HPC in julia and an active member of the JuliaHEP community.", "public_name": "Florian Henkes", "guid": "f1d9ec27-3f09-5d6c-b6f2-0fed1294eac3", "url": "https://pretalx.com/juliacon-2026/speaker/WRBDBA/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HVZZQM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HVZZQM/", "attachments": []}, {"guid": "114d9e85-cb0c-5673-aac5-1462b25f5f86", "code": "ZSLQ7J", "id": 103854, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZSLQ7J/image_lLJi5Or.webp", "date": "2026-08-12T12:30:00+02:00", "start": "12:30", "end": "2026-08-12T13:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-103854-panel-discussion-what-s-next-for-juliahep-from-wrappers-to-community", "url": "https://pretalx.com/juliacon-2026/talk/ZSLQ7J/", "title": "Panel Discussion: What\u2019s Next for JuliaHEP? From Wrappers to Community.", "subtitle": "", "track": "JuliaHEP Mini 2026 - Julia for Nuclear and Elementary Particle Physics", "type": "Long talk", "language": "en", "abstract": "Julia has demonstrated strong potential for high-performance scientific computing, and the JuliaHEP community is rapidly expanding across simulation and data analysis in high-energy, hadron, plasma, and nuclear physics. This moderated panel will discuss what comes next: how can we move beyond individual success stories and wrapping existing frameworks towards building a sustainable software ecosystem for these communities?\nThe discussion will address scaling challenges, the balance between integrating existing tools and developing native solutions, and the role of interoperability in established scientific software landscapes. Beyond technical aspects, the panel will explore opportunities for collaboration, community growth, and lowering barriers to adoption. The goal is to identify concrete next steps to strengthen JuliaHEP and support the wider use of the Julia programming language.", "description": "Julia has demonstrated strong potential for high-performance scientific computing, and the JuliaHEP community is rapidly expanding across simulation and data analysis in high-energy, hadron, plasma, and nuclear physics. This moderated panel will discuss what comes next: how can we move beyond individual success stories and wrapping existing frameworks towards building a sustainable software ecosystem for these communities?\nThe discussion will address scaling challenges, the balance between integrating existing tools and developing native solutions, and the role of interoperability in established scientific software landscapes. Beyond technical aspects, the panel will explore opportunities for collaboration, community growth, and lowering barriers to adoption. The goal is to identify concrete next steps to strengthen JuliaHEP and support the wider use of the Julia programming language.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7D3TNW", "name": "Uwe Hernandez Acosta", "avatar": "https://pretalx.com/media/avatars/7D3TNW_8KuuBWA.webp", "biography": "I am a particle physicist by training, currently working on the theoretical side of strong laser interactions and matter under extreme conditions at the Helmholtz-Zentrum Dresden-Rossendorf. For the past five years, I have conducted all my research in Julia, and I am the primary author of several Julia packages, including the QuantumElectrodynamics.jl framework, JuliaXRTS, and a maintainer at the JuliaHEP GitHub organization. In addition, for the past three years, I have served as one of the conveners of the JuliaHEP working group within the HEP Software Foundation, making me an active member of the Julia community in high-energy physics.", "public_name": "Uwe Hernandez Acosta", "guid": "87698a99-3fb9-5a95-9a8c-fdf5d1282599", "url": "https://pretalx.com/juliacon-2026/speaker/7D3TNW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZSLQ7J/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZSLQ7J/", "attachments": []}, {"guid": "a2a28481-e072-5e76-8773-2651d8074150", "code": "N7CSPV", "id": 92770, "logo": "https://pretalx.com/media/juliacon-2026/submissions/N7CSPV/image_jFVNVAM.webp", "date": "2026-08-12T14:30:00+02:00", "start": "14:30", "end": "2026-08-12T14:45:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92770-a-purely-numeric-approach-to-the-nonlinear-coherent-thomson-scattering-by-structured-light", "url": "https://pretalx.com/juliacon-2026/talk/N7CSPV/", "title": "A purely numeric approach to the nonlinear coherent Thomson scattering by structured light.", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Short talk", "language": "en", "abstract": "Structured light in interaction with matter has been of interest, particularly as it relates to the production of high intensity gamma beams. In our package, [ElectronDynamicsModels.jl]([url](https://github.com/SebastianM-C/ElectronDynamicsModels.jl)), we developed a way to efficiently compute the radiated field resulting from the scattering of a Laguerre-Gauss laser beam off a thin sheet of electrons. The electrons are represented as relativistic classical particles whose motion is integrated using _DifferentialEquations.jl_. _ModellingToolkit.jl_  was used to formulate the model, allowing us to take advantage of its compiler to generate efficient _Julia_ code. Moreover, this also enables an easy scaling to parallel ensemble simulations on the CPU and GPU. Besides performance, this approach is also useful for enabling higher precision computations, which naturally leverage _Julia_'s multiple dispatch. Once the trajectories are known, the far electromagnetic field can be computed over a grid of pixels from the Lienardt-Wiechert potentials, and finally we compute their Fourier transform.", "description": "In our thin sheet model each electron interacts only with the incident radiation and its motion is described by the manifest covariant equations of motion (used here for accuracy and speed reasons) which is a system of 8 ODEs expressed in term of proper time. Thus, by taking advantage of _ModellingToolkit.jl_'s codegen we can readily construct a _DifferentialEquations.jl_ `EnsembleProblem` in order to perform the embarrassing parallelism of solving for the particles' trajectories. \n\nSince our goal is to compute the Fourier transform of the total radiated field with respect to laboratory time, it's necessary that for each pixel on our screen we accumulate the synchronized (for the sake of coherence) contribution. This is achieved by solving an implicit ODE relating the screen advanced time to the proper time of each particle.\n\nFinally, using the sampled screen electromagnetic field we approximate the continuous Fourier transform using _FFTW.jl_ in order to get the spatial profile and phase of the resulting harmonics of the Thomson scattered radiation, as well as evaluate how coherence is lost for increasingly more intense incident radiation.", "recording_license": "", "do_not_record": false, "persons": [{"code": "99WPSY", "name": "Petru-Vlad TOMA", "avatar": null, "biography": "- PhD student at the Faculty of Physics of the University of Bucharest.\n- Research assistant at the Center of Advanced Laser Technologies (CETAL) of the National Institute for Laser Plasma and Radiation (Romania).", "public_name": "Petru-Vlad TOMA", "guid": "dc725c87-5d69-53ad-931c-806d585d6fa1", "url": "https://pretalx.com/juliacon-2026/speaker/99WPSY/"}, {"code": "C7BG78", "name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "avatar": null, "biography": "Software Eng. at JuliaHub & PhD student at University of Bucharest.", "public_name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "guid": "3e31b822-d61c-5033-b95f-3f20ed7eccbc", "url": "https://pretalx.com/juliacon-2026/speaker/C7BG78/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/N7CSPV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/N7CSPV/", "attachments": [{"title": "flyer-N7CSPV", "url": "/media/juliacon-2026/submissions/N7CSPV/resources/N7CSPV_CgKzQLO.png", "type": "related"}]}, {"guid": "84480fd4-e28c-5c01-9e31-eadd17e9de4f", "code": "WKD3CN", "id": 93475, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WKD3CN/image_eZh2HUe.webp", "date": "2026-08-12T14:45:00+02:00", "start": "14:45", "end": "2026-08-12T15:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93475-aquarium-2-0-realizing-robotic-swimming-with-differentiable-fluid-structure-interaction-simulation", "url": "https://pretalx.com/juliacon-2026/talk/WKD3CN/", "title": "Aquarium 2.0: Realizing Robotic Swimming with Differentiable Fluid-Structure Interaction Simulation", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Short talk", "language": "en", "abstract": "Matching the swimming efficiency and agility of fish has remained an elusive goal in underwater robotics \u2014 one that demands accurate simulation of complex vortex interactions between a robot's body and the surrounding fluid. These dynamics, governed by coupled ordinary and partial differential equations, pose far greater computational challenges than the multi-body dynamics of classical rigid robotic systems.\n\nWe present Aquarium 2.0, a Julia framework for simulating strongly coupled fluid-robot multiphysics as a unified optimization problem. The coupled manipulator and incompressible Navier-Stokes equations are derived together from a single Lagrangian using the principle of least action, and we employ discrete variational mechanics to obtain a stable, well-conditioned, and physically accurate scheme for jointly simulating articulated bodies and their surrounding fluid. Derivatives of the fully coupled dynamics are computed via the implicit function theorem, making the simulator directly amenable to gradient-based optimization within Julia's scientific computing ecosystem.\n\nWe showcase various swimming demonstrations of a bioinspired swimming robot, including forward undulation and a highly dynamic, optimized C-start escape maneuver. Both gaits are validated on physical hardware, demonstrating successful sim-to-real transfer.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "UVMYTW", "name": "JJ Lee", "avatar": "https://pretalx.com/media/avatars/G7N7EP_FvJCztC.webp", "biography": "JJ Lee is a PhD candidate in the Robotic Exploration (REx) Lab at the Carnegie Mellon University Robotics Institute, where he works on dynamics modeling, optimization, and control for bio-inspired underwater robots. His research spans optimal control, fluid-structure interaction, and data-driven control methods for complex robotic systems.", "public_name": "JJ Lee", "guid": "8a29d320-1cbb-5079-84bd-167a0621f349", "url": "https://pretalx.com/juliacon-2026/speaker/UVMYTW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WKD3CN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WKD3CN/", "attachments": []}, {"guid": "c2186385-d950-54a6-b8cd-e9fc0139ef09", "code": "LP7GLM", "id": 92531, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LP7GLM/image_d2X195U.webp", "date": "2026-08-12T15:00:00+02:00", "start": "15:00", "end": "2026-08-12T15:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92531-fewbodytoolkit-jl-solving-2-and-3-body-quantum-systems-in-1d-3d-with-general-potentials", "url": "https://pretalx.com/juliacon-2026/talk/LP7GLM/", "title": "FewBodyToolkit.jl: Solving 2- and 3-body quantum systems in 1D\u20133D with general potentials", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Short talk", "language": "en", "abstract": "[FewBodyToolkit.jl](https://github.com/lhapp27/FewBodyToolkit.jl) is a Julia package for solving quantum few-body problems with two and three particles in one to three dimensions. It supports pair interactions of general shape, bound and resonant states, and on-the-fly calculation of observables. The package has been applied in research on ultracold atomic systems and hadron physics, providing a unified framework for diverse few-body applications.", "description": "[FewBodyToolkit.jl](https://github.com/lhapp27/FewBodyToolkit.jl) is a Julia package for solving quantum few-body problems with two and three particles in one to three dimensions. It supports pair interactions of general shape, bound and resonant states, and on-the-fly calculation of observables. The package has been applied in research on ultracold atomic systems and hadron physics, providing a unified framework for diverse few-body applications. In this talk, I will give an overview of its main features, selected implementation aspects, and future development plans.", "recording_license": "", "do_not_record": false, "persons": [{"code": "R9X7X8", "name": "Lucas Happ", "avatar": "https://pretalx.com/media/avatars/EGLQTJ_hvQMj7J.webp", "biography": "I am a postdoctoral researcher (SPDR) at RIKEN in Japan. My research focuses on quantum physics, especially few-body physics, but I like to discuss about anything.", "public_name": "Lucas Happ", "guid": "a0d148ad-5226-5fec-bd1b-40a332fff0d3", "url": "https://pretalx.com/juliacon-2026/speaker/R9X7X8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LP7GLM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LP7GLM/", "attachments": []}, {"guid": "9a50e693-3937-5f16-9c34-500f6bfdc3f8", "code": "VSZT9C", "id": 92632, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VSZT9C/image_95rWkMF.webp", "date": "2026-08-12T15:15:00+02:00", "start": "15:15", "end": "2026-08-12T15:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92632-implementing-lattice-qcd-to-multi-gpu-systems-with-juliaqcd", "url": "https://pretalx.com/juliacon-2026/talk/VSZT9C/", "title": "Implementing Lattice QCD to Multi-GPU Systems with JuliaQCD", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Short talk", "language": "en", "abstract": "Lattice QCD simulations are restricted by both massive computational costs and high memory requirements. To simulate large physical volumes, distributing the lattice across multiple GPUs is essential. We present JuliaQCD, a native Julia ecosystem designed for computations on HPCs. By integrating JACC.jl for vendor-neutral GPU kernels and MPI.jl for lattice decomposition, we enable simulations on lattices that exceed the memory capacity of a single device while maintaining high performance.", "description": "Background:\n\nLattice Quantum Chromodynamics (LQCD),  which stands as a foundational framework enabling first-principle non-perturbative study for the strong force in nature, is a grand challenge computational problem in high-energy physics. Due to the 4-dimensional nature of the spacetime grid, memory requirements grow with the lattice extent. Often, a single high-end GPU does not have enough VRAM to store the necessary gauge and fermion fields for physically relevant volumes. Therefore, multi-GPU parallelism in JuliaQCD is not only an optimization for speed but also a fundamental requirement for large-volume physics simulations.\n\nTechnical Content:\n\n- Overcoming the Memory Wall: We discuss how JuliaQCD handles 4D lattice decomposition across multiple GPU nodes. This allows us to simulate larger volumes by pooling the memory of an entire cluster.\n\n- Performance Portability with JACC.jl: We highlight the use of JACC.jl to write kernels that are portable across NVIDIA, AMD, and other hardware. This ensures that JuliaQCD remains flexible as HPC centers transition to diverse GPU architectures.\n\n- Communication Overheads: We detail our implementation of halo (ghost zone) exchanges using MPI.jl. We explain how we minimize the latency penalties associated with moving data between GPUs to maintain efficiency as the number of nodes increases.\n\nThe main goal of this talk is to demonstrate that the JuliaQCD suite provides a scalable, memory-efficient framework for modern lattice simulations. We aim to show that Julia is fully capable of handling the memory distribution of large-scale theoretical physics with multiple GPUs on various HPC architectures.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZNMYKG", "name": "Ho Hsiao", "avatar": null, "biography": "I am a Postdoctoral Researcher at the Center for Computational Sciences (CCS), University of Tsukuba, Japan. My research focuses on Lattice Field Theory, aiming to understand the strong force, one of the fundamental interactions in nature. To achieve such research goals, I develop numerical tools that leverage HPC across diverse architectures, including both CPUs and GPUs. Recently, I have also been interested in applying Machine Learning to accelerate lattice calculations.", "public_name": "Ho Hsiao", "guid": "ed9042c2-b2cf-58fd-b714-68e35672b956", "url": "https://pretalx.com/juliacon-2026/speaker/ZNMYKG/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VSZT9C/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VSZT9C/", "attachments": []}, {"guid": "d6efe5ad-bfd8-51e5-b0b4-9ffa686c04b7", "code": "NDZHNQ", "id": 92621, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NDZHNQ/image_1ufvRw7.webp", "date": "2026-08-12T15:30:00+02:00", "start": "15:30", "end": "2026-08-12T16:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92621-juliaqcd-a-pure-julia-framework-for-lattice-qcd-and-its-extension-with-compiler-level-automatic-differentiation", "url": "https://pretalx.com/juliacon-2026/talk/NDZHNQ/", "title": "JuliaQCD: A Pure Julia Framework for Lattice QCD and Its Extension with Compiler-Level Automatic Differentiation", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Long talk", "language": "en", "abstract": "Lattice Quantum Chromodynamics (Lattice QCD) is a first-principles approach to strongly interacting gauge theories and requires large-scale high-performance computing. Production codes in this field have traditionally been written in C, C++, or Fortran.\nJuliaQCD is a Lattice QCD simulation framework written entirely in Julia, developed to explore whether a high-level language can support both rapid development and large-scale HPC simulations. The framework provides distributed-memory parallelism via MPI and achieves competitive performance on modern CPUs and GPUs while keeping lattice field operations and data structures modular and expressive through multiple dispatch.\nIn our recent work [1], we extend JuliaQCD by introducing compiler-level reverse-mode automatic differentiation for the first time. Using LLVM-based AD, Hybrid Monte Carlo (HMC) force terms are generated directly from the action code, removing the need for separately derived and manually maintained force implementations. We validate the correctness of the automatically generated forces and examine their performance relative to conventional hand-written implementations.\nThis work demonstrates how Julia can support both conventional HPC-style implementations and more modern compiler-based techniques within a single, coherent framework for large-scale scientific computing.\n[1] Yuki Nagai, Akio Tomiya, Hiroshi Ohno, \"Lattice Gauge Theory via LLVM-Level Automatic Differentiation\", arXiv:2602.20516", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "UKAMFV", "name": "Yuki Nagai", "avatar": "https://pretalx.com/media/avatars/UKAMFV_jAhv2xW.webp", "biography": "I received my Doctor of Science degree in Physics from The University of Tokyo in 2010. After graduation, I joined the Center for Computational Science and e-Systems (CCSE) at the Japan Atomic Energy Agency (JAEA) as a Senior Scientist, where I worked from 2010 to 2024. During this period, I was also a Visiting Scholar in the Department of Physics at the Massachusetts Institute of Technology (MIT) from 2016 to 2017 and a Visiting Researcher at the RIKEN Center for Advanced Intelligence Project (RIKEN AIP) from 2018 to 2023. Since 2024, I have been an Associate Professor at the Information Technology Center, The University of Tokyo.\n\nMy research focuses on developing novel computational methods by combining machine learning, statistical physics, and high-performance computing. I am particularly interested in quantum many-body physics, superconductivity, first-principles molecular dynamics, and lattice gauge theory. I also develop open-source software in Julia for large-scale scientific computing on modern supercomputers.\n\nI am a developer of [JuliaQCD](https://github.com/JuliaQCD) project, [FluxKAN.jl](https://github.com/cometscome/FluxKAN.jl), [TightBinding.jl](https://github.com/cometscome/TightBinding.jl) and [QuadraticHamiltonians.jl](https://github.com/cometscome/QuadraticHamiltonians.jl)", "public_name": "Yuki Nagai", "guid": "b88455ba-0775-5728-9e69-d4c2c2fcdaeb", "url": "https://pretalx.com/juliacon-2026/speaker/UKAMFV/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NDZHNQ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NDZHNQ/", "attachments": []}, {"guid": "b462572f-2841-5edf-94a1-97a5a2fa8280", "code": "E7N39Z", "id": 92571, "logo": "https://pretalx.com/media/juliacon-2026/submissions/E7N39Z/image_yuYUstV.webp", "date": "2026-08-12T16:15:00+02:00", "start": "16:15", "end": "2026-08-12T16:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92571-parameter-optimization-of-domain-wall-fermion-based-on-machine-learning-framework", "url": "https://pretalx.com/juliacon-2026/talk/E7N39Z/", "title": "Parameter optimization of domain-wall fermion based on machine-learning framework", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Short talk", "language": "en", "abstract": "Lattice QCD is one of the most computationally demanding problems in theoretical physics, requiring large-scale parallel computation and sophisticated numerical algorithms.\nJuliaQCD is an open-source project that implements lattice QCD simulations in Julia, with an emphasis on performance portability across different computer architectures. By leveraging Julia\u2019s abstraction mechanisms and multiple dispatch, the framework enables rapid prototyping, flexible algorithm development, and high-performance execution on a wide range of computing platforms.\nIn this project, we focus on the domain-wall fermion formulation and construct a numerical optimization framework to tune its free parameters by minimizing selected physical observables, such as the effective mass. We discuss how this approach can be implemented efficiently within the JuliaQCD code base while maintaining readability and flexibility of the code.\nTo enable large-scale simulations, we employ MPI-based parallelization and demonstrate production runs on the Fugaku supercomputer, where Julia is not pre-installed. We address practical challenges of deploying Julia on such systems, including building the Julia runtime, integrating with the system MPI libraries, and preparing job scripts and execution environments.\nWe will present the physical motivation, software architecture, implementation strategies on Fugaku and related HPC systems, scaling and performance results for domain-wall fermions, and plans for the open-source release of these developments within JuliaQCD.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "MHWER9", "name": "Kenta Yoshimura", "avatar": "https://pretalx.com/media/avatars/RW8YR9_zgBsV8C.webp", "biography": "PhD student of physics department in Institute of Science Tokyo (former: Tokyo Institute of Technology).\nSpecialty is Nuclear and Hadron physics involved with high-performance computing.", "public_name": "Kenta Yoshimura", "guid": "942e177c-2233-5358-855d-af9f21643e80", "url": "https://pretalx.com/juliacon-2026/speaker/MHWER9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/E7N39Z/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/E7N39Z/", "attachments": [{"title": "Slides", "url": "/media/juliacon-2026/submissions/E7N39Z/resources/Juliacon_Ken_oimCo85.pdf", "type": "related"}]}, {"guid": "e0d41bcf-904f-5153-b9ac-544b6f416c5f", "code": "UFKC3U", "id": 92546, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UFKC3U/image_p0oTYlV.webp", "date": "2026-08-12T16:30:00+02:00", "start": "16:30", "end": "2026-08-12T17:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92546-testability-first-design-for-few-body-systems-physics", "url": "https://pretalx.com/juliacon-2026/talk/UFKC3U/", "title": "Testability-First Design for Few-Body Systems Physics", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Long talk", "language": "en", "abstract": "Open-source software (OSS) is a practical way to ensure reproducibility and avoid reinventing the wheel in computational physics. In particular, few-body systems physics has various targets and various software requirements, so we are building an open-source ecosystem in Julia as a shared infrastructure with reusability and extensibility. In this talk, we introduce the architectural design and development roadmap derived from system-level testability and report the current development status.", "description": "Few-body systems physics is an intersection of many subfields of physics and chemistry. Its targets are various few-body systems across various scales, from hadrons and nuclei to atoms, molecules, and quantum dots. Ideally, researchers would like to calculate physical observables for arbitrary particle numbers, arbitrary interactions, and arbitrary quantum states. In practice, no single software package can satisfy all of these requirements, and researchers often develop self-tailored codebases for their specific problems.\n\nTo make this development culture scalable, we are building an open-source ecosystem as shared infrastructure that provides reusable building blocks and a flexible common interface in the Julia programming language. CI/CD with automated testing is a baseline requirement for maintainability and for accepting external contributions. Beyond its role in maintenance, testing also has a role in design, because the requirements for the system-level testability derive the architectural design.\n\nDifferential testing based on cross-method comparisons has been used in [few-body calculations](https://doi.org/10.1103/PhysRevC.64.044001). For established benchmark problems, the final outputs can be validated using test oracles built from a database of results in the literature. However, for previously unexplored problems, there is no verified \u201ccorrect answer\u201d. This requirement motivates a common interface that allows multiple methods to operate on the same Hamiltonian.\n\nThe Rayleigh\u2013Ritz method using [explicitly correlated Gaussian (ECG)](https://doi.org/10.1103/RevModPhys.85.693) basis functions is widely used for solving the Schr\u00f6dinger equation of quantum-mechanical few-body systems, while [variational Monte Carlo (VMC)](\nhttps://doi.org/10.48550/arXiv.1508.02989\n) can calculate the energy for a given trial wave function. Currently, we are developing [FewBodyECG.jl](https://github.com/JuliaFewBody/FewBodyECG.jl) and [MetropolisAlgorithm.jl](https://github.com/JuliaFewBody/MetropolisAlgorithm.jl) (toward [FewBodyVMC.jl](https://github.com/JuliaFewBody/FewBodyVMC.jl)) for cross-checking multiple methods. [FewBodyHamiltonian.jl](https://github.com/JuliaFewBody/FewBodyHamiltonians.jl) supports flexible construction of Hamiltonians as a common input for multiple methods. [FewBodyDB.jl](https://github.com/JuliaFewBody/FewBodyDB.jl) works as a test oracle database, and [TwoBody.jl](https://github.com/JuliaFewBody/TwoBody.jl) works as a prototype of FewBody.jl, the common interface of this [JuliaFewBody](https://github.com/JuliaFewBody) ecosystem.\n\nWhile we don't necessarily practice test-first or test-driven development, we find testability to be a compass for our overall architectural design and long-term development roadmap. We hope this ecosystem will open the gate to a \"trading port\" of computational methods.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7XEYCP", "name": "Shuhei Ohno", "avatar": "https://pretalx.com/media/avatars/7XEYCP_6B2IM7E.webp", "biography": "PhD student at Yokohama City University and co-founder of [JuliaFewBody](https://github.com/JuliaFewBody)", "public_name": "Shuhei Ohno", "guid": "bdcc7aa6-13fb-55c1-8477-dba11e5b84ab", "url": "https://pretalx.com/juliacon-2026/speaker/7XEYCP/"}, {"code": "FWSLJK", "name": "Martin Mikkelsen", "avatar": null, "biography": "PhD student at the University of Copenhagen working on tensor networks", "public_name": "Martin Mikkelsen", "guid": "031293ce-bf0e-5257-a01d-16720bdbf116", "url": "https://pretalx.com/juliacon-2026/speaker/FWSLJK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UFKC3U/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UFKC3U/", "attachments": [{"title": "flyer-UFKC3U", "url": "/media/juliacon-2026/submissions/UFKC3U/resources/UFKC3U_JfWRcWE.png", "type": "related"}]}, {"guid": "b584cba9-5f11-53c8-aafc-4c5d87817b3d", "code": "T9CVPT", "id": 92665, "logo": "https://pretalx.com/media/juliacon-2026/submissions/T9CVPT/image_3h6vw7d.webp", "date": "2026-08-12T17:00:00+02:00", "start": "17:00", "end": "2026-08-12T17:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92665-visualizinglqcd-jl-visualization-of-quantum-vacuum", "url": "https://pretalx.com/juliacon-2026/talk/T9CVPT/", "title": "VisualizingLQCD.jl: Visualization of quantum vacuum", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Short talk", "language": "en", "abstract": "Inside atomic nuclei, \"empty space\" is not empty: the strong force comes from a gluon field that fluctuates and binds quarks into protons and neutrons. Lattice QCD computes this by simulating QCD on a grid in space and time, usually on supercomputers. VisualizingQCD.jl, a JuliaQCD package, turns your own configuration files into 3D movies of local observables, so Julia users can see, debug, and share the quantum vacuum.", "description": "Quantum chromodynamics (QCD) is the theory of the strong force that holds atomic nuclei together. It predicts that the \"vacuum\" is a fluctuating gluon field, so even empty space has structure, and those fluctuations shape protons and neutrons. Lattice QCD makes this computable by simulating QCD on a 4D grid and generating many snapshots of the gluon field, typically on supercomputers. Because the output is a large 4D dataset, visualization is a practical way to build intuition, compare runs, and explain results.\n\nPrevious QCD outreach movies showed how compelling vacuum visualization can be, but they are fixed examples. When you develop algorithms or tune parameters, you want to visualize your own configurations from your own simulations. VisualizingQCD.jl, part of the JuliaQCD project, provides an open-source pipeline that reads ILDG configuration files, computes a 3D scalar field on each time slice, and renders iso-surfaces into MP4 videos with Makie. The workflow is plain Julia code, so it is reproducible and easy to extend.\n\nIn this short talk I will demo the pipeline end to end, starting from an ILDG file and producing a movie in a few lines. Next I will explain what can be visualized, focusing on local observables such as plaquette-based activity (the smallest loop on the grid), action density after gradient flow (a standard smoothing procedure), a common probe of vacuum structure. I will close with performance notes and pointers for adding new observables within the JuliaQCD ecosystem.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7FNFA7", "name": "Akio Tomiya", "avatar": "https://pretalx.com/media/avatars/7FNFA7_H0Rtbej.webp", "biography": "I am an associate professor in Tokyo Woman\u2019s Christian University and Kyoto University, visiting researcher of RIKEN using Julia for lattice QCD with machine learning.\n\nMy CV: https://www2.yukawa.kyoto-u.ac.jp/~akio.tomiya/index_en.html", "public_name": "Akio Tomiya", "guid": "b9ccc89f-f9b1-5960-af33-93f3d76350a4", "url": "https://pretalx.com/juliacon-2026/speaker/7FNFA7/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/T9CVPT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/T9CVPT/", "attachments": [{"title": "flyer-T9CVPT", "url": "/media/juliacon-2026/submissions/T9CVPT/resources/T9CVPT_FGth3zf.png", "type": "related"}]}, {"guid": "4c957ee0-3ea6-53f1-9963-8e2f629505a7", "code": "MKW7DV", "id": 92179, "logo": "https://pretalx.com/media/juliacon-2026/submissions/MKW7DV/image_F9rvI8A.webp", "date": "2026-08-12T17:15:00+02:00", "start": "17:15", "end": "2026-08-12T17:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92179-what-s-new-in-raytraceheattransfer-jl-since-juliacon2024", "url": "https://pretalx.com/juliacon-2026/talk/MKW7DV/", "title": "What's new in RayTraceHeatTransfer.jl since JuliaCon2024", "subtitle": "", "track": "Computational Physics Minisymposium", "type": "Short talk", "language": "en", "abstract": "RayTraceHeatTransfer.jl is a package for thermal radiation heat transfer calculations in Julia. The package features new methods which guarantee energy conservation and physically correct results, across the wavelength spectrum and for user-specified domains. In 1D and 2D the package handles volumetric radiation, relevant in combustion, climate sciences and astrophysics and in 3D it handles surface radiation, relevant in engineering design.", "description": "An early version of this package was presented at JuliaCon2024 Eindhoven. On the same day as the presentation in Eindhoven, the author invented a new method for solving general radiative transfer problems. This invention became the foundation which the new versions of the package builds upon. Since then, the package has evolved to adress most of the issues previously presented as future work.\n\nThis talk will present the main new features: Applications to 1D, 2D and 3D, with parameter variation across both the spatial and wavelength dimensions of the domains. Furthermore, the talk will touch on some of the fundamental physics of radiation, to clarify the necessary inputs and the concepts of the package.\n\nThe fundamental method which RayTraceHeatTransfer.jl is based on, is presented in the paper \"A Radiation Exchange Factor Transformation with Proven Convergence, Non-Negativity, and Energy Conservation\" (Bielefeld, 2025). In this paper, the method is proven to be mathematically and physically correct, under specified conditions. The paper is available at the arXiv preprint server, and more papers are under way, detailing currently unpublished features already available in the package.", "recording_license": "", "do_not_record": false, "persons": [{"code": "AUBBH9", "name": "Nikolaj Maack Bielefeld", "avatar": "https://pretalx.com/media/avatars/BQMRUD_Qu9Fxhy.webp", "biography": "I work as an engineer with model-based energy planning. In my sparetime I enjoy researching, developing and programming in Julia.", "public_name": "Nikolaj Maack Bielefeld", "guid": "2a1231ec-b26b-5bb8-bfe1-663aff232de1", "url": "https://pretalx.com/juliacon-2026/speaker/AUBBH9/"}], "links": [{"title": "ArXiv article presenting the fundamental method and guarantees", "url": "https://arxiv.org/abs/2512.22157", "type": "related"}, {"title": "GitHub package repository", "url": "https://github.com/NikoBiele/RayTraceHeatTransfer.jl", "type": "related"}, {"title": "JuliaCon2024 Eindhoven presentation", "url": "https://www.youtube.com/watch?v=3cIv7FOKAVM", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/MKW7DV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/MKW7DV/", "attachments": [{"title": "flyer-MKW7DV", "url": "/media/juliacon-2026/submissions/MKW7DV/resources/MKW7DV_ypr8PR8.png", "type": "related"}]}], "Alte Mensa \u2014 Atrium Maximum": [{"guid": "ea73dbe0-bdc9-5893-a419-9b31cab04a8a", "code": "7U7ERV", "id": 92359, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7U7ERV/image_EmyjJBk.webp", "date": "2026-08-12T10:00:00+02:00", "start": "10:00", "end": "2026-08-12T10:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92359-reduction-methods-for-sum-of-squares-programming-applied-to-quantum-control-problems", "url": "https://pretalx.com/juliacon-2026/talk/7U7ERV/", "title": "Reduction methods for Sum of Squares Programming applied to Quantum Control problems", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Short talk", "language": "en", "abstract": "This talk explores algebraic methods for quantum control polynomial optimization problems. We show how degeneracy, numerical stability and scalability are central issues in quantum control and how ConicSolve.jl has been extended to use face reduction and symmetry reduction (via the Wedderburn decomposition).\n\nWe close with open questions and further work so such tools may become a practical reality. We hope that this work acts as a foundation for further research, new tools and methods for realizing quantum control systems where certification and high precision are paramount.", "description": "Many quantum control problems are posed as polynomial optimization problems. Sum of Squares programming is effective at utilizing symmetric self-dual solvers due to such solvers exhibiting polynomial-time convergence and achieving solutions to high precision. Despite this solving Semidefinite programs and moreover Sum of Squares programs remain challenging. First is the computational blowup in memory and runtime cost as the problem size increases, second is the ill conditioning of solving polynomial optimization problems using the monomial basis, third is the numerical struggle that solvers must deal with.\n\nSum of squares programming is widely used in control (Lyapunov stability), robotics (trajectory optimization) and the validation and verification of safety critical systems. Global convergence and solution quality are becoming increasingly relevant in quantum control and quantum information sciences. Scaling quantum systems without compromising solution quality remains an ongoing challenge. This motivates the question; how can we adapt existing well established algebraic and computational tools in the quantum realm.\n\nIn this talk we\u2019ll focus on the fixed-time control problem of finding the control that achieves as close as possible a given target unitary at the end of a given evolution time. The QCPOP method popularized by Bondar et al., 2025 uses TSSOS.jl for solving the sum of squares programming problem. We take a different approach by proposing a computational pipeline based on the ideas discussed in Permenter, 2017 to apply face reduction on top of symmetry reduction as a preprocessing step before solving the SOS problem. This is attractive for several theoretical/practical reasons, it exploits problem structure without relaxing the original problem, these mathematical methods can act as additional layers to extend existing solvers, packages such as SymbolicWedderburn.jl (which we use) already exists for exploiting algebraic structure.\n\nAn ongoing challenge with adopting these tools is a robust solver architecture. We\u2019ll show the changes in ConicSolve.jl made to further reduce degeneracy and numerical ill-conditioning, particularly the use of matrix decompositions, equilibration and regularization methods. We hope this talk stimulates discussion and interest in further advancing Julia\u2019s ecosystem to realize and explore new algebraic and computational methods. It is without many Julia packages like OperatorScaling.jl and SymbolicWedderburn.jl that this talk and exploration of these ideas would remain just that, a theoretical ideal.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TZ8PB8", "name": "Alexander Leong", "avatar": null, "biography": "I'm a software engineer based in Brisbane, Australia. My interests are in mathematical optimization and numerical methods for engineering and science. I code only in Julia in my spare time.", "public_name": "Alexander Leong", "guid": "64e4cd4b-bf0c-5af4-9301-50c121d14ab0", "url": "https://pretalx.com/juliacon-2026/speaker/TZ8PB8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7U7ERV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7U7ERV/", "attachments": [{"title": "Presentation", "url": "/media/juliacon-2026/submissions/7U7ERV/resources/juliacon2026_Q0CgdUM.pdf", "type": "related"}, {"title": "Paper", "url": "/media/juliacon-2026/submissions/7U7ERV/resources/qcsos_paper_oddzSh8.pdf", "type": "related"}]}, {"guid": "d3a48344-8607-5db8-b6fe-11fc4323af27", "code": "XMMZE7", "id": 92466, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XMMZE7/image_esvCpFm.webp", "date": "2026-08-12T10:15:00+02:00", "start": "10:15", "end": "2026-08-12T10:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92466-giac-jl-bringing-the-giac-computer-algebra-system-to-julia-from-ffi-bindings-to-interactive-pluto-notebooks", "url": "https://pretalx.com/juliacon-2026/talk/XMMZE7/", "title": "Giac.jl: Bringing the Giac Computer Algebra System to Julia, from FFI Bindings to Interactive Pluto Notebooks", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Short talk", "language": "en", "abstract": "I present Giac.jl, a Julia interface to Giac, the open-source computer algebra system behind Xcas, GeoGebra and HP Prime. Giac.jl relies on libgiac-julia-wrapper for FFI (ie foreign function interface) bindings to the C++ library. Around it, companion packages extend Giac into the notebook world:\nMathJSON.jl handles the MathJSON interchange format, PlutoMathInput.jl provides a WYSIWYG MathField widget for Pluto, and MathJSONComputeEngineBridge.jl connects them. I demonstrate this\nworkflow live in a reactive Pluto notebook.", "description": "Giac is a mature, open-source C++ computer algebra system developed by Bernard Parisse at Universite Grenoble Alpes. It powers the CAS engine in Xcas, GeoGebra and HP Prime calculators, and provides capabilities in symbolic algebra, calculus, polynomial arithmetic, Groebner bases, and more. Previous Julia interfaces to Giac exist (by Harald Hofstaetter and by Bernard Parisse himself), but they have seen limited maintenance and do not integrate well with the broader Julia ecosystem (notably notebooks and math interchange formats).\n\n**[Giac.jl](https://github.com/s-celles/Giac.jl)** is the core package of this work. It's trying to bring the full power of GIAC to Julia, offering a high-level, Julian API for symbolic computation. Under the hood, **[libgiac-julia-wrapper](https://github.com/s-celles/libgiac-julia-wrapper)** provides the low-level FFI bindings to the giac C++ library, handling memory management and type conversions between C++ and Julia.\n\nAround this core, I have developed companion packages that extend Giac.jl into the interactive notebook world:\n\n- _[MathJSON.jl](https://github.com/s-celles/MathJSON.jl)_ -- Parse, manipulate, and serialize MathJSON expressions in Julia. MathJSON is a lightweight JSON-based interchange format for mathematical expressions created by the CortexJS project. MathJSON.jl converts between the MathJSON tree format (e.g. `[\"Add\", [\"Power\", \"x\", 2], 1]`) and Julia symbolic types.\n    \n- _[PlutoMathInput.jl](https://github.com/s-celles/PlutoMathInput.jl)_ -- A Pluto notebook widget embedding a MathLive MathField (from Arno Gourdol), a WYSIWYG math editor. Using Pluto's `@bind` macro, users type expressions in a visual editor (rendered math, not raw LaTeX) that are converted to MathJSON and bound to a Julia variable reactively.\n    \n- _[MathJSONComputeEngineBridge.jl](https://github.com/s-celles/MathJSONComputeEngineBridge.jl)_ -- The glue layer that connects the MathJSON representation to Giac.jl, coordinating the flow from visual input through parsing to symbolic evaluation and back.", "recording_license": "", "do_not_record": true, "persons": [{"code": "UV39X7", "name": "S\u00e9bastien Celles", "avatar": "https://pretalx.com/media/avatars/UV39X7_2fBGU68.webp", "biography": "Professeur agr\u00e9g\u00e9 PRAG (Higher Education) of applied physics at Universit\u00e9 de Poitiers", "public_name": "S\u00e9bastien Celles", "guid": "412fa428-39b3-5c09-8697-ba50420afc24", "url": "https://pretalx.com/juliacon-2026/speaker/UV39X7/"}], "links": [{"title": "Presentation", "url": "https://s-celles.github.io/Giac.jl_juliacon2026/", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XMMZE7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XMMZE7/", "attachments": []}, {"guid": "aecc6233-fc03-53c3-bf98-bace5d448aac", "code": "YJVVDR", "id": 92532, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YJVVDR/image_uQoGw5m.webp", "date": "2026-08-12T10:30:00+02:00", "start": "10:30", "end": "2026-08-12T10:45:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92532-certified-homotopy-and-monodromy-computation-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/YJVVDR/", "title": "Certified homotopy and monodromy computation in Julia", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Short talk", "language": "en", "abstract": "We present CertifiedHomotopyTracking.jl, a Julia package for certified homotopy tracking. Homotopy path tracking has been used as an effective method across diverse fields, but its outputs are not certified. Our package ensures correctness using interval arithmetic (via Nemo.jl). The package uses Symbolics.jl to construct systems via SLPs. Based on this, it automates monodromy group analysis through GAP integration. Finally, we discuss interaction with HomotopyContinuation.jl and Pandora.jl.", "description": "Homotopy path tracking is widely used to solve systems of nonlinear equations in scientific computing and applied mathematics. The basic idea is to start from a system whose solutions are already known and continuously deform it into the system of interest, while tracking each solution along the deformation. This process defines smooth solution paths that connect the known solutions to the target solutions.\n\nIn practice, solution paths are tracked using floating-point approximations step by step from the initial system to the target system. This approach may cause a jump to another solution path, resulting in an incorrect solution. Because detecting path jumping is generally hard, certifying only the final approximation in this case is insufficient. Certified homotopy tracking not only tracks the path but also proves that the entire path is uniquely contained within a rigorously validated compact region. It guarantees that the computed endpoint corresponds to the intended solution path.\n\nCertifiedHomotopyTracking.jl implements certified homotopy tracking using interval arithmetic to construct a rigorous compact region that uniquely contains a solution path. Systems of equations are defined using Symbolics.jl and compiled via straight-line programs, and interval arithmetic is handled through Nemo.jl.\n\nAs a key feature, the package supports the certified computation of monodromy groups for parametrized systems. Such systems frequently arise in applied mathematics, particularly in data-driven problems. When one tracks a loop in parameter space, the system's solutions are permuted, thereby inducing correspondences among them. To prove these correspondences, certified tracking along the entire loop is essential. The package employs a homotopy graph approach to compute the resulting monodromy group, and automates group-theoretic analysis through GAP.jl.\n\nThe talk consists of a brief review of backgrounds, an overview of the certified tracking algorithms, and a software demonstration. We emphasize how the package interoperates with other nonlinear algebra packages in Julia, such as HomotopyContinuation.jl and Pandora.jl, enabling users to invoke certified computation within existing workflows whenever rigorous guarantees are required.\n\nThe value of the package lies in its integration and application of multiple Julia packages for nonlinear algebra. We expect the package to provide a unified framework that broadens the practical use of certified numerical methods.", "recording_license": "", "do_not_record": false, "persons": [{"code": "Z3LARS", "name": "Kisun Lee", "avatar": null, "biography": "I am a postdoc at Clemson University.\nMy research interest is in computational algebraic geometry.", "public_name": "Kisun Lee", "guid": "16dd2c05-c377-5e03-9100-633587188af9", "url": "https://pretalx.com/juliacon-2026/speaker/Z3LARS/"}], "links": [{"title": "GitHub repo for the package (under development)", "url": "https://github.com/klee669/certified_homotopy_tracking", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YJVVDR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YJVVDR/", "attachments": [{"title": "Presentation slides", "url": "/media/juliacon-2026/submissions/YJVVDR/resources/certifiedHom_spK9aIb.pdf", "type": "related"}]}, {"guid": "341850d4-819b-5b0a-a5e6-ec1e65956ad0", "code": "S7ANGA", "id": 92576, "logo": "https://pretalx.com/media/juliacon-2026/submissions/S7ANGA/image_PHcslrW.webp", "date": "2026-08-12T10:45:00+02:00", "start": "10:45", "end": "2026-08-12T11:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92576-serialization-of-algebraic-data", "url": "https://pretalx.com/juliacon-2026/talk/S7ANGA/", "title": "Serialization of Algebraic Data", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Short talk", "language": "en", "abstract": "Due to the nature of data in computer algebra, the storage of such data requires a more sophisticated format. The mrdi file format is a JSON based file format with the necessary structure for saving and loading common types among computer algebra software. \n\nThe first implementation for serializing mrdi files has been written in the computer algebra system Oscar.jl. We present the format and discuss features of the implementation that allow users/developers to extend to new algebraic types as well as alter encodings for specific contexts.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "F3YY9J", "name": "Antony Della Vecchia", "avatar": null, "biography": "I am a PhD student at the TU Berlin in mathematics.\nI am also a maintainer of Oscar.jl", "public_name": "Antony Della Vecchia", "guid": "d6058499-7fb0-5121-b4b6-ce17aa72eb18", "url": "https://pretalx.com/juliacon-2026/speaker/F3YY9J/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/S7ANGA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/S7ANGA/", "attachments": [{"title": "slides", "url": "/media/juliacon-2026/submissions/S7ANGA/resources/serializing-_u9gTEgZ.pdf", "type": "related"}]}, {"guid": "8f904c8d-93d9-51a1-8fea-558ab3e1e95c", "code": "ZPMNJZ", "id": 92701, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZPMNJZ/image_qpb7hTZ.webp", "date": "2026-08-12T11:15:00+02:00", "start": "11:15", "end": "2026-08-12T11:45:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92701-the-oscar-computer-algebra-system", "url": "https://pretalx.com/juliacon-2026/talk/ZPMNJZ/", "title": "The OSCAR Computer Algebra System", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Long talk", "language": "en", "abstract": "[OSCAR](https://www.oscar-system.org/) is a general-purpose computer algebra system written in julia that builds on the four cornerstones GAP, polymake, Singular, and Antic (Hecke, Nemo) and has capabilities for dealing with problems in number theory, group and representation theory, tropical and polyhedral geometry, algebraic geometry, commutative algebra, non-commutative algebra, and many more areas of computer algebra. It is being developed as part of the [SFB-TRR 195 \"Symbolic Tools in Mathematics and their Application\"](https://www.computeralgebra.de/sfb/), which is supported by the German Research Foundation (DFG).\n\nIn the first part of the talk, we will give an introduction to the philosophy behind OSCAR and discuss questions like \"What is computer algebra and why do I need it?\", \"Why yet another computer algebra system?\", \"Why did you choose julia?\", and \"How does OSCAR compare to other similar tools like [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) and [HomotopyContinuation.jl](https://www.juliahomotopycontinuation.org/)?\"\nIn the second part, we show some very introductory examples of how to use OSCAR. These are not intended to be mathematically interesting at all. Instead, any listener with an introductory course of algebra should be able to easily follow the mathematics involved, so that we can put more emphasis on the key usage principles and syntax.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "QEHHUB", "name": "Lars G\u00f6ttgens", "avatar": "https://pretalx.com/media/avatars/SAHXVV_Dkw4oeX.webp", "biography": "PhD-student in Algebra at RWTH Aachen University, Germany.\nResearch in algebraic Lie theory and representation theory, with some contact points to symmetric tensor categories.\n\nCore developer of the OSCAR computer algebra system.\n\nAlso a contributor to various other julia packages, which promise to make his life easier.", "public_name": "Lars G\u00f6ttgens", "guid": "864c1fe5-ea94-52a7-9de6-a6c91436bac2", "url": "https://pretalx.com/juliacon-2026/speaker/QEHHUB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZPMNJZ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZPMNJZ/", "attachments": [{"title": "Slides", "url": "/media/juliacon-2026/submissions/ZPMNJZ/resources/2026-Mainz-J_Gc3Thyx.pdf", "type": "related"}]}, {"guid": "1737bb2f-ab60-5187-8954-f3b7d4d42622", "code": "NWDQSQ", "id": 93415, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NWDQSQ/image_GY0Pnpp.webp", "date": "2026-08-12T11:45:00+02:00", "start": "11:45", "end": "2026-08-12T12:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-93415-graphical-modeling-with-symbolic-algebra-in-oscar-jl", "url": "https://pretalx.com/juliacon-2026/talk/NWDQSQ/", "title": "Graphical Modeling with Symbolic Algebra in OSCAR.jl", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Short talk", "language": "en", "abstract": "Graphical models encode dependencies between variables through graphs whose implied statistical models obey algebraic constraints. We show how symbolic computation in the Julia package OSCAR enables causal effect estimation in such models. Using Groebner basis elimination, we resolve linear parameter identification beyond classical criteria and demonstrate a reproducible Julia workflow on a real data example.", "description": "Graphical models represent dependencies between variables through graphs and are widely used in causal inference and statistics. In linear structural equation models, the implied covariance relations define polynomial equations, making these models naturally accessible to methods from algebraic statistics.\n\nIn this talk, we present a [Julia workflow]([url](https://st-mardi.quarto.pub/gmci/chapters/notebook_gallery/notebooks/OscarID/notebook.html)) based on the computer algebra system [OSCAR]([url](https://docs.oscar-system.org/stable/)) to study parameter identifiability and causal effects in graphical models. Existing theory such as the [half-trek criterion]([url](https://doi.org/10.1214/12-AOS1012)) provides sufficient but not necessary conditions for identifiability. Using symbolic computation and Groebner basis methods, we can analyze specific models directly and determine whether parameters are generically identifiable.\n\nBuilding on recent implementations in [OSCAR]([url](https://docs.oscar-system.org/stable/Experimental/AlgebraicStatistics/introduction/)), we demonstrate how graphical models can be translated into polynomial systems, how elimination ideals can be computed, and how resulting identification formulas can be derived automatically. This allows us to resolve cases that classical criteria leave undetermined.\n\nThe workflow is illustrated on a real data example and highlights how OSCAR.jl enables reproducible and extensible algebraic-statistical analysis for causal graphical models.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UEQYMP", "name": "Leopold Mareis", "avatar": "https://pretalx.com/media/avatars/XKLZLA_UJivX3T.webp", "biography": "Leopold Mareis is a doctoral student in the field of applied mathematical statistics at the Technical University of Munich. He previously worked at the Fraunhofer Institute for Cognitive Systems IKS in the 'Reasoned AI Decisions' group. His research interest lies in the efficient estimation and uncertainty quantification of structural parameters in graphical modeling.", "public_name": "Leopold Mareis", "guid": "492605e1-6535-5a73-a613-c1ce0fbbbce1", "url": "https://pretalx.com/juliacon-2026/speaker/UEQYMP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NWDQSQ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NWDQSQ/", "attachments": [{"title": "Presentation_Mareis", "url": "/media/juliacon-2026/submissions/NWDQSQ/resources/main_jdFlZ4w.pdf", "type": "related"}]}, {"guid": "639d0487-0296-5128-a64a-beba4d5a2f50", "code": "XKMGRR", "id": 92760, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XKMGRR/image_lOb951d.webp", "date": "2026-08-12T12:00:00+02:00", "start": "12:00", "end": "2026-08-12T12:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92760-modeling-algebraic-curves-with-oscar-jl", "url": "https://pretalx.com/juliacon-2026/talk/XKMGRR/", "title": "Modeling algebraic curves with Oscar.jl", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Short talk", "language": "en", "abstract": "In 1987, Eugenii Shustin published an article titled \"A New M-Curve of Degree 8\". This article contains the construction of a polynomial in two variables, whose set of zeros forms a so-called M-curve. In this talk, we will go into detail of the problems we encountered, when attempting to reproduce this construction using Oscar.jl in Julia.", "description": "Algebraic geometry is a mathematical field that is notoriously hard for computers, since its constructions often require exact computations. The price for this exactness requirement is often paid in performance degradation.\n\nPlane algebraic curves arise as the zero set of polynomials in two variables. Every such curve subdivides the plane into a certain number of regions. An M-curve is a plane algebraic curve that maximizes this number of regions. The 1987 article by Shustin contains an algebro-geometric description of how to assemble such an M-curve, but no actual polynomial. We want to translate this article into Julia code and encounter a multitude of obstacles:\n- The end result is a smooth curve, meaning that it has no singularities, i.e. there are no points in the zero set where partial derivatives vanish simultaneously. Such a curve is robust with respect to small errors in the coefficients. However the intermediate curves have singularities which are highly sensitive against such perturbations and require exact computations.\n- How can we verify that the end result is indeed a M-curve?\n- In Oscar.jl there are many different number types we can use for the coefficients. We can even switch to fields much larger than the rational numbers. However, computation in these fields is much slower than in the rationals. Is there a way to force certain polynomial systems to have rational solutions?\n- Since many of our computations are expensive, is there a way to store intermediate results?\n- Can we visualize our intermediate curves and the end result? What different visual models are there?", "recording_license": "", "do_not_record": false, "persons": [{"code": "YSQJC3", "name": "Lars Kastner", "avatar": null, "biography": "I am Lars Kastner, a mathematician and programmer from TU Berlin.", "public_name": "Lars Kastner", "guid": "c97a83be-df2f-5c22-9599-96dbb67ec382", "url": "https://pretalx.com/juliacon-2026/speaker/YSQJC3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XKMGRR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XKMGRR/", "attachments": [{"title": "flyer-XKMGRR", "url": "/media/juliacon-2026/submissions/XKMGRR/resources/XKMGRR_ZlsOdYg.png", "type": "related"}]}, {"guid": "84901847-336d-55b0-8a2b-1c9bcc237910", "code": "7CGFWE", "id": 92890, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7CGFWE/image_WL55IUH.webp", "date": "2026-08-12T12:15:00+02:00", "start": "12:15", "end": "2026-08-12T12:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92890-solving-parametric-lmis-via-real-root-classification-a-julia-approach-to-automated-convergence-analysis", "url": "https://pretalx.com/juliacon-2026/talk/7CGFWE/", "title": "Solving parametric LMIs via real root classification: A Julia approach to automated convergence analysis", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Short talk", "language": "en", "abstract": "Parametric linear matrix inequalities (LMIs) arise in optimization and control. A key question, motivated by the automation of convergence analysis of numerical optimization schemes, is to understand how their feasibility depends on parameters. In this talk, I present an approach that turns parametric LMI feasibility into parametric polynomial equations with constraints. The resulting parameter space can then be analyzed using real root classification based on Hermite's quadratic forms. I will show how this approach is implemented in Julia (notably Nemo.jl and AlgebraicSolving.jl, with real-geometry backends where appropriate), with efficient techniques such as multivariate rational interpolation. Finally, I will show that our implementation cleanly detects regions of parameter space where convergence properties change, for parametric LMIs arising from convergence analyses of first-order optimization methods.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "D3YTKR", "name": "Weijia Wang", "avatar": null, "biography": "Weijia Wang is a PhD student in the PolSys team of LIP6, Sorbonne Universit\u00e9, and in the Sierra team at Inria Paris. His research centers on designing computer algebra-based algorithms to automate the convergence analysis of first-order optimization algorithms.", "public_name": "Weijia Wang", "guid": "b81b67a3-472f-5965-ac21-c7f934aca837", "url": "https://pretalx.com/juliacon-2026/speaker/D3YTKR/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7CGFWE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7CGFWE/", "attachments": [{"title": "Slides", "url": "/media/juliacon-2026/submissions/7CGFWE/resources/slides_Ve7IreA.pdf", "type": "related"}]}, {"guid": "9fcb906f-d0c6-558a-bf38-6fdabba80164", "code": "SMBUHF", "id": 92763, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SMBUHF/image_T8HJxx1.webp", "date": "2026-08-12T12:30:00+02:00", "start": "12:30", "end": "2026-08-12T13:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92763-using-monodromy-and-representation-theory-to-recover-symmetries-of-polynomial-systems", "url": "https://pretalx.com/juliacon-2026/talk/SMBUHF/", "title": "Using monodromy and representation theory to recover symmetries of polynomial systems", "subtitle": "", "track": "Symbolic and Numerical Methods in (Nonlinear) Algebra", "type": "Long talk", "language": "en", "abstract": "Parametric polynomial systems can be represented as dominant maps between irreducible algebraic varieties of the same dimension, where symmetries correspond to automorphisms of these maps. Galois, or monodromy, groups provide a numerical tool for detecting the existence of such symmetries in solution sets. A central computational challenge, however, is to recover explicit formulas for these automorphisms in order to better understand and more efficiently solve the systems. We combine numerical homotopy continuation with multivariate rational function interpolation to compute candidate symmetries, implemented in the Julia package [DecomposingPolynomialSystems.jl](https://github.com/MultivariatePolynomialSystems/DecomposingPolynomialSystems.jl).\n\nFor structured systems with many variables, such as minimal problems in computer vision, the resulting Vandermonde-like interpolation matrices become prohibitively large, leading to expensive nullspace computations and numerical instability. We address this by exploiting equivariance of minimal problems with respect to matrix Lie group actions. The interpolation space of bounded-degree polynomials decomposes into isotypic components as a representation of the Lie symmetry group, allowing a substantial reduction of the problem size. This representation-theoretic decomposition is implemented in the Julia package [DecomposingGroupRepresentations.jl](https://github.com/MultivariatePolynomialSystems/DecomposingGroupRepresentations.jl). Together, these tools provide a scalable Julia-based framework that integrates monodromy and computational representation theory to recover closed-form symmetries of polynomial systems.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "RUHECA", "name": "Viktor Korotynskiy", "avatar": null, "biography": "PhD in Computer Vision and Math (CTU in Prague, 2020 - 2026)", "public_name": "Viktor Korotynskiy", "guid": "f2850e9a-f813-5271-883f-fb127613d1d5", "url": "https://pretalx.com/juliacon-2026/speaker/RUHECA/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SMBUHF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SMBUHF/", "attachments": []}, {"guid": "d2ca712a-678c-5dd9-a0b1-0e7cf94833bf", "code": "YX8CHD", "id": 92843, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YX8CHD/image_bhYXNco.webp", "date": "2026-08-12T14:30:00+02:00", "start": "14:30", "end": "2026-08-12T14:45:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92843-an-offer-you-can-t-refuse-corleone-jl-flexible-direct-multiple-shooting-for-optimal-control-and-experimental-design-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/YX8CHD/", "title": "An Offer you can't refuse: Corleone.jl - Flexible direct multiple shooting for optimal control and experimental design in Julia", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "We introduce Corleone.jl, a package for solving optimization problems related to dynamic processes. It aims at leveraging the full SciML ecosystem to model, integrate, and solve the resulting nonlinear optimization problem. We showcase Corleone's integration inside scientific machine learning, how it can be used via ModelingToolkit, and recent results from academic and industrial use cases.", "description": "At its core, Corleone is a package for direct shooting methods in Julia. Its main area of application is a) optimal control, b) parameter estimation, and c) optimal experimental design for dynamic processes. As opposed to similar packages, e.g. OptimalControl.jl, the ModelingToolkit Extensions, or the recently announced BoundaryValueDiffEq.jl, Corleone.jl models everything as a Lux Layer, as such offering natural support for scientific machine learning, which is used to build an Optimization.jl problem. As such, it offers full support for all ODE and DAE integrators and all sensitivity algorithms of SciML next to the flexible choice of optimization algorithms. Additionally, the block structure resulting from multiple shooting can be directly used by specialized algorithms, e.g. BlockSQP.jl.\n\nNext to these core functionalities, we also offer optimal experimental design as its own sublibrary CorleoneOED.jl. Here the focus lies on augmenting the system with its symbolically derived sensitivity equations and methods to calculate the Fisher information matrix. \n\nThrough the talk we are planning to present: \n\n1) A brief introduction into the topic of dynamic optimization, especially the aspect of multiple shooting.\n2) Relate the structure of piecewise constant approximations in optimal control, multiple shooting (and possibly later on multi stage) problems to the computational graph of Corleone and hence to Lux.jl.\n3) Show the API on a simple example (Lotka-Volterra Fishing)\n4) Explain how optimal experimental design is an optimal control problem  [1]\n5) Showcase some recent work on OED with an industrial partner from the chemical process industry (as far as we are allowed to do :) )\n6) Showcase recent academic studies on how Corleone can be used in OED settings (see e.g. [2,3])\n7) Talk about the roadmap and planned directions of this package\n\n\n[1] C. J. Martensen, C. Plate, and S. Sager, \u201cDynamicOED.jl: A Julia package for solving optimum experimental design problems,\u201d JOSS, vol. 9, no. 98, p. 6605, Jun. 2024, doi: 10.21105/joss.06605.\n[2] C. Plate, C. J. Martensen, and S. Sager, \u201cOptimal Experimental Design for Universal Differential Equations,\u201d IEEE Trans. Automat. Contr., pp. 1\u201316, 2025, doi: 10.1109/TAC.2025.3609533.\n[3] L. Kaps et al., \u201cOptimal Experiments for Hybrid Modeling of Methanol Synthesis Kinetics,\u201d Feb. 07, 2025, Chemistry and Materials Science. doi: 10.20944/preprints202502.0422.v1.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HGT9HV", "name": "Carl Julius Martensen", "avatar": "https://pretalx.com/media/avatars/UJ8U9C_g2xCdve.webp", "biography": "Julius is currently pursuing his PhD at the Otto-von-Guericke University Magdeburg.\n\nAs a mechanical engineer with a keen interest in system identification and control, he is researching how to transform data and data-driven black-box models in readable equations.", "public_name": "Carl Julius Martensen", "guid": "25afe973-48a8-5878-96f6-a295585d44b8", "url": "https://pretalx.com/juliacon-2026/speaker/HGT9HV/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YX8CHD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YX8CHD/", "attachments": [{"title": "flyer-YX8CHD", "url": "/media/juliacon-2026/submissions/YX8CHD/resources/YX8CHD_OeyLKAM.png", "type": "related"}]}, {"guid": "db238887-fc1d-5cbc-ae20-8757aff8509b", "code": "AHVQ3V", "id": 92030, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AHVQ3V/image_101xnDh.webp", "date": "2026-08-12T14:45:00+02:00", "start": "14:45", "end": "2026-08-12T15:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92030-cross-country-macroeconomic-forecasting-using-physics-informed-neural-networks-and-universal-differential-equations-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/AHVQ3V/", "title": "Cross-Country Macroeconomic Forecasting Using Physics-Informed Neural Networks and Universal Differential Equations in Julia", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "Macroeconomic debt cycles evolve over time through interacting growth, inflation, and fiscal pressures. This study compares India, Sri Lanka, and Argentina using Physics-Informed Neural Networks (PINNs) and Universal Differential Equations (UDEs) to model continuous economic dynamics. By embedding economic structure into neural differential systems, we evaluate their ability to reconstruct national macroeconomic trajectories under sparse annual data conditions.", "description": "This project investigates whether continuous-time scientific machine learning methods can better represent such evolving economic systems. Building on the idea of long-term debt cycles, the study moves beyond a single country setting to compare India, Sri Lanka, and Argentina. Three economies with distinct fiscal histories and crisis trajectories. By analyzing multiple countries, the research expands its horizon from local model fitting to cross-country structural comparison. The central question is not about which model predicts better, but how embedding economic structure into learning systems affects stability, interpretability, and long-term trajectory reconstruction.\n\nThe methodology is based on Physics-Informed Neural Networks (PINNs) and Universal Differential Equations (UDEs). Unlike purely data-driven neural models, these approaches integrate differential equation structures and domain constraints directly into the learning process. This allows the model to respect economic relationships such as debt-growth feedback and dynamic fiscal adjustments while still learning unknown components from data. The implementation is carried out in Julia, leveraging its high-performance scientific computing ecosystem. \n\nIn particular, the project utilizes the DifferentialEquations.jl suite for numerical ODE solving, DiffEqFlux.jl and SciMLSensitivity.jl for neural differential equation training and adjoint-based gradient computation, and NeuralPDE.jl for physics-informed learning. Optimization is handled using Optimization.jl with ADAM and second-order solvers, while data processing and evaluation rely on DataFrames.jl and Statistics. Julia\u2019s amazing SciML framework enables tight coupling between symbolic differential equations and neural networks, allowing us to experiment with both structural constraints and hybrid modeling. Using annual macroeconomic indicators, the study evaluates how well these hybrid systems reconstruct historical trajectories under sparse sampling conditions.\n\nBy comparing performance across three countries, the research explores how structural constraints influence validity, error accumulation, and generalization. Ultimately, this project aims to bridge macroeconomic modeling and scientific machine learning, advancing a more disciplined and interpretable approach to modeling national economic dynamics.", "recording_license": "", "do_not_record": false, "persons": [{"code": "MN7CST", "name": "Vrishank Sai Anand", "avatar": null, "biography": "Vrishank Sai Anand is a Grade 10 student at GEMS Modern Academy in the UAE, originally from India. He was recognized as an Azeem Scholar and has received academic distinctions as a subject topper in Digital Design, Physics, Mathematics, and History. His academic interests lie at the intersection of Scientific Machine Learning (SciML), artificial intelligence, and quantitative finance, where he explores how mathematical modeling and algorithms can be used to understand complex economic systems.\n\nHe programs primarily in Julia and Python, with hands-on experience in SciML frameworks, neural differential equations, and research-oriented model development. Alongside his technical work, he has experience in UI/UX design, Flask-based web development, and deployment of computational projects, reflecting his interest in building both theoretical and practical systems.\n\nVrishank care's strongly about collaboration, communication, and impact. Through his podcast, Beyond Tomorrow - Navigating Fontiers, his school Futures Club, and his literacy initiatives in India, he has seen how powerful it can be to share knowledge and bring people together around ideas. Vrishank has authored an essay in the Journal of Future Studies on the \u201cDigital Divide,\u201d examining how book access and reading initiatives can reduce educational inequities. He has also won awards in the NGFP Young Voices Challenge in 2024 and 2025 for projects related to SDG 16 and SDG 4. \n\nBeyond academics, Vrishank is a goalkeeper for Elite Sports in the UAE and has represented his school at the UAE IB Nationals. As a captain, he values discipline, leadership, and resilience, qualities he carries into his academic pursuits. He also enjoys playing video games, which complement his interest in systems thinking and decision-making.", "public_name": "Vrishank Sai Anand", "guid": "977f272b-a010-5901-a3b9-b5518395ebf3", "url": "https://pretalx.com/juliacon-2026/speaker/MN7CST/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AHVQ3V/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AHVQ3V/", "attachments": []}, {"guid": "42388e45-ac0e-56dd-9ddb-0ba498bd17be", "code": "HMSN8Q", "id": 92704, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HMSN8Q/image_ErCQu2Z.webp", "date": "2026-08-12T15:00:00+02:00", "start": "15:00", "end": "2026-08-12T15:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92704-decisionsystems-jl-closing-the-loop-between-physics-and-decisions", "url": "https://pretalx.com/juliacon-2026/talk/HMSN8Q/", "title": "DecisionSystems.jl: Closing the Loop Between Physics and Decisions", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "Decisions often affect the dynamics of a system. DecisionSystem is a unified way of capturing both dynamics and decisions of complex systems.\nReal systems don't just evolve, they respond. DecisionSystems is a unified way of capturing physics simulation and decision-making, letting you model the full cycle: simulate dynamics, observe state, decide, act, repeat.", "description": "A vehicle braking system. A drug dosing schedule. A spacecraft attitude controller. What these share is a fundamental loop: physics evolves continuously, an agent decides discretely, and those decisions reshape the physics.\n\nDecisionSystems.jl provides an optimized API to design experiments to define these dynamics in Dyad (ModelingToolkit) and add decision parameters to iteratively update actions based on MDP, POMDP and RL environments. The library handles: state extraction, integrator stepping, action application, and belief tracking under uncertainty.\n\nUsing examples, the talk walks through the core idea, its generalizations, and what it unlocks for hybrid physical-decision systems in Julia, covering both current features and what's planned.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9ULN8Y", "name": "Venkatesh-Prasad Bhat", "avatar": null, "biography": "He builds Dyad Agent at JuliaHub. He is leveraging generative AI and scientific AI to radically change how modeling and simulation is done.\n\nHe loves to code, paint, write and trek. He likes Julia ecosystem and contributes to it.", "public_name": "Venkatesh-Prasad Bhat", "guid": "942df39f-d850-520b-ae29-ffd78f5f8554", "url": "https://pretalx.com/juliacon-2026/speaker/9ULN8Y/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HMSN8Q/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HMSN8Q/", "attachments": []}, {"guid": "0ebd240d-40a4-5452-9d24-f5f5aa1955ec", "code": "EYUQVV", "id": 92925, "logo": "https://pretalx.com/media/juliacon-2026/submissions/EYUQVV/image_yfDRxYe.webp", "date": "2026-08-12T15:15:00+02:00", "start": "15:15", "end": "2026-08-12T15:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92925-different-automatic-differentiation-algorithms-from-scimlsensitivity-jl-and-when-to-use-them", "url": "https://pretalx.com/juliacon-2026/talk/EYUQVV/", "title": "Different Automatic Differentiation algorithms from `SciMLSensitivity.jl`, and when to use them.", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "Automatic Differentiation (AD) methods present an efficient way for computing function derivatives, however the sheer amount of the implemented methods can be overwhelming. \nChosing the correct method for the task can have crucial impact on the performance[1], hence understanding the differences between them and their use cases is beneficial.", "description": "The idea for this talk arose after implementing the Gauss\u2013Kronrod adjoint. While there are many introductory talks on automatic differentiation (AD), there are few/none that focus on the practical differences between specific methods.\n\nTherefore, this talk is not intended as an introduction to AD. Instead, it will explore when and why to use forward- or reverse-mode AD, as well as when and why to choose different adjoint sensitivity algorithms.\n\nThe topic contains enough material for a 30-minute talk. However, I believe focusing on the core insights would be more engaging and a better use of the audience\u2019s time, making a 15-minute format ideal. Those interested in further details can follow Chris Rackauckas\u2019s lecture [2], read the SciMLSensitivity documentation, or reach out to me after the talk.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZVXCED", "name": "Marko Polic", "avatar": null, "biography": "I am an M.Sc. student in Mathematics in Science and Engineering at the Technical University of Munich, currently focusing on mathematical modeling as a working student at JuliaHub.\nI recently completed my Master\u2019s thesis, in which I formally introduced the Gauss\u2013Legendre and Gauss\u2013Kronrod adjoints and implemented the latter, under the supervision of Prof. Dr. Oliver Junge and Dr. Christopher Rackauckas.\n\nI discovered the Julia programming language three years ago and have used it as my primary programming language ever since. While I am passionate about many areas, my main research interests lie in mathematical modeling, adjoint sensitivity analysis, and vehicle-related systems.", "public_name": "Marko Polic", "guid": "61dc6045-33e7-58ce-80bf-b0b864cc6f94", "url": "https://pretalx.com/juliacon-2026/speaker/ZVXCED/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/EYUQVV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/EYUQVV/", "attachments": []}, {"guid": "cc1371b1-219b-5157-bba7-05eef0b0a78b", "code": "DG33PK", "id": 92735, "logo": "https://pretalx.com/media/juliacon-2026/submissions/DG33PK/image_qVkoxSA.webp", "date": "2026-08-12T15:45:00+02:00", "start": "15:45", "end": "2026-08-12T16:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92735-discovering-governing-equations-for-neural-populations-pem-ude-with-multiple-shooting-for-chaotic-brain-dynamics", "url": "https://pretalx.com/juliacon-2026/talk/DG33PK/", "title": "Discovering Governing Equations for Neural Populations: PEM-UDE with Multiple Shooting for Chaotic Brain Dynamics", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "Chaotic neural dynamics resist equation discovery because parameter sensitivity creates intractable optimization landscapes. Using the SciML ecosystem, we combine prediction-error methods with universal differential equations (PEM-UDE) and multiple shooting to tame chaos during learning. In spiking networks, we derive novel mean-field equations for sparse cortical connectivity that predict frequency shifts and synchrony patterns, validated by intracranial recordings.", "description": "Understanding how neural populations generate brain rhythms requires equations that bridge the biophysics of individual neurons with macroscopic network dynamics. Next-generation neural mass models (NGNMMs) achieve this analytically but rely on the assumption of all-to-all connectivity between neurons, a condition that holds only in deep brain structures like the hippocampus but fails badly in the cortex, where connectivity is typically 1-5%. Removing this assumption analytically has proven contentious, with competing approaches yielding inconsistent results.\n\nWe sidestep the analytical difficulty entirely by learning the governing equations directly from data using scientific machine learning. Our PEM-UDE approach combines the prediction-error method with universal differential equations to discover equations from chaotic neural time series. The key insight is that the PEM correction removes the sensitive dependence on parameters that makes chaotic systems intractable for standard UDE training. This effectively smooths the loss landscape while preserving the correct solution. After training, we extract symbolic equations via sparse regression (STLSQ) or genetic algorithm methods.\n\nWe already demonstrated PEM-UDE on benchmark chaotic systems (Roessler attractor, Petrzela-Polak circuit with 5x noise), and applied it to learn novel NGNMMs from populations of Izhikevich neurons with connectivity ranging from 5% to 100% (arXiv:2507.03631). The learned equations include correction terms that capture how sparsity modifies firing rate and voltage dynamics. Here, we extend the original method by combining PEM with multiple shooting (arXiv:2602.21588) to handle longer time series, regime transitions, and mixed excitatory-inhibitory populations with varying neuron parameters.\n\nWe will present and discuss how neural network architecture, prediction error, and multiple-shooting parameters affect training performance (speed and accuracy).  Our longer-term goal is to develop an automated tool chain for fitting data to UDEs.\n\nFinally, we will present progress on analytically flattening a more complex neural circuit (Nature Communications 17(390)2026) using UDEs.  This work is motivated by the inability of large-scale neuron simulations to be used for parameter fitting of experimental data.  By reducing the dimensionality of the system (going from 10,000s to 10s of states), we should be able to parameter-fit our models to individual patients.", "recording_license": "", "do_not_record": false, "persons": [{"code": "KAK8Z3", "name": "Helmut Strey", "avatar": "https://pretalx.com/media/avatars/EVVJZ7_ltl5k6w.webp", "biography": "I am an Associate Professor at the Biomedical Engineering Department at Stony Brook University.  I also have affiliate positions at the Martinos Center for Biomedical Imaging at MGH/Harvard Medical School and at JuliaLab at MIT/CSAIL.  I am currently leading the development of Neuroblox.jl, a Julia package to design, simulate, and analyze dynamic models of the brain.  Our effort is built on top of ModelingToolkit.jl, but we are also developing our own, and sometimes more efficient, algorithms to build graphs of dynamical motives (we just released GraphDynamics.jl", "public_name": "Helmut Strey", "guid": "c9053a3e-1e4a-52d9-985e-5b9b993f578e", "url": "https://pretalx.com/juliacon-2026/speaker/KAK8Z3/"}, {"code": "WUWQQ3", "name": "Chris Rackauckas", "avatar": "https://pretalx.com/media/avatars/WUWQQ3_otHw1Wk.webp", "biography": "Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.", "public_name": "Chris Rackauckas", "guid": "5ecf5886-9c68-55ca-8dcf-c4f85742c1ca", "url": "https://pretalx.com/juliacon-2026/speaker/WUWQQ3/"}, {"code": "RNDHRR", "name": "Anthony Chesebro", "avatar": null, "biography": null, "public_name": "Anthony Chesebro", "guid": "bac6ee1e-65ee-57b3-bf10-998a3a08a236", "url": "https://pretalx.com/juliacon-2026/speaker/RNDHRR/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/DG33PK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/DG33PK/", "attachments": [{"title": "flyer-DG33PK", "url": "/media/juliacon-2026/submissions/DG33PK/resources/DG33PK_1ynV9h4.png", "type": "related"}]}, {"guid": "9527e5fa-5744-53fc-859a-2dd110d9ed13", "code": "YQXSKZ", "id": 92521, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YQXSKZ/image_GbMOOzB.webp", "date": "2026-08-12T16:15:00+02:00", "start": "16:15", "end": "2026-08-12T16:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92521-optimizing-race-car-track-times-in-dyad", "url": "https://pretalx.com/juliacon-2026/talk/YQXSKZ/", "title": "Optimizing race car track times in Dyad", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "In this talk we will present how DyadModelOptimizer is solving free final time problems using ModelingToolkit, BoundaryValueDiffEq and OptimizationMadNLP, showing the full julia stack that powers the Dyad analyses. Specifically we examine the solution in the context of minimum lap time optimization for a race car. The solution assumes a continuous lap optimizing throttle and braking under dynamical constraints.\u00a0 We formulate the problem as a nonlinear optimal control problem with free terminal time, where the objective is to minimize total lap time subject to coupled vehicle dynamics, tire force limits, and path constraints along a prescribed track centerline. The vehicle model captures longitudinal and lateral dynamics, load transfer effects, and tire saturation through nonlinear algebraic relationships, resulting in a differential-algebraic system expressed symbolically. The talk walks through the entire process end to end: building the symbolic model, converting it into a boundary value formulation, choosing a discretization strategy, assembling the nonlinear program, and configuring the solver. We will also share practical lessons on mesh refinement, scaling for numerical stability, and what solve times and convergence actually look like in practice.", "description": "The support for optimal control problems in the SciML ecosystem has been expanding recently, with new additions for both external solvers like InfiniteOpt or CasADi and also the more recently added support for BoundaryValueDiffEq in ModelingToolkit.\n\nThe new `BVPProblem` interface is allows users to specify constraints and cost functions symbolically, which are then internally used for the formulation of the problem in terms of generalized boundary value problems. Moreover, the parameters of the system can also be tuned while solving, by appending the tunable parameters to the mesh points that we are solving for, allowing us to find both the required trajectory and the optimal parameters at the same time.\n\nOne of the advantages of using the generalized boundary problem solvers over usual collocation methods is that we can use an adaptive mesh instead of a discretization that uses a fixed time step.\n\nThe boundary value problem is formulated internally using an optimization problem when tuning parameters. The BVP solvers expose an Optimization.jl compatible interface, where we can use solvers such as Ipopt (via OptimizationIpopt) or MadNLP (via OptimizationMadNLP). The OptimizationMadNLP package is also a more recent addition to the Optimziation ecosystem, exposing an advanced and high performance constrained optimization solver in pure julia.\n\nAll of this describes the full julia stack that powers DyadModelOptimizer and we will show how this performs on a concrete problem by targeting the optimization of the race car track times.", "recording_license": "", "do_not_record": false, "persons": [{"code": "C7BG78", "name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "avatar": null, "biography": "Software Eng. at JuliaHub & PhD student at University of Bucharest.", "public_name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "guid": "3e31b822-d61c-5033-b95f-3f20ed7eccbc", "url": "https://pretalx.com/juliacon-2026/speaker/C7BG78/"}, {"code": "9YHUWL", "name": "Rajeev Voleti", "avatar": "https://pretalx.com/media/avatars/GCQ8Q3_qw9dXjr.webp", "biography": "Rajeev holds a Ph.D. in Aerospace Engineering with expertise in dynamical systems, controls, and numerical optimization. At JuliaHub, he works on advanced modeling and simulation workflows using Dyad, ModelingToolkit and the broader Julia ecosystem, focusing on large-scale dynamical systems and optimal control.", "public_name": "Rajeev Voleti", "guid": "3158b35e-5488-5636-9c52-59640cb52514", "url": "https://pretalx.com/juliacon-2026/speaker/9YHUWL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YQXSKZ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YQXSKZ/", "attachments": []}, {"guid": "76573801-dfa3-505c-b627-2ebfe449d31a", "code": "UHMHQX", "id": 93467, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UHMHQX/image_vFJSeKv.webp", "date": "2026-08-12T16:45:00+02:00", "start": "16:45", "end": "2026-08-12T17:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-93467-scientific-machine-learning-for-geophysical-modelling-inversion-and-uncertainty-quantification", "url": "https://pretalx.com/juliacon-2026/talk/UHMHQX/", "title": "Scientific Machine Learning for Geophysical Modelling, Inversion and Uncertainty Quantification", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "This talk discusses two complementary directions in scientific machine learning for geophysics. The first uses DeepONet surrogates to accelerate magnetotelluric forward modelling and transdimensional probabilistic inversion, making uncertainty analysis more practical. The second uses implicit neural representations for three-dimensional gravity inversion, where the subsurface model is learned under physics-based machine learning.", "description": "The first direction concerns neural networks trained as surrogates for geophysical forward problems. I will present a workflow in which a DeepONet is trained to emulate one-dimensional magnetotelluric responses and is then used within a transdimensional probabilistic inversion scheme. By replacing repeated evaluations of the full forward solver with a learned surrogate, the method makes large-scale posterior exploration and uncertainty analysis substantially more practical. The example shows how surrogate modelling can reduce computational cost while preserving the level of accuracy needed for probabilistic inversion.\n\nThe second direction concerns the use of neural networks as the inversion model itself. In this setting, the subsurface is represented as an implicit neural field constrained by the governing physics rather than by a fixed voxel parameterisation. I will illustrate this with three-dimensional gravity inversion, where implicit neural representations recover both smooth variations and sharp contrasts while reducing the reliance on explicit depth weighting. The gravity examples show that neural-field parameterisations can produce geologically plausible models while remaining compact and flexible.", "recording_license": "", "do_not_record": true, "persons": [{"code": "CML3LL", "name": "Pankaj K Mishra", "avatar": "https://pretalx.com/media/avatars/LNCJEY_aRcZokQ.webp", "biography": "Pankaj K Mishra is a Senior Scientist (Geophysics) at Geological Survey of Finland. \nFor more info visit: [https://pankajkmishra.github.io/](https://pankajkmishra.github.io/)", "public_name": "Pankaj K Mishra", "guid": "b303a917-727a-5779-ac2a-476ebdf2b87c", "url": "https://pretalx.com/juliacon-2026/speaker/CML3LL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UHMHQX/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UHMHQX/", "attachments": []}, {"guid": "516cb183-cb8d-547b-aae2-1db3f4892e58", "code": "3GYGWQ", "id": 92388, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3GYGWQ/image_oTPCZBP.webp", "date": "2026-08-12T17:00:00+02:00", "start": "17:00", "end": "2026-08-12T17:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92388-type-stable-symbolic-computation", "url": "https://pretalx.com/juliacon-2026/talk/3GYGWQ/", "title": "Type-stable Symbolic Computation", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "Symbolic computation is inherently very dynamic. It needs to support representing arbitrary function calls, on arbitrary symbolic types. Older versions of Symbolics.jl and SymbolicUtils.jl were notoriously type-unstable. This talk goes over the methods used and challenges involved in making the latest major versions of those packages type-stable and precompilation-friendly. The techniques and insights discussed are generally applicable to other packages as well.", "description": "There are various different approaches taken to represent programs. Data representations can vary from SSA-IR to expression-trees. Some tools may choose to specialize on very specific types of expressions - polynomial algebra, or real numbers. In contrast, SymbolicUtils.jl (the core of the Symbolics.jl CAS) aims to be a framework for representing arbitrary symbolic expressions in arbitrary algebras, where different parts of the expression can have different types. It even allows (in fact, requires) the ability to represent different types of expressions in different ways. For example, the expressions for associative-commutative `+` and `*` uses a completely different representation than the standard tree-based approach. So how is all this made possible, while retaining type-stability and precompiling common workflows?\n\nThe answer lies in the recent SymbolicUtils version 4 release. Older versions had significant historical baggage, and were victims of the tooling available when they were written. Since then, the language and package ecosystem have grown in leaps and bounds. The new release rewrote most of the core package to use modern alternatives to many core packages, with an emphasis on benchmarking and performance. This talk walks through the design of SymbolicUtils today, contrasting it with older versions and illustrating how we solved the innumerable problems along the way. In doing so, it also highlights the idiosyncrasies of the language, the various useful tooling for debugging type-stability and precompilation, and pain-points that make such investigation difficult.", "recording_license": "", "do_not_record": false, "persons": [{"code": "YJXBHC", "name": "Aayush Sabharwal", "avatar": null, "biography": "Software Engineer at JuliaHub\n\nLead developer of ModelingToolkit.jl and underlying symbolic infrastructure", "public_name": "Aayush Sabharwal", "guid": "0f0d59dc-e71e-5aae-947d-6c23fbe60f5d", "url": "https://pretalx.com/juliacon-2026/speaker/YJXBHC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3GYGWQ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3GYGWQ/", "attachments": []}, {"guid": "067ca3ce-458a-50b9-96aa-c7fb5de776e6", "code": "AHGR7C", "id": 89579, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AHGR7C/image_1SzKukc.webp", "date": "2026-08-12T17:15:00+02:00", "start": "17:15", "end": "2026-08-12T17:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-89579-what-is-the-best-ode-solver-for-your-problem-a-detailed-walk-through-differentialequations-jl", "url": "https://pretalx.com/juliacon-2026/talk/AHGR7C/", "title": "What is the best ODE solver for your problem? A detailed walk through DifferentialEquations.jl", "subtitle": "", "track": "Methods and Applications of Scientific Machine Learning (SciML)", "type": "Short talk", "language": "en", "abstract": "There are hundreds of ODE solvers in DifferentialEquations.jl. Which is the best one for your problem? In this talk we go through the many classes and types of solvers and build a map to help you understand when to use various choices. We start by highlighting the basics, the core solvers that tend to do well for all problems, and then start to showcase more specialized methods and detail when they are likely to be improvements.", "description": "For most people, you use the default solver solve(prob), and in most cases, this is pretty good. But in some cases, you may choose a solver. Some choices are generic: Tsit5(), FBDF(), etc. that tend to just do well in general. But then it can get detailed:\n\n* You know about stiff and non-stiff equations, but what about semi-stiffness where your eigenvalues are dominated by real parts? Try to ROCK methods. \n* What about stiff equations of 3-200 equations on multicore devices? Try the implicit extrapolation methods. \n* What about non-stiff equations which are smooth and able to be symbolically analyzed? Try the adaptive Taylor methods\n* What about highly stiff equations which are real valued and require very high precision? Try the adaptive Radau methods.\n\nEtc. etc. This will walk through the space with benchmarks, a high level intuition behind the method, and give an idea of how much these optimizations may give over using the generic methods. By the end you should feel more comfortable going into the details of the DifferentialEquations.jl library to further optimize the choices for your specific code.", "recording_license": "", "do_not_record": false, "persons": [{"code": "WUWQQ3", "name": "Chris Rackauckas", "avatar": "https://pretalx.com/media/avatars/WUWQQ3_otHw1Wk.webp", "biography": "Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.", "public_name": "Chris Rackauckas", "guid": "5ecf5886-9c68-55ca-8dcf-c4f85742c1ca", "url": "https://pretalx.com/juliacon-2026/speaker/WUWQQ3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AHGR7C/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AHGR7C/", "attachments": []}]}}, {"index": 4, "date": "2026-08-13", "day_start": "2026-08-13T04:00:00+02:00", "day_end": "2026-08-14T03:59:00+02:00", "rooms": {"Tent \u2014 RW1": [{"guid": "84a4e3c5-2730-58d2-a0eb-c1cfada8245a", "code": "PEZTQJ", "id": 93210, "logo": null, "date": "2026-08-13T08:30:00+02:00", "start": "08:30", "end": "2026-08-13T08:45:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93210-building-aeolus-with-julia-from-wildfires-to-weather-machines-sponsor-talk-from-aeolus-labs", "url": "https://pretalx.com/juliacon-2026/talk/PEZTQJ/", "title": "Building Aeolus with Julia - From Wildfires to Weather Machines (Sponsor Talk from Aeolus Labs)", "subtitle": "", "track": "General", "type": "Platinum Sponsor Talk", "language": "en", "abstract": "Sponsor Talk from Aeolus Labs by Mason Lee from Aeolus Labs.", "description": "Aeolus Labs is a San Francisco-based weather intelligence startup and a Platinum Sponsor of JuliaCon Global 2026.", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/PEZTQJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/PEZTQJ/", "attachments": []}, {"guid": "aebc7250-bb0d-5c84-ad2d-679ae99b772c", "code": "YRYY9T", "id": 93045, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YRYY9T/image_1daakdZ.webp", "date": "2026-08-13T08:45:00+02:00", "start": "08:45", "end": "2026-08-13T09:45:00+02:00", "duration": "01:00", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93045-julia-for-quantum-software-lessons-from-paulipropagation-jl", "url": "https://pretalx.com/juliacon-2026/talk/YRYY9T/", "title": "Julia For Quantum Software: Lessons from PauliPropagation.jl", "subtitle": "", "track": "General", "type": "Keynote", "language": "en", "abstract": "Quantum computing progress depends as much on software as on hardware. In this keynote, we\u2019ll start with a practical view of how high-quality code supports the development and use of quantum devices\u2014through simulation, compilation, verification, benchmarking, and control. We'll also stress the value of state of the art classical methods to delineate where a quantum computer is genuinely required, versus where well-designed classical software is the right (and often faster) choice. We will then zoom in on PauliPropagation.jl, a Julia package we have been developing for efficiently simulating quantum circuits. We will outline the core abstractions and implementation details in the package, and what problems it is meant to make easy. A central thread will be \"why Julia\". Beyond performance, Julia lets us offer a fully extensible package with custom gates, data structures, and evolving types. We\u2019ll end with an honest account of building Julia tools as a scientist: what has worked well, what has been surprisingly hard, and what we have learned about presenting research software to a community that often defaults to Python expectations.", "description": "Quantum computing progress depends as much on software as on hardware. In this keynote, we\u2019ll start with a practical view of how high-quality code supports the development and use of quantum devices\u2014through simulation, compilation, verification, benchmarking, and control. We'll also stress the value of state of the art classical methods to delineate where a quantum computer is genuinely required, versus where well-designed classical software is the right (and often faster) choice. We will then zoom in on PauliPropagation.jl, a Julia package we have been developing for efficiently simulating quantum circuits. We will outline the core abstractions and implementation details in the package, and what problems it is meant to make easy. A central thread will be \"why Julia\". Beyond performance, Julia lets us offer a fully extensible package with custom gates, data structures, and evolving types. We\u2019ll end with an honest account of building Julia tools as a scientist: what has worked well, what has been surprisingly hard, and what we have learned about presenting research software to a community that often defaults to Python expectations.", "recording_license": "", "do_not_record": false, "persons": [{"code": "FY9HF8", "name": "Zo\u00eb Holmes", "avatar": "https://pretalx.com/media/avatars/RT8W3F_LVOeHAH.webp", "biography": "Zo\u00eb Holmes received in 2015 her MPhil degree in Physics and Philosophy from the University of Oxford. In 2016 she obtained her MRes (Master of Research) from the Imperial College London, where in 2019 she got her PhD in quantum thermodynamics. In 2020 she started as a Postdoctoral Researcher at Los Alamos National Laboratory (USA) working on quantum algorithms and quantum machine learning. In 2021 she became the Mark Kac Fellow at Los Alamos National Lab. Since August 2022 she is Tenure Track Assistant Professor of Physics at EPFL where her research ranges from quantum algorithms and quantum learning theory to classical methods to simulate quantum systems.", "public_name": "Zo\u00eb Holmes", "guid": "195216c4-c6b0-57b5-91a4-d3072bc7e354", "url": "https://pretalx.com/juliacon-2026/speaker/FY9HF8/"}, {"code": "XNWA3X", "name": "Manuel Rudolph", "avatar": null, "biography": null, "public_name": "Manuel Rudolph", "guid": "1e167cbc-c3a4-575b-a640-979a86dd37f4", "url": "https://pretalx.com/juliacon-2026/speaker/XNWA3X/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YRYY9T/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YRYY9T/", "attachments": []}, {"guid": "57d35ca1-f35c-58c6-8469-b3edf9477094", "code": "BZEYUC", "id": 93355, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BZEYUC/image_IPFUKpJ.webp", "date": "2026-08-13T10:00:00+02:00", "start": "10:00", "end": "2026-08-13T10:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93355-what-s-new-in-the-julia-extension-for-vs-code", "url": "https://pretalx.com/juliacon-2026/talk/BZEYUC/", "title": "What's new in the Julia extension for VS Code", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "We will do a deep dive how we modernized the core of the language server with an incremental computation engine, multi environment support and runtime analysis features. We will also highlight how these features are now easily exposed to command line and other users. Furthermore, we will introduce the new test item runner system that supports efficient parallel test execution and integrates into various workflows.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "8HWZWB", "name": "Sebastian Pfitzner", "avatar": null, "biography": "Sebastian is a software engineer at JuliaHub focusing on tooling around Julia, including the JuliaHub platform and the Julia extension for VS Code, as well as various other contributions to the Julia ecosystem.", "public_name": "Sebastian Pfitzner", "guid": "3e58ac79-7b6a-5975-b6f5-ccdadd9e6a1d", "url": "https://pretalx.com/juliacon-2026/speaker/8HWZWB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BZEYUC/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BZEYUC/", "attachments": []}, {"guid": "773eacfe-d771-5157-ab8d-b66b37c919ab", "code": "U39FKW", "id": 92793, "logo": "https://pretalx.com/media/juliacon-2026/submissions/U39FKW/image_rPiUll7.webp", "date": "2026-08-13T10:30:00+02:00", "start": "10:30", "end": "2026-08-13T11:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92793-julialowering-jl-provenance-automatic-hygiene-and-tooling", "url": "https://pretalx.com/juliacon-2026/talk/U39FKW/", "title": "JuliaLowering.jl: Provenance, automatic hygiene, and tooling", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "The first time you implement lowering, it takes parsed code, expands macros, and compiles it to linear, untyped IR. But then you get users. Users have questions like \"can I figure out what code this IR came from so I can make essential tools like Revise.jl?\" and \"can we write macros without so much manual escaping?\" and \"why are errors after lowering so cryptic?\" and \"not a question, but Revise works now because I've written a program that correctly reverses lowering about 80% of the time.\"\n\nThis talk is about the second time you implement lowering.", "description": "JuliaLowering is an ongoing rewrite of macro expansion and lowering, the first few passes of the compiler that run immediately after parsing. Roughly, lowering analyzes and simplifies the symbolic structure of the code without referring to type information or global state.\n\nThe JuliaCon 2024 talk, \"Dude, where's my code?\", provided motivation and sketched a plan for rewriting the Julia compiler frontend, along with demonstrating some early progress. This talk follows by discussing the outcomes of that work. Covered topics will include technical details of the new lowering implementation, project status, lessons learned, and all the current or planned tooling improvements that make such a nontrivial rewrite worth doing.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7TLADW", "name": "Claire Foster", "avatar": null, "biography": "Claire is a long time enthusiastic user of Julia and enjoys contributing to various\npackages across the open source ecosystem, Julia standard libraries and\ncompiler. She love hearing about people's fascinating technical computing\nadventures of all types! Find her at https://github.com/c42f", "public_name": "Claire Foster", "guid": "a9c61fc2-8a01-582b-9407-e686fac61bd3", "url": "https://pretalx.com/juliacon-2026/speaker/7TLADW/"}, {"code": "3TGPJG", "name": "Em Chu", "avatar": null, "biography": "Compiler engineer at JuliaHub", "public_name": "Em Chu", "guid": "1319800f-4336-520f-9cab-433f035f411e", "url": "https://pretalx.com/juliacon-2026/speaker/3TGPJG/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/U39FKW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/U39FKW/", "attachments": []}, {"guid": "81a3b3d4-6eed-552c-bfcb-2d75240957e1", "code": "LJVPDR", "id": 92585, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LJVPDR/image_suzbuIM.webp", "date": "2026-08-13T11:15:00+02:00", "start": "11:15", "end": "2026-08-13T11:30:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92585-uncertainty-in-eeg-topoplots-designing-and-testing-new-visualizations-with-unfoldmakie-jl", "url": "https://pretalx.com/juliacon-2026/talk/LJVPDR/", "title": "Uncertainty in EEG Topoplots: Designing and Testing New Visualizations with UnfoldMakie.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "EEG topoplots are a central visualization tool in computational neuroscience and biological signal analysis. However, they typically display only mean effects while omitting uncertainty arising from subjects, trials, and model variability. Our qualitative user study with domain experts shows that researchers consider uncertainty visualization essential for interpretation, yet report lacking appropriate tools and established methods to implement it in practice.\n\nIn this talk, I present six (seven) uncertainty visualization prototypes developed in UnfoldMakie, a Julia-based ecosystem for regression-based EEG analysis. Several approaches, such as bivariate and value-suppressing topoplots, introduce entirely new visualization strategies. Some are already available, while others are in active development.\n\nWe are currently conducting a quantitative user study to systematically assess which of these plots most effectively support accuracy and interpretability in typical EEG analysis tasks. By empirically comparing these designs, we aim to identify best practices rather than proposing yet another visualization variant.\n\nAs tool developers, we argue that enabling appropriate uncertainty representations is a responsibility: without accessible methods, researchers lack the means to communicate variability, which directly impacts research integrity and reproducibility in computational biology.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "VGGFQA", "name": "Vladimir Mikheev", "avatar": "https://pretalx.com/media/avatars/8AWCAG_mLNTnXr.webp", "biography": "I work at University of Stuttgart and do visualizations for neuroscience.", "public_name": "Vladimir Mikheev", "guid": "c8fc9dc6-60d6-54f9-8229-c83145d4b543", "url": "https://pretalx.com/juliacon-2026/speaker/VGGFQA/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LJVPDR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LJVPDR/", "attachments": [{"title": "flyer-LJVPDR", "url": "/media/juliacon-2026/submissions/LJVPDR/resources/LJVPDR_QGtonni.png", "type": "related"}]}, {"guid": "c7f996ed-4b0d-545e-9f4e-7e16fa73e30a", "code": "QLXWSB", "id": 92702, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QLXWSB/image_WGdlZ3A.webp", "date": "2026-08-13T11:30:00+02:00", "start": "11:30", "end": "2026-08-13T12:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92702-jetls-bringing-julia-s-runtime-into-language-server-analysis", "url": "https://pretalx.com/juliacon-2026/talk/QLXWSB/", "title": "JETLS: Bringing Julia\u2019s runtime into language-server analysis", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "At JuliaCon 2025, we introduced [JETLS](https://github.com/aviatesk/JETLS.jl), built on byte-precise lowering and abstract interpretation. This year, we focus on the core design behind it: loading parts of user code to reuse Julia's macro handling and compiler type inference. We compare this runtime-integrated approach with the overhauled LanguageServer.jl/JuliaWorkspaces.jl stack and demo its progress on real-world code.", "description": "At JuliaCon 2025, we introduced [JETLS.jl](https://github.com/aviatesk/JETLS.jl), then an early-alpha Julia language server built on [JET.jl](https://github.com/aviatesk/JET.jl)'s abstract interpretation and [JuliaLowering.jl](https://github.com/JuliaLang/julia/tree/master/JuliaLowering)'s byte-precise, provenance-tracking lowering. One year later, this talk goes beyond a progress report to examine the core design that makes this compiler-grounded analysis possible.\n\nJETLS selectively loads parts of the target program into its analysis process. This runtime integration enables JuliaLowering to execute the program\u2019s actual macros while preserving byte-precise source provenance, and JET to reuse Julia\u2019s compiler inference to derive type information for source-level developer tooling.\n\nWe compare this design in depth with the [LanguageServer.jl](https://github.com/julia-vscode/LanguageServer.jl)/[JuliaWorkspaces.jl](https://github.com/julia-vscode/JuliaWorkspaces.jl) stack, which has undergone a major overhaul over the past year. Rather than presenting JETLS simply as a replacement, we place the two approaches on a spectrum of runtime integration: JETLS aims to provide precise information about macro expansion and lowering, together with compiler-inferred types, while the largely static approach prioritizes responsiveness and safety and benefits from greater production maturity.\n\nThese architectural differences become concrete in a live demo using real-world code from CSV.jl and JuliaFormatter.jl. We show how JETLS's runtime-integrated design translates into practical editor features, most of which were either early prototypes at last year's talk or did not yet exist.\n\nFinally, we summarize JETLS's current feature coverage, installation and environment isolation, and editor setup. We also briefly show how the same analysis is exposed through a CLI for CI and AI-agent workflows, showcasing agentic bugfixes drawn from a [Julia Base audit with JETLS](https://github.com/JuliaLang/julia/issues/62526).\nTime permitting, we share performance measurements before outlining future work.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UZCDZY", "name": "Shuhei Kadowaki", "avatar": "https://pretalx.com/media/avatars/8BZR7G_HyyVxGs.webp", "biography": "SRE at JuliaHub, Inc. Working on the Julia compiler. Creator of JET.jl.", "public_name": "Shuhei Kadowaki", "guid": "8b4056e7-0762-5964-9743-d3174af46064", "url": "https://pretalx.com/juliacon-2026/speaker/UZCDZY/"}, {"code": "8HWZWB", "name": "Sebastian Pfitzner", "avatar": null, "biography": "Sebastian is a software engineer at JuliaHub focusing on tooling around Julia, including the JuliaHub platform and the Julia extension for VS Code, as well as various other contributions to the Julia ecosystem.", "public_name": "Sebastian Pfitzner", "guid": "3e58ac79-7b6a-5975-b6f5-ccdadd9e6a1d", "url": "https://pretalx.com/juliacon-2026/speaker/8HWZWB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QLXWSB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QLXWSB/", "attachments": []}, {"guid": "d2c5babf-f2bd-511c-be86-2b77c937a540", "code": "TNVVU8", "id": 92620, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TNVVU8/image_zIqeKpE.webp", "date": "2026-08-13T12:00:00+02:00", "start": "12:00", "end": "2026-08-13T12:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92620-smallcollections-jl-variable-length-collections-that-don-t-allocate", "url": "https://pretalx.com/juliacon-2026/talk/TNVVU8/", "title": "SmallCollections.jl: variable-length collections that don't allocate", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "[SmallCollections.jl](https://github.com/matthias314/SmallCollections.jl) provides variable-length vectors, sets and dictionaries which in their immutable versions don't allocate memory. Using these types often results in significant speed-ups for performance-critical code. I will illustrate this with the package [SmallCombinatorics.jl](https://github.com/matthias314/SmallCombinatorics.jl).", "description": "Many users are familiar with the package [StaticArrays.jl](https://github.com/JuliaArrays/StaticArrays.jl). It defines fixed-size arrays which in their immutable version don't allocate memory. [SmallCollections.jl](https://github.com/matthias314/SmallCollections.jl) provides vectors, sets and dictionaries with the same behavior, but whose length is variable up to some user-defined limit. The limit should be small enough for the data to fit into one or a few processor registers. Operations from `Base` have dedicated fast methods for the new types; this includes vector indexing and checked arithmetic.\n\nIn this talk I will give an overview of the various types defined in the package and how to use them efficiently:\n- `SmallVector` and `MutableSmallVector` with variable length,\n- `FixedVector` and `MutableFixedVector` with fixed length (like `SVector` and `MVector`),\n- `PackedVector` with fewer than 8 bits per element,\n- `SmallBitSet` for sets based on bitmasks,\n- `SmallDict` and `SmallSet` plus their mutable versions.\n\nAs an example application I will briefly discuss the package [SmallCombinatorics.jl](https://github.com/matthias314/SmallCombinatorics.jl), which is based on SmallCollections.jl. Compared to [Combinatorics.jl](https://github.com/JuliaMath/Combinatorics.jl), the functions in SmallCombinatorics.jl are often 1-3 orders of magnitude faster.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BY8DLV", "name": "Matthias Franz", "avatar": null, "biography": "Professor of Mathematics at the University of Western Ontario, with interest in Topology and Computer Algebra.", "public_name": "Matthias Franz", "guid": "cfdcc24a-fd67-5893-87a9-d417c3af60fc", "url": "https://pretalx.com/juliacon-2026/speaker/BY8DLV/"}], "links": [{"title": "GitHub repository", "url": "https://github.com/matthias314/SmallCollections.jl", "type": "related"}, {"title": "Documentation", "url": "https://matthias314.github.io/SmallCollections.jl/", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TNVVU8/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TNVVU8/", "attachments": []}, {"guid": "a72c586a-7da2-50f0-9e82-e0f7465c2653", "code": "9WAZ7B", "id": 92748, "logo": "https://pretalx.com/media/juliacon-2026/submissions/9WAZ7B/image_2fvGzGQ.webp", "date": "2026-08-13T12:30:00+02:00", "start": "12:30", "end": "2026-08-13T13:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92748-thermal-fluid-modeling-in-dyad", "url": "https://pretalx.com/juliacon-2026/talk/9WAZ7B/", "title": "Thermal-Fluid Modeling in Dyad", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "The Dyad language and ecosystem provides a modern approach to model-based systems engineering leveraging the symbolic-numeric advances of ModelingToolkit.jl. This talk presents the Dyad FluidComponents and HVACComponents libraries which are a suite of pre-built models for 1D thermal-fluid flow. We present unique features of Dyad such as path variables that simplify modeling, discuss approaches to translating existing libraries and conclude by providing industrial examples.", "description": "We introduce Dyad as a feature-rich language for fluid system modeling that simplifies specification of distinct fluid circuits, enables hierarchical media and physical property modeling, and allows for combining declarative and imperative semantics. \n\nNext, we discuss approaches for translating existing fluid libraries. We present an experimental transpiler that allows automatic source-to-source conversion of Modelica models to Dyad. \n\nWe present FluidComponents (a generic library for 1D thermal-fluid flow) and HVACComponents (a specialized high-performance library for refrigeration systems). We show the numerical techniques necessary to solve the challenges of modeling two-phase, compressible, viscous, turbulent flows with heat transfer. \n\nLastly, we demonstrate industrial applications such as vapor compression cycles for refrigeration and battery chiller models in electric vehicles.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UYFFJM", "name": "Avinash Subramanian", "avatar": null, "biography": "Software Engineer - Simulation, Control and Optimization at JuliaHub", "public_name": "Avinash Subramanian", "guid": "2dde4644-6871-5da8-90ff-1f33b75cf63e", "url": "https://pretalx.com/juliacon-2026/speaker/UYFFJM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/9WAZ7B/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/9WAZ7B/", "attachments": [{"title": "flyer-9WAZ7B", "url": "/media/juliacon-2026/submissions/9WAZ7B/resources/9WAZ7B_ZipicBf.png", "type": "related"}]}, {"guid": "ac55a181-43ab-58f1-939b-25257b9c2059", "code": "7GNWCD", "id": 92313, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7GNWCD/image_Bx9ZEEF.webp", "date": "2026-08-13T14:30:00+02:00", "start": "14:30", "end": "2026-08-13T15:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92313-dynamicalsystems-jl-in-2026-successes-and-new-components", "url": "https://pretalx.com/juliacon-2026/talk/7GNWCD/", "title": "DynamicalSystems.jl in 2026: Successes and New Components", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Long talk", "language": "en", "abstract": "DynamicalSystems.jl is an internationally acclaimed general purpose library for nonlinear dynamics and nonlinear timeseries analysis. It encompasses a vast array of features that, despite the established age of dynamical systems theory, no other software has attempted so far. In this talk I will review the state of the DynamicalSystems.jl library in 2026: what's there, what's new, what is going well, what parts need more love, and what is planned for the future, and what you can do to help. I will then highlight recent successes and additions to the library, based primarily on ComplexityMeasures.jl and Attractors.jl, while also highlighting recent additions to the library: CriticalTransitions.jl, TransitionsInTimeseries.jl, and RecurrenceMicrostatesAnalysis.jl.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "GB8WTV", "name": "George Datseris", "avatar": "https://pretalx.com/media/avatars/GB8WTV_KjiCmmK.webp", "biography": "I am an applied physicist with a broad interest in nonlinear dynamics and complex systems and their application to understand the physical world at a conceptual level. My scientific interests focused in understanding the interaction of clouds, climate variability, and climate multistability, as well as developing new methodologies for nonlinear dynamics and nonlinear timeseries analysis. I am the lead dev for JuliaDynamics.", "public_name": "George Datseris", "guid": "c572f2b9-6dbe-50e8-9426-cfe4e0acc344", "url": "https://pretalx.com/juliacon-2026/speaker/GB8WTV/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7GNWCD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7GNWCD/", "attachments": [{"title": "flyer-7GNWCD", "url": "/media/juliacon-2026/submissions/7GNWCD/resources/7GNWCD_RNFymP5.png", "type": "related"}]}, {"guid": "f4287bf4-eef1-5f53-b958-62d6e874807f", "code": "RUJHNQ", "id": 92917, "logo": "https://pretalx.com/media/juliacon-2026/submissions/RUJHNQ/image_fHMGV1h.webp", "date": "2026-08-13T15:00:00+02:00", "start": "15:00", "end": "2026-08-13T15:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92917-criticaltransitions-jl-a-toolbox-for-noise-and-rate-induced-transitions-in-forced-dynamical-systems", "url": "https://pretalx.com/juliacon-2026/talk/RUJHNQ/", "title": "CriticalTransitions.jl: A toolbox for noise- and rate-induced transitions in forced dynamical systems", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Long talk", "language": "en", "abstract": "The DynamicalSystems.jl library allows analyzing nonlinear dynamical systems in Julia. However, functionality for systems with random or time-dependent forcing has been limited. Extending the existing interface to include coupled stochastic differential equations and nonautonomous systems, we introduce [CriticalTransitions.jl](https://github.com/JuliaDynamics/CriticalTransitions.jl): a user-friendly, well-documented package of numerical methods from large deviations and dynamical systems theory to simulate and understand critical behavior, e.g. tipping.", "description": "Metastability and tipping phenomena are important features of nonlinear dynamical systems in the natural and human world. [CriticalTransitions.jl](https://github.com/JuliaDynamics/CriticalTransitions.jl) provides tools in a familiar user interface that allow to study such behavior.\n\nWe discuss the structure, functionality and intuitive interface of the package, closely following the way dynamical systems theory would be written in textbooks. The code builds on, and naturally integrates with, the well-established packages DynamicalSystems.jl and DifferentialEquations.jl, ensuring proven long-term reliability and compatibility.\n\nBy means of two example systems, we demonstrate the basic usage of CriticalTransitions.jl:\n\n1. Noise-induced transitions\n\n- Set up a \u2018CoupledSDEs\u2019 (a system of stochastic differential equations)\n\n- Compute its minimum-action path (\u201cinstanton\u201d) between two attractors\n\n- Efficiently sample ensembles of noise-induced transition paths;\n\n\n2. Rate-induced transitions\n\n- Set up a \u2018RateSystem\u2019 by applying a time-dependent parametric forcing to a given system\n\n- Sample rate-induced transitions and calculate critical rates\n\n- Visualize the morphing stability landscape as a function of the forcing.", "recording_license": "", "do_not_record": false, "persons": [{"code": "8D8PFL", "name": "Reyk B\u00f6rner", "avatar": "https://pretalx.com/media/avatars/J8C3NH_UFd0Ps1.webp", "biography": "Postdoctoral researcher at the Institute for Marine and Atmospheric research Utrecht, Utrecht University, working at the interface between dynamical systems theory, complexity science and climate dynamics.", "public_name": "Reyk B\u00f6rner", "guid": "4e8bca78-df7c-572c-aefa-34ecc8030e12", "url": "https://pretalx.com/juliacon-2026/speaker/8D8PFL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/RUJHNQ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/RUJHNQ/", "attachments": [{"title": "flyer-RUJHNQ", "url": "/media/juliacon-2026/submissions/RUJHNQ/resources/RUJHNQ_J9litcn.png", "type": "related"}]}, {"guid": "b62afe57-9ae6-511a-a8ec-0ef44a6e6156", "code": "78GRUZ", "id": 92767, "logo": "https://pretalx.com/media/juliacon-2026/submissions/78GRUZ/image_D9SsLpa.webp", "date": "2026-08-13T15:30:00+02:00", "start": "15:30", "end": "2026-08-13T15:45:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92767-automated-algorithm-analysis-in-julia-with-algorithmanalysis-jl", "url": "https://pretalx.com/juliacon-2026/talk/78GRUZ/", "title": "Automated Algorithm Analysis in Julia with AlgorithmAnalysis.jl", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Short talk", "language": "en", "abstract": "This talk introduces `AlgorithmAnalysis.jl`, a Julia package that automates the worst-case analysis of black-box algorithms. Analyzing the performance of an algorithm by hand is both tedious and error prone. This package enables users to express algorithms in a natural way using a high-level domain-specific language. This symbolic description is then transformed behind the scenes into a semidefinite program that is solved numerically to construct tight numerical bounds on the worst-case performance. The package implements the performance estimation problem (PEP) and control theoretic frameworks, both of which have been used to analyze a wide variety of first-order methods in optimization.", "description": "Algorithms are numerical recipes to solve problems. In some applications, such as large-scale or safety-critical scenarios, it is imperative that algorithms have both rigorous convergence guarantees and strong performance on practical problems of interest.\n\nThe conventional approach to analyzing algorithm performance relies on deep insights by experts in the field along with tedious and error-prone calculations. Recent work from both the optimization and controls communities, however, has established a systematic methodology to analyze the worst-case performance of an algorithm over a broad class of problems. The systematic analysis methodology has been applied to various types of algorithms, from first-order accelerated methods to stochastic algorithms and operator splitting methods. Beyond analysis, the systematic methodology has also been used to *design* novel algorithms.\n\nThis talk introduces `AlgorithmAnalysis.jl`, a Julia package that implements the automated approach to algorithm analysis. The talk will cover the basic use cases and structure of the package, including:\n\n- Modeling black-box algorithms using a high-level domain-specific language.\n\n- An overview of the analysis frameworks implemented:\n  - The performance estimation problem (PEP) finds the worst-case performance of a black-box algorithm over a given finite number of iterations.\n  - The control methodology interprets the algorithm as a dynamical system and searches for a Lyapunov function whose existence certifies convergence with a particular rate.\n- Results of the analysis applied to various first-order methods in optimization.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZXLXCN", "name": "Bryan Van Scoy", "avatar": "https://pretalx.com/media/avatars/XSQCTX_u1uBDhb.webp", "biography": "Assistant Professor in the Department of Electrical and Computer Engineering at Miami University\n\nLead developer of `AlgorithmAnalysis.jl`, a Julia package for the automated analysis of algorithms", "public_name": "Bryan Van Scoy", "guid": "af3b4fac-f7e2-5eda-a3b2-c31e72ce2ac9", "url": "https://pretalx.com/juliacon-2026/speaker/ZXLXCN/"}, {"code": "EWUSZ7", "name": "Sam Skinner", "avatar": null, "biography": null, "public_name": "Sam Skinner", "guid": "72b41c75-a252-532f-973e-8d1c1899fdb2", "url": "https://pretalx.com/juliacon-2026/speaker/EWUSZ7/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/78GRUZ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/78GRUZ/", "attachments": [{"title": "flyer-78GRUZ", "url": "/media/juliacon-2026/submissions/78GRUZ/resources/78GRUZ_4q8PBI8.png", "type": "related"}]}, {"guid": "f265f154-6a06-55b3-b39c-27e0624e9d46", "code": "UCKDNF", "id": 92440, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UCKDNF/image_If2mQkN.webp", "date": "2026-08-13T15:45:00+02:00", "start": "15:45", "end": "2026-08-13T16:00:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92440-chaos-and-noise-in-evolutionary-game-dynamics", "url": "https://pretalx.com/juliacon-2026/talk/UCKDNF/", "title": "Chaos and noise in evolutionary game dynamics", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Short talk", "language": "en", "abstract": "Evolutionary game theory has traditionally employed deterministic models to describe population dynamics. These models, due to their inherent nonlinearities, can exhibit deterministic chaos, where population fluctuations follow complex, aperiodic patterns. Recently, the focus has shifted towards stochastic models, quantifying fixation probabilities and analysing systems with constants of motion. \nYet, the role of stochastic effects in systems with chaotic dynamics remains largely unexplored within evolutionary game theory. This study addresses how demographic noise -- arising from probabilistic birth and death events -- impacts chaotic dynamics in finite populations. We show that despite stochasticity, large populations retain a signature of chaotic dynamics, as evidenced by comparing a chaotic deterministic system with its stochastic counterpart. More concretely, the strange attractor observed in the deterministic model is qualitatively recovered in the stochastic model, where the term deterministic chaos loses its meaning. We employ tools from nonlinear dynamics using Julia to quantify how the population size influences the dynamics. We observe that for small populations, stochasticity dominates, overshadowing deterministic selection effects. However, as population size increases, the dynamics increasingly reflect the underlying chaotic structure. This resilience to demographic noise can be essential for maintaining diversity in populations, even in non-equilibrium dynamics. Overall, our results broaden our understanding of population dynamics, and revisit the boundaries between chaos and noise, showing how they maintain structure when considering finite populations in systems that are chaotic in the deterministic limit.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "PXDKLF", "name": "Alejandra Ramirez", "avatar": "https://pretalx.com/media/avatars/GNVTYZ_TJIkY5V.webp", "biography": "I am a postdoctoral researcher at the Max Planck Institute for Evolutionary Anthropology in Germany. \nI am currently working on topics related to Dynamical Systems, Evolutionary Game Theory, Cultural Evolution and Agent-Based Modelling.\nI am passionate about coding and to be part of the Julia community has been a wonderful experience!", "public_name": "Alejandra Ramirez", "guid": "e40e22df-7670-5f00-995b-852acbdc2409", "url": "https://pretalx.com/juliacon-2026/speaker/PXDKLF/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UCKDNF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UCKDNF/", "attachments": []}, {"guid": "1bbd4fe2-bf69-5c74-adfb-bb09e0a94d5f", "code": "HXY7PF", "id": 92554, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HXY7PF/image_ojo5JZL.webp", "date": "2026-08-13T16:00:00+02:00", "start": "16:00", "end": "2026-08-13T16:15:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92554-hydrodynamics-of-composable-active-structures-with-microswimmers-jl", "url": "https://pretalx.com/juliacon-2026/talk/HXY7PF/", "title": "Hydrodynamics of composable active structures with MicroSwimmers.jl", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Short talk", "language": "en", "abstract": "MicroSwimmers.jl is a package for simulating the dynamics of flagellated microswimmers in low-Reynolds-number fluid environments where geometry and actuation determine global behaviour. Built on the boundary-element regularised Stokeslet method, it provides a composable framework for constructing time-dependent swimmer morphologies and exploring parameter-driven transitions in trajectories and flow fields, integrated with the Julia scientific computing ecosystem.", "description": "Flagella (also known as cilia) are cellular appendages that beat with self-organised, large amplitude waves. Active, elastic and evolutionarily ancient structures, flagella perform a remarkable diversity of functions in modern-day organisms. They can be found driving fluid flow in our brains, respiratory tract, and reproductive systems, as well as allowing single-celled microorganisms to swim, navigate, feed, hunt and avoid predators. The multi-scale and multi-physics approaches needed to study these complex systems provide numerous computational challenges.\n\nMicroSwimmers.jl is designed to explore the functions of ciliated cells by combining rapid solutions to fluid dynamical problems using the boundary element-regularised stokeslet method with a composable framework for the design and variation of microswimmer morphologies and kinematics. The software connects with the existing Julia ecosystem (e.g. DifferentialEquations.jl for calculating trajectories and Makie.jl for a suite of visualisation tools). Work is ongoing to improve auto-differentiation compatibility to explore optimal behaviour patterns across fluid environments and geometries. \n\nThe Julia implementation will enable biological and clinical research without knowledge of numerical methods or computational geometry, with the goal of rapid simulation and parameter variation on a laptop. I will demonstrate the accessible interface for adding new flagellar beating models and body geometries, and show a few examples of the pipeline from design to results visualisation, including a filter-feeding organism that generates a vortex ring inside a cavity, driving fluid flow over a ciliary band where nutrients are captured.     \n \nThe biophysics of diverse ciliated single-celled organisms is largely unexplored, and both the mechanistic origins of how cells control their flagella to exhibit diverse multi-stable patterns (in the absence of neural control) and the ecological significance of such patterns are highly debated. A better understanding has implications for health, the environment and bio-inspired technologies.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NWSGPR", "name": "James Cass", "avatar": "https://pretalx.com/media/avatars/87AJND_yaN3VRg.webp", "biography": "I'm a postdoctoral researcher at the University of Exeter interested in dynamical models with biological applications. I use analytical and numerical techniques applied to systems away from equilbrium, and am interested in methods to fit experimental data to nonlinear differential equation models.\n\nFor my PhD I studied the internal nonlinear mechanics of eukaryotic cilia and flagella. The big open question is how individual molecular motor proteins act collectively to generate propagating waves that enable microorganisms to swim or pump fluid. Now I am interested in how seemingly 'intelligent' behaviour of single-celled organisms can be controlled through e.g. bioelectricity or genetic networks and how to model such situations mathematically and computationally.", "public_name": "James Cass", "guid": "0fdce09f-4d7f-5fbb-972d-83760722b135", "url": "https://pretalx.com/juliacon-2026/speaker/NWSGPR/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HXY7PF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HXY7PF/", "attachments": []}, {"guid": "c3e7f1a2-a89c-579a-afaf-0d2ce2a8729e", "code": "EJVNCK", "id": 92369, "logo": "https://pretalx.com/media/juliacon-2026/submissions/EJVNCK/image_iWweRQk.webp", "date": "2026-08-13T16:15:00+02:00", "start": "16:15", "end": "2026-08-13T16:45:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92369-sirens-jl-hybrid-and-multiscale-modeling-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/EJVNCK/", "title": "Sirens.jl: Hybrid and multiscale modeling in Julia", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Long talk", "language": "en", "abstract": "Hybrid and multiscale modeling gives rise to complex systems that are often difficult to simulate. Such models are integral to many areas of biology, where simple mechanisms can induce complex emergent phenomena across scales. We have developed Sirens.jl, in which complex systems can be treated as components (models) and connections between them. We demonstrate the power of this approach through a range of examples, from the scale of single proteins to entire populations.", "description": "Sirens.jl is a new package aimed at simplifying the simulation of hybrid (continuous and discrete) and multiscale systems. It uses a component-based architecture where modular components are stepped forwards in time and synchronized through variables passed along user defined inter-connections. Components are simulated using other packages and controlled and connected by Sirens through a minimal interface. Sirens already has implementations of this interface for many common modeling paradigms covering agent-based systems and differential equations. Additionally, Sirens is designed to be highly user-extensible, allowing new packages to easily meet the Sirens interface.\nIn this talk, we will present the core features of Sirens.jl using several examples as a guide. This will include the simulation of an engineered genetic oscillator in a growing population of cells that combines agent-based modelling with an SDE of gene regulatory network function and ODE of cell growth. We end by comparing Sirens.jl to other tools both inside and outside of the Julia ecosystem (e.g., Julia\u2019s ModelingToolkit and Python\u2019s Vivarium). We will demonstrate why these tools are limited for general hybrid and multiscale modeling, and the aspects of Julia that mean it could be a great language for these problems in future.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9398EW", "name": "Matt Owen", "avatar": "https://pretalx.com/media/avatars/7QEJDL_gTpiBJY.webp", "biography": "I am a mathematical biologist from the University of Bristol, UK. I have previously worked on efficient parameterisation of cardiac ion channel models, uncertainty quantification and models of blood coagulation. I currently work as a Postdoctoral Research Associate in Computation Biology within a synthetic biology group.", "public_name": "Matt Owen", "guid": "a67fbbb5-15a2-5b69-bf0c-2b77aa3a00bb", "url": "https://pretalx.com/juliacon-2026/speaker/9398EW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/EJVNCK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/EJVNCK/", "attachments": []}, {"guid": "0c3f3788-9b96-5c53-a2f4-7ea0886f1ba6", "code": "DXX7EW", "id": 92177, "logo": "https://pretalx.com/media/juliacon-2026/submissions/DXX7EW/image_NDTf9Vz.webp", "date": "2026-08-13T16:45:00+02:00", "start": "16:45", "end": "2026-08-13T17:00:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92177-statsop-jl-a-julia-package-for-time-series-testing-via-ordinal-patterns", "url": "https://pretalx.com/juliacon-2026/talk/DXX7EW/", "title": "StatsOP.jl: A Julia Package for Time Series Testing via Ordinal Patterns", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Short talk", "language": "en", "abstract": "StatsOP.jl is a Julia package for (sequential) hypothesis testing in time series using ordinal pattern analysis. The methodology builds on the seminal work of Christoph Bandt and Bernd Pompe (2002), who introduced complexity measures for time series derived from ordinal comparisons of neighboring values.\n\nDesigned with usability in mind, the package provides a streamlined interface for extracting ordinal patterns and conducting statistical inference. A central goal of StatsOP.jl is full compatibility with ComplexityMeasures.jl.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZGYTAZ", "name": "Philipp Ad\u00e4mmer", "avatar": null, "biography": "Philipp Ad\u00e4mmer works at the Institute of Data Science at the University of Greifswald, where his current research focuses on applied econometrics, machine learning, and nonlinear statistics. He earned his doctorate in economics from the University of M\u00fcnster in 2016.", "public_name": "Philipp Ad\u00e4mmer", "guid": "8967e5a6-30f9-5abf-a88c-79516d499fd8", "url": "https://pretalx.com/juliacon-2026/speaker/ZGYTAZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/DXX7EW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/DXX7EW/", "attachments": []}, {"guid": "97df128a-6f29-5796-98ab-2c17c5d64dd8", "code": "LJB3ES", "id": 88773, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LJB3ES/logo_v877fH5.svg", "date": "2026-08-13T17:00:00+02:00", "start": "17:00", "end": "2026-08-13T17:15:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-88773-simulate-large-scale-networked-systems-using-networkdynamics-jl-and-powerdynamics-jl", "url": "https://pretalx.com/juliacon-2026/talk/LJB3ES/", "title": "Simulate large-scale networked systems using NetworkDynamics.jl and PowerDynamics.jl", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Short talk", "language": "en", "abstract": "From power grids to hydrogen pipelines and diffusion processes, dynamic flow networks are ubiquitous in science and engineering. In this talk, we present NetworkDynamics.jl, a Julia package for modelling such systems, along with PowerDynamics.jl, a domain-specific library for power grid simulations built on top of it.\nBoth packages have been around for many years. However, over the last 1-2 years we have essentially rewritten both from the ground up, fully embracing ModelingToolkit.jl as the primary way to define component models and deepening our integration with the broader SciML ecosystem.\nNetworkDynamics.jl enables users to model inhomogeneous network systems in terms of components: dynamical systems on nodes (e.g. generators or pumps) and on edges (e.g. power lines or pipelines). Component models can be defined using ModelingToolkit.jl and are then placed on a graph. The interconnection between components is handled by our performance-oriented backend. This clear separation between dynamic models and network topology enables efficient scaling for large networks. Rather than symbolically analyzing the entire system\u2014which may contain hundreds of thousands of equations\u2014we compile each component type once and reuse it across all instances.\nThe resulting system is simply a right-hand side function for a differential equation, making it fully compatible with the SciML ecosystem: OrdinaryDiffEq.jl for time integration, SymbolicIndexingInterface.jl for accessing network states and observables, and SciMLSensitivity.jl for parameter optimisation.\nWe will present the underlying mathematical model, demonstrate applications in hydrogen networks and power grids, and show how Makie.jl, Bonito.jl, and GraphMakie.jl can be used to build interactive dashboards for exploring simulation results.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "93DCJL", "name": "Hans W\u00fcrfel", "avatar": null, "biography": "I am a researcher at Potsdam Institute of Climate Impact Research. My work focuses on developing an open source software for simulating dynamics of large power grids, for that I develop and maintain the two packages PowerDynamics.jl and NetworkDynamics.jl. Within the community you can find me as @hexaeder on slack, discourse and GitHub.", "public_name": "Hans W\u00fcrfel", "guid": "65b2e0c8-9ee5-52d5-aba6-3a37a21c6724", "url": "https://pretalx.com/juliacon-2026/speaker/93DCJL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LJB3ES/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LJB3ES/", "attachments": [{"title": "flyer-LJB3ES", "url": "/media/juliacon-2026/submissions/LJB3ES/resources/LJB3ES_DoShb0A.png", "type": "related"}]}, {"guid": "c4b2804d-5257-5e30-902c-7b354ae4236f", "code": "HMHAZL", "id": 91936, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HMHAZL/image_tRUUeHY.webp", "date": "2026-08-13T17:15:00+02:00", "start": "17:15", "end": "2026-08-13T17:30:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-91936-optimal-control-with-an-electrophysiology-experiment-in-the-loop", "url": "https://pretalx.com/juliacon-2026/talk/HMHAZL/", "title": "Optimal control with an electrophysiology experiment in the loop", "subtitle": "", "track": "Nonlinear and complex systems analysis with Julia", "type": "Short talk", "language": "en", "abstract": "Predicting the response of a system to a specific input is a very important task in engineering and nature in general. To accurately predict the response, we developed a closed-loop algorithm to perform model selection based on the output of a reference system obtained following a known input. We applied our algorithm to identify the most accurate model to predict the output of a lab experiment, which consists of light sensitive cells. Using light signals, one can control the output current of the cell. We integrated the experiment in a closed-loop framework, which brought up a constraint on the computation time. Indeed, the recordings of the output current is limited in time, meaning that the model selection has to be performed as fast as possible. To do that, we leveraged the Julia package Sockets.jl and the high speed intranet to export the computation on a server, where the required code had been precompiled. This allowed us to efficiently identify the most accurate model to use for our experiment.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZLARFX", "name": "Melvyn Tyloo", "avatar": null, "biography": "I obtained my PhD in Theoretical Physics at the Swiss Federal Institute of Technology in Lausanne (EPFL) respectively in 2020. From Feb.2022 to Oct.2024, I was a Director's Postdoc Fellow at the Los Alamos National Laboratory (LANL) and also affiliated with the Center for Nonlinear Studies (CNLS). I am currently a Postdoctoral Research Fellow in the Living Systems Institute at the University of Exeter. My research focuses on complex network-coupled dynamical systems, the identification of their local/global vulnerabilities against external perturbations and their control. I am currently working on open and closed loop algorithms for the control of networks of neurons which are used in experiments.", "public_name": "Melvyn Tyloo", "guid": "b117e34f-4876-51a3-ae22-4e648d218b0d", "url": "https://pretalx.com/juliacon-2026/speaker/ZLARFX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HMHAZL/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HMHAZL/", "attachments": []}, {"guid": "c8954d2c-9ef0-5319-a9d3-909e0c8b62bd", "code": "DVSBHX", "id": 93041, "logo": "https://pretalx.com/media/juliacon-2026/submissions/DVSBHX/image_pvxYhNh.webp", "date": "2026-08-13T17:45:00+02:00", "start": "17:45", "end": "2026-08-13T18:45:00+02:00", "duration": "01:00", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93041-sustainability-in-computational-science-and-engineering", "url": "https://pretalx.com/juliacon-2026/talk/DVSBHX/", "title": "Sustainability in Computational Science and Engineering", "subtitle": "", "track": "General", "type": "Keynote", "language": "en", "abstract": "Computational Science and Engineering (CSE) integrates engineering, applied mathematics, and computer science to enable model-based design, knowledge generation, and decision support. While CSE has become a key enabler of sustainable products and operations, sustainability is still often treated as an afterthought in computational method development. This talk reflects on sustainability as a core design principle for CSE\u2014one that aligns naturally with scientific relevance, long-term usability, and enduring research value. Drawing on representative examples, we present a holistic perspective spanning resource consumption, digital infrastructure, and organizational practices. We examine how these aspects interact in modern computational research and conclude with directions for impact through sustainable CSE.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "YEAWUJ", "name": "Julia Kowalski", "avatar": "https://pretalx.com/media/avatars/YNYEYF_K1hGSOU.webp", "biography": "I am an enthusiastic Computational Science and Engineering researcher interested in developing computational methods to study and predict complex engineering systems and environmental processes, such as geohazards. My specific interests include data-integrated simulation models for shallow flow, contact, and phase-change processes, as well as surrogate models for real-time prediction, applied uncertainty management, and Bayesian model selection. I am an open science and FAIR data advocate, passionate about modern research software engineering, holistic computational workflow management, and ensuring 'analysis-readiness' of data to foster sustainability in CSE and to maximize our knowledge return from computational investments. I am similarly passionate about promoting a diverse working culture, convinced that bringing together a multitude of perspectives not only enriches innovation but also paves the way for broadly accepted scientific solutions.", "public_name": "Julia Kowalski", "guid": "fa015f6c-fdb8-5efc-8755-94c2195665a5", "url": "https://pretalx.com/juliacon-2026/speaker/YEAWUJ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/DVSBHX/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/DVSBHX/", "attachments": []}], "Muschel \u2014 N1": [{"guid": "75c3d4d6-50f2-5e7e-a40f-e85859bd2ac7", "code": "3LGMBE", "id": 92708, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3LGMBE/image_qhFZIyo.webp", "date": "2026-08-13T10:00:00+02:00", "start": "10:00", "end": "2026-08-13T10:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92708-the-state-of-biojulia", "url": "https://pretalx.com/juliacon-2026/talk/3LGMBE/", "title": "The State of BioJulia", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "The BioJulia organization began as `Bio.jl` circa January 2014 (julia v0.2 era) and has grown into a loosely organized collection of packages for computational biology, from plotting protein structures (`BioMakie.jl`) to performing matrix manipulations on single-cell RNA sequencing data (`SingleCellProjections.jl`) to low-level biological file type I/O (`Automa.jl`, `FASTX.jl`, `XAM.jl`). Here, we will provide a brief history of the Org and recent efforts to provide more structure for the community, as well as growth areas and a vision for the future.", "description": "Computational biology has a rich history, and in many ways julia is an ideal language for working biologists, as it provides high-level interactivity, an excellent data stack, and the ability to optimize low-level performance. Yet growth of the Bio community has been slow. The purpose of this talk is threefold:\n\n1. To provide a history of the organization, highlighting the unique aspects of julia for computational biology development, and the contributions that BioJulia community members have made to the broader julia ecosystem.\n2. To describe recent and on-going efforts to improve the structure of the organization and community.\n3. To share a vision for the future growth of BioJulia, and to solicit feedback from the community on priorities and opportunities for outreach.\n\nKevin Bonham asked his first Stack Overflow question about dictionaries in julia in June of 2014, and has been an active contributor across the julia ecosystem for more than 10 years. He became an admin of BioJulia sometime around 2020.", "recording_license": "", "do_not_record": false, "persons": [{"code": "JAMMRT", "name": "Kevin Bonham, PhD", "avatar": "https://pretalx.com/media/avatars/JAMMRT_gkLrOHF.webp", "biography": "I am an assistant professor at Tufts Medical Center with nearly 11 years in computational biology and bioinformatics, much of that time spent coding in Julia. I study the relationship between the gut microbiome and human development. I am a co-maintainer of the BioJulia organization and maintain or contribute to packages in the Biology, Data, Ecology, and Statistics ecosystems, and have worked on educational material for Pumas.ai and JuliaHub.", "public_name": "Kevin Bonham, PhD", "guid": "7ef7ccf0-8e55-505d-a5fc-5ba19031defd", "url": "https://pretalx.com/juliacon-2026/speaker/JAMMRT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3LGMBE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3LGMBE/", "attachments": []}, {"guid": "d435db09-f611-5252-a79c-17562ef77d72", "code": "7YBX7H", "id": 92684, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7YBX7H/image_pvUwuPa.webp", "date": "2026-08-13T10:15:00+02:00", "start": "10:15", "end": "2026-08-13T10:45:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92684-julia-for-bioinformatics", "url": "https://pretalx.com/juliacon-2026/talk/7YBX7H/", "title": "Julia for bioinformatics", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Long talk", "language": "en", "abstract": "Julia is a fantastic language for bioinformatics. But why? What about bioinformatics makes Julia so suitable? On which bioinformatics problems have Julians focused their efforts to far? And if Julia is so useful for bioinformatics, why isn't it more popular? This talk presents the state of affairs of programming in modern bioinformatics and where Julia fits into that.", "description": "Bioinformatics is a perfect example of a discipline suffering under the two language problem: Our datasets are huge and our algorithms demanding, yet the majority of our code is written by scientists, not software engineers. Our code requires both performance, and also introspection and interactivity, and the line between exploratory data science and demanding production code is hazy.\nFor this reason, myself and others have found Julia to be a good tool. The Julian bioinformaticians have created a selection of great packages, and several of us have been using Julia in our research for years. Yet Julia has never taken off in our field, and bioinformatics remains dominated by Python and increasingly Rust, neither of which make any attempt at solving the two language problem.\n\nIn this talk, I will cover what particular requirements bioinformatics, as a discipline, demands from a programming language. What computational problems bioinformaticians tend to face, and why Julia is a good fit. I will briefly touch on the current, and possible future state of bioinformatics software in Julia. Finally, I will attempt to answer why Julia, despite its important advantages, have remained niche in the field, and speculate on how Julia can continue to provide value as a niche programming language.", "recording_license": "", "do_not_record": false, "persons": [{"code": "DHLEWB", "name": "Jakob Nybo Andersen", "avatar": "https://pretalx.com/media/avatars/TB3PQB_xiikT0J.webp", "biography": "I am a research software engineer from Copenhagen, Denmark.\nI currently work for the Danish health authorities, writing software for pathogen surveillance. I am trained as a molecular biologist, and have previously been working as an academic researching bioinformatics.\nI program in Python, Rust and Julia, and am an active developer in the BioJulia ecosystem. I write Julia packages for efficient I/O and parsing, and foundational bioinformatics functionality such as BioSequences and Kmers.jl.", "public_name": "Jakob Nybo Andersen", "guid": "38b80cb2-0f3d-5559-9ecd-224070b8b191", "url": "https://pretalx.com/juliacon-2026/speaker/DHLEWB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7YBX7H/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7YBX7H/", "attachments": []}, {"guid": "7531570a-ac71-5e52-a969-5f97df8919b0", "code": "3NCLXU", "id": 92680, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3NCLXU/image_Ne9euqa.webp", "date": "2026-08-13T10:45:00+02:00", "start": "10:45", "end": "2026-08-13T11:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92680-efficient-robust-parsing-with-bufferio-jl", "url": "https://pretalx.com/juliacon-2026/talk/3NCLXU/", "title": "Efficient, robust parsing with BufferIO.jl", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Despite being a high-performance language, Julia's I/O functionality has been designed for convenience, and is neither robust nor efficient. I present an alternate I/O interface in BufferIO.jl, which has more well-defined semantics, and permits low level, high performance I/O operations.", "description": "Bioinformatics is rife with file formats, and as such, I/O and parsing is a bottleneck for bioinformatics workflows, and therefore a major concern for BioJulia. Unfortunately, Base Julia provides few functions for efficient I/O, and what functionality exists is severely underspecified. Historically, BioJulia packages has worked around this by reading data into a Vector{UInt8} in bulk, and then re-implementing various Base functionality by operating on the buffer using packages like TranscodingStreams, BufferedStreams and Automa. This ad hoc approach improved I/O performance over Base, but did not develop into any coherently designed buffered IO API, and many inefficiencies remained.\nTaking inspiration from Rust's BufRead APIs, BufferIO.jl provides a new, reimagined I/O interface. It uses a simple, low-level core set of methods, concrete types, and well-defined semantics in order to allow reliably high performance. Combined with StringViews.jl and MemoryViews.jl, performance in simple I/O benchmarks almost matches Rust's. The package has been used in a couple of BioJulia packages, and have proved a good foundational package to build abstractions on.\nSome problems remain: Because BufferIO.jl has not re-implemented basic functionality such as IOStream or Base Julia's file system operations, but wraps these Base I/O objects, BufferIO does not truly shield users from the inefficiency and unreliability of Base Julia.\nIn this talk, I will motivate and demonstrate the new BufferIO.jl package, and showcase examples of how a new interface can improve the experience of writing high performance I/O code in Julia.", "recording_license": "", "do_not_record": false, "persons": [{"code": "DHLEWB", "name": "Jakob Nybo Andersen", "avatar": "https://pretalx.com/media/avatars/TB3PQB_xiikT0J.webp", "biography": "I am a research software engineer from Copenhagen, Denmark.\nI currently work for the Danish health authorities, writing software for pathogen surveillance. I am trained as a molecular biologist, and have previously been working as an academic researching bioinformatics.\nI program in Python, Rust and Julia, and am an active developer in the BioJulia ecosystem. I write Julia packages for efficient I/O and parsing, and foundational bioinformatics functionality such as BioSequences and Kmers.jl.", "public_name": "Jakob Nybo Andersen", "guid": "38b80cb2-0f3d-5559-9ecd-224070b8b191", "url": "https://pretalx.com/juliacon-2026/speaker/DHLEWB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3NCLXU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3NCLXU/", "attachments": []}, {"guid": "fa390bf0-7e28-5c45-98c5-190ad7d73dcd", "code": "QACULP", "id": 93466, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QACULP/image_5yrnkye.webp", "date": "2026-08-13T11:00:00+02:00", "start": "11:00", "end": "2026-08-13T11:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93466-modeling-indirect-readout-through-dna-deformation-free-energies-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/QACULP/", "title": "Modeling Indirect Readout through DNA Deformation Free Energies in Julia", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Protein-DNA binding can depend on sequence changes even outside direct complex contact regions. While an MD-based protocol can capture this indirect readout with high correlation to experiment, its cost limits large-scale use. We present a Julia package to estimate DNA deformation free energies with practical analytical models optimized from simulation trajectories. These models aim to explain these affinity differences at much lower cost, enabling broader screening or sequence optimization.", "description": "### Biological motivation\n\nProtein recognition of DNA target sequences relies on two main mechanisms: direct and indirect readout. While direct readout arises from base-specific contacts between amino acids and DNA chemical groups, indirect readout depends on sequence-dependent DNA shape and deformability. This makes it subtler, but still highly influential in protein\u2013DNA binding affinity.\n\n### Modeling DNA deformation\n\nAs a concrete example, we consider the Fis\u2013DNA complex, whose binding affinity is sensitive to sequence changes even outside the protein\u2013DNA contact region. Our work shows that a molecular dynamics (MD)-based protocol with modern force-field parameters can already capture these changes, with strong correlation to experiment. However, this approach is too computationally expensive for broad comparative studies or target-sequence optimization. To address this, we developed a Julia package to estimate DNA deformation free energies from simulation data using practical models with different accuracy tradeoffs.\n\nThese models build on well-established features of DNA mechanics: local interactions along the chain, rigid-base descriptions in helical coordinates, and the influence of discrete backbone states on conformational preferences. Together, these ingredients make it possible to represent DNA deformation energetics with a reduced and tractable set of parameters. Using MD simulations as a data source, we introduce maximum-likelihood and pseudo-maximum-likelihood approaches that learn these reduced models while retaining the essential physics of DNA deformation. The resulting analytical framework enables rapid estimation of thermodynamic quantities such as deformation free energies, opening the door to larger-scale studies and sequence screening.\n\n### Julia implementation\n\nJulia is a natural fit for this problem because it brings together statistical modeling, optimization, and high-performance scientific computing in a single expressive environment. The package is designed with abstractions that separate model definitions from implementation details, making it easier to extend, test, and compare new approaches. This talk will show how biologically motivated assumptions and Julia-based software design come together in a practical tool for lower-cost estimation of sequence-dependent DNA deformation effects.", "recording_license": "", "do_not_record": false, "persons": [{"code": "JSBN3E", "name": "Christian Sustay Martinez", "avatar": "https://pretalx.com/media/avatars/EZXX7B_f4sm3Ex.webp", "biography": "### Hello everyone!\n\nMy name is Christian Sustay and I am currently a PhD student at the Technical University of Munich in the group for Theoretical Biophysics - Molecular Dynamics.\n\n#### Interests\nAt the moment I am mostley interested in\n- Enhanced sampling techniques\n- DNA interactions and deformation\n- Statistical mechanics for biomolecules\n\n#### Programming Languages\nFor the most part, I carry out my work using Python and Julia.\n\n#### Contact Info\nIf you are interested in anything of what I do, please feel free to contact me at <christian.sustay@tum.de>.", "public_name": "Christian Sustay Martinez", "guid": "58c3eaae-9317-5427-a349-1bde245b9ab1", "url": "https://pretalx.com/juliacon-2026/speaker/JSBN3E/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QACULP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QACULP/", "attachments": []}, {"guid": "298a7634-2c1e-501c-ad9c-95e94df33350", "code": "BK783X", "id": 93393, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BK783X/image_aSphoB1.webp", "date": "2026-08-13T11:15:00+02:00", "start": "11:15", "end": "2026-08-13T11:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93393-modeling-and-visualizing-late-embryogenesis-in-the-caenorhabditis-elegans", "url": "https://pretalx.com/juliacon-2026/talk/BK783X/", "title": "Modeling and Visualizing Late Embryogenesis in the Caenorhabditis elegans", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Caenorhabditis elegans, a small roundworm, is widely used as a model for studying how cells and tissues develop and function. Using a form of light\u2011sheet imaging that records the embryo from two views (dual\u2011view selective plane illumination microscopy, diSPIM), we capture three\u2011dimensional movies of late embryogenesis. From these data, we build a smooth, time\u2011resolved anatomical model of the embryo\u2019s surface and interior. We first mark a set of easily identifiable \u201cseam\u201d cells along the body wall and fit a flexible mesh to the organism\u2019s surface. To ensure continuity and accuracy, we represent shapes and motions with smooth curves and periodic functions (natural cubic B\u2011splines and Fourier series), which provide robust interpolation across space and time. We also developed Julia\u2011based software that \u201cuntwists\u201d each worm by transforming the 3D mesh into a cylindrical coordinate system centered on the embryo\u2019s anterior\u2013posterior axis. Individual models are then averaged over time and registered to a shared coordinate frame to create a common reference. Positions of tracked, fluorescently labeled cells can be mapped into this reference, yielding a four\u2011dimensional (3D + time) cellular atlas of C. elegans embryogenesis. When combined with single\u2011cell transcriptomics, the atlas can support spatially and temporally resolved maps of gene expression during development. Overall, these tools provide a flexible and accurate modeling framework with fast runtimes that enable rapid analysis and feedback. \n\nhttps://github.com/JaneliaSciComp/ShroffCelegansModels.jl (currently public) \nhttps://github.com/JaneliaSciComp/Transcriptome4D (currently private)", "description": "Authors: Mark Kittisopikul*, Ben Arthur, Ryan Christensen, Diyi Chen, Matthew Chaw, Stephen Xu, Alyssa Stark, John Walsh, Brie Yarbrough, Hari Shroff \n\n* Presenting\n\nCaenorhabditis elegans, a small roundworm, is widely used as a model for studying how cells and tissues develop and function. Using a form of light\u2011sheet imaging that records the embryo from two views (dual\u2011view selective plane illumination microscopy, diSPIM), we capture three\u2011dimensional movies of late embryogenesis. From these data, we build a smooth, time\u2011resolved anatomical model of the embryo\u2019s surface and interior. We first mark a set of easily identifiable \u201cseam\u201d cells along the body wall and fit a flexible mesh to the organism\u2019s surface. To ensure continuity and accuracy, we represent shapes and motions with smooth curves and periodic functions (natural cubic B\u2011splines and Fourier series), which provide robust interpolation across space and time. We also developed Julia\u2011based software that \u201cuntwists\u201d each worm by transforming the 3D mesh into a cylindrical coordinate system centered on the embryo\u2019s anterior\u2013posterior axis. Individual models are then averaged over time and registered to a shared coordinate frame to create a common reference. Positions of tracked, fluorescently labeled cells can be mapped into this reference, yielding a four\u2011dimensional (3D + time) cellular atlas of C. elegans embryogenesis. When combined with single\u2011cell transcriptomics, the atlas can support spatially and temporally resolved maps of gene expression during development. Overall, these tools provide a flexible and accurate modeling framework with fast runtimes that enable rapid analysis and feedback. \n\nhttps://github.com/JaneliaSciComp/ShroffCelegansModels.jl (currently public) \nhttps://github.com/JaneliaSciComp/Transcriptome4D (currently private)", "recording_license": "", "do_not_record": false, "persons": [{"code": "FALWEJ", "name": "Mark Kittisopikul, Ph.D.", "avatar": "https://pretalx.com/media/avatars/USMVVE_LEXahCX.webp", "biography": "Software Engineer, Scientific Computing Software at the Howard Hughes Medical Institute Janelia Research Campus", "public_name": "Mark Kittisopikul, Ph.D.", "guid": "3979a8ff-8ca5-562f-91b0-1b398221d1ae", "url": "https://pretalx.com/juliacon-2026/speaker/FALWEJ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BK783X/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BK783X/", "attachments": []}, {"guid": "f3857ba2-36c4-5bdb-bb81-7ea22d2e8b03", "code": "8Z8UFG", "id": 93117, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8Z8UFG/image_FeB9BPf.webp", "date": "2026-08-13T11:30:00+02:00", "start": "11:30", "end": "2026-08-13T11:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93117-reproduciblejobs-jl-enables-practical-and-reproducible-workflows-in-singlecellprojections-jl", "url": "https://pretalx.com/juliacon-2026/talk/8Z8UFG/", "title": "ReproducibleJobs.jl enables practical and reproducible workflows in SingleCellProjections.jl", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Reproducibility in scientific analyses is often hampered by insufficient tooling. Large data and slow computations force users to save partial results to disk, and the actual steps to reproduce the entire chain from raw data to end results are lost. Here we present ReproducibleJobs.jl - a computational framework that enables natural workflows and fast turnaround, while still achieving reproducibility and show how it works in practice for single cell expression data in SingleCellProjections.jl.", "description": "In this talk, we will describe how ReproducibleJobs.jl is structured, and show how it is used in SingleCellProjections.jl to achieve reproducible and practical workflows for large single cell expression data.\n\nReproducibleJobs.jl is a framework for reproducible analyses of scientific data, that is based on the following ideas:\n* The burden of reproducibility should be moved from the user to the packages they use for analysis, when possible.\n* Memoization/caching is a good strategy because while the raw data can be large, the computed results are in essence much smaller.\n* It is possible to create succinct specifications of how to perform analyses.\n\nResults in ReproducibleJobs.jl are lazy. Consider a simple but standard single cell workflow that look something like this:\n```\ncounts = load_counts([\"paths/to/large/files\"])\ntransformed = sctransform(counts)\nnormalized = normalize_matrix(transformed)\nreduced = pca(normalized; nsv=100)\n```\nTo actually retrieve the result of the PCA (Principal Component Analysis) computation, the user then needs to call `fetch!(reduced)`. This is what enables ReproducibleJobs.jl to work under the hood. The lazy result is in fact a *specification*, a recipe, of what to compute. And if the computation was already memoized (cached), even in an earlier Julia session, ReproducibleJobs.jl can load the result from disk directly, without accessing any data for the earlier analysis steps. Importantly, several steps are taken to standardize the specifications, such that only changes that actually affect the results cause recomputations.\n\nThe talk also describe some of the technical challenges and solutions relating to:\n* Specification design\n* Specification metaprogramming/preprocessing - going from \"intent\" to \"implementation\"\n* Hashing and caching\n* Canonical representations of specifications and why they are important\n* Strategies for handling high-level data types (such as tables, DataMatrices (matrix + variable & observation annotations)) in specifications\n* Stability across Julia sessions and package versions\n* How specification metaprogramming enables projections of one dataset onto another in SingleCellProjections.jl\n\n**SingleCellProjections.jl** is a package for analyzing Single Cell expression data in Julia, with support for loading, transforming, normalizing, filtering, doing dimension reduction, performing statistical tests and more. Internally, SingleCellProjections.jl uses matrix expressions built from sparse and low rank matrices, thus avoiding speed and memory problems that competing (R/python) packages face when using large dense matrices.", "recording_license": "", "do_not_record": false, "persons": [{"code": "YCNJP8", "name": "Rasmus Henningsson", "avatar": "https://pretalx.com/media/avatars/YCNJP8_SbxiAia.webp", "biography": "Rasmus Henningsson\u2019s research interests are centered around high-dimensional biological data in general and Leukemia in particular. He is currently developing new methods for dimension reduction, analysis and visualization of single cell expression data. He got his PhD degree in applied mathematics at Lund University in 2018, working on dimension reduction, viral evolution and Leukemia.", "public_name": "Rasmus Henningsson", "guid": "9da1379a-7765-54ce-8e15-df1cffe1fa99", "url": "https://pretalx.com/juliacon-2026/speaker/YCNJP8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8Z8UFG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8Z8UFG/", "attachments": []}, {"guid": "0b14e51b-c165-574a-9e01-39098ac7d4d3", "code": "8JFFSK", "id": 92711, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8JFFSK/image_11T4orY.webp", "date": "2026-08-13T11:45:00+02:00", "start": "11:45", "end": "2026-08-13T12:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92711-spatialomics-jl-using-the-geo-image-and-data-stacks-to-analyze-spatial-transcriptomics-data", "url": "https://pretalx.com/juliacon-2026/talk/8JFFSK/", "title": "SpatialOmics.jl - Using the geo, image, and data stacks to analyze spatial transcriptomics data", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Spatial transcriptomics (sTx) is a cutting edge technology combining microscopy and high-throughput sequencing or molecular probes to measure transcription in the context of cells within their tissue context, enabling researchers to achieve single-cell resolution and identify spatial relationships between cell types. While this technology opens up many new avenues for investigation, proprietary analysis applications provided by the manufacturers of sTx machines are typically slow (often requiring cloud-based compute), feature-poor, and do not generalize across technologies. `SpatialOmics.jl` is a new package that combines functionality from JuliaImages, JuliaGeo, and JuliaData to offer open source and extensible end-to-end analysis tools for spatial 'omics applications.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "JAMMRT", "name": "Kevin Bonham, PhD", "avatar": "https://pretalx.com/media/avatars/JAMMRT_gkLrOHF.webp", "biography": "I am an assistant professor at Tufts Medical Center with nearly 11 years in computational biology and bioinformatics, much of that time spent coding in Julia. I study the relationship between the gut microbiome and human development. I am a co-maintainer of the BioJulia organization and maintain or contribute to packages in the Biology, Data, Ecology, and Statistics ecosystems, and have worked on educational material for Pumas.ai and JuliaHub.", "public_name": "Kevin Bonham, PhD", "guid": "7ef7ccf0-8e55-505d-a5fc-5ba19031defd", "url": "https://pretalx.com/juliacon-2026/speaker/JAMMRT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8JFFSK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8JFFSK/", "attachments": []}, {"guid": "252f11a7-4495-5038-a5bc-7e33fe1dea20", "code": "ADYCYS", "id": 93500, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ADYCYS/image_YjCQxyD.webp", "date": "2026-08-13T12:00:00+02:00", "start": "12:00", "end": "2026-08-13T12:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93500-geometric-coembedding-of-complex-interacting-systems", "url": "https://pretalx.com/juliacon-2026/talk/ADYCYS/", "title": "Geometric coembedding of complex interacting systems", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Biology is driven by interactions: between transcription factors and genes, receptors and ligands, and pre- and post-synaptic neurons. To gain insights about complex systems, embedding methods are commonly used to represent pairwise similarity relationships; however, we lack tools for *coembedding* two or more classes of interacting objects. I will present new machine learning tools for coembedding interacting systems. A distinguishing feature is the representation of entities by Gaussian probability distributions rather than points, which results in a quadratic compression of dimensionality, enabling quantiatively-accurate visualization of more complex systems than is possible by traditional techniques.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "DNXX8F", "name": "Tim Holy", "avatar": "https://pretalx.com/media/avatars/J9BSUH_jUUc1I6.webp", "biography": "Timothy E. Holy is the Alan A. and Edith L. Wolff Professor of Neuroscience and Biomedical Engineering at Washington University in St. Louis. His lab combines technological innovation with analysis of the rules governing neuronal function and computation. His work on Julia includes contributions to the type system, the array and broadcasting infrastructure, the standard library, and developer tools like the profiler, debugger, Revise, and many others.", "public_name": "Tim Holy", "guid": "baa486d6-5ebc-537a-85d8-349d97deddfc", "url": "https://pretalx.com/juliacon-2026/speaker/DNXX8F/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ADYCYS/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ADYCYS/", "attachments": []}, {"guid": "3a1e957e-8232-54f8-8deb-918d81bde4d4", "code": "QTDH38", "id": 92309, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QTDH38/image_RhE0oBo.webp", "date": "2026-08-13T12:15:00+02:00", "start": "12:15", "end": "2026-08-13T12:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92309-phylohd-jl-hyperdimensional-computing-meets-phylogenetic-reconstruction", "url": "https://pretalx.com/juliacon-2026/talk/QTDH38/", "title": "PhyloHD.jl: Hyperdimensional Computing meets phylogenetic reconstruction", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Phylogenetic reconstruction and comparative analysis are fundamental to understanding evolutionary relationships and biological diversity. Traditional algorithms rely heavily on multiple sequence alignments and statistical modelling, which face significant computational challenges with large-scale datasets. Furthermore, integrating information from multiple data sources, such as sequences, structures, and functional annotations, at the time of reconstruction, remains technically challenging, limiting the feasibility of phylogenetic reconstruction using today\u2019s diversity of biological annotations.\n\nHyperdimensional computing (HDC) is a novel computational paradigm that employs high-dimensional representations of atomic entities (e.g., amino acids) and combines them via algebraic operations to represent more complex data structures (e.g., proteins). This paradigm, parallel to connectionist modelling, is characterised by modelling the brain's distributed memory and the operations underlying its processing. HDC exhibits several properties advantageous for biological data analysis: robustness to noise, holographic information distribution, and the ability to integrate heterogeneous data sources seamlessly. Recent applications in DNA sequencing, pattern matching, and molecular classification have demonstrated HDC's potential in bioinformatics, where its computational efficiency, interpretability, and natural capacity for multimodal data fusion make it particularly well suited to complex phylogenetic analyses.\n\nIn this talk, we showcase the potential of HDC for phylogenetic reconstruction and comparative analysis. Here, we present PhyloHD.jl, a Julia package for representing biological data as hypervectors and reconstructing phylogenetic trees from these representations. We will showcase how to calculate branch support using the HDC paradigm and present a multimodal tree reconstruction approach that integrates multiple heterogeneous data sources, including sequences, structures, and functional annotations. Finally, we will showcase how HDC learning techniques can be used for family-based phylogenetic tree reconstruction and ancestral sequence reconstruction. This work represents the first attempt to use hyperdimensional computing as a computational paradigm for phylogenetics and opens new avenues for research in this field.", "description": "The talk will be present:\n\n- **What is hyperdimensional computing?:** A brief primer on how to build your own brain and computing with concepts (hypervectors) + HyperdimensionalComputing.jl package introduction.\n\n- **Why is it useful for phylogenetic reconstruction and comparative analysis?:** A discussion of the properties of HDC that make it particularly well-suited for biological data analysis, and a review of recent applications in bioinformatics.\n\n- **How do we represent biological data as hypervectors?:** A presentation on a novel framework for representing biological data as hypervectors, including sequences, three-dimensional structures, and functional annotations, and how this can be combined to represent multimodal biological entities.\n\n- **Alignment-free phylogenetic reconstruction using HDC**: From biological data to hypervectors to phylogenetic tree, showcase of cvigilv/PhyloHD.jl\n\n- **Branch support calculation for HDC-based phylogenetics:** Showcase on hypervector perturbation, bootstrapping, jack-knifing, and other techniques for calculating branch support based on the HDC paradigm.\n\n- **Multimodal tree reconstruction using HDC:** A showcase of how to integrate multiple data sources at the time of reconstruction using HDC, showcasing 3 distinct Tree-of-Life based on data fusion to represent more complex biological entities. \n\n- **HDC learning techniques for family-based phylogenetic tree reconstruction and ancestral sequence reconstruction:** Preliminary results on using HDC learning techniques for family-based phylogenetic tree reconstruction.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UHM3PP", "name": "Carlos Vigil-V\u00e1squez", "avatar": "https://pretalx.com/media/avatars/NZAZDZ_eRLvLHh.webp", "biography": "I'm a Chilean biochemist-turned-computational biologist currently pursuing my PhD at the Universit\u00e9 de Lille, focusing on reconstructing an evolutionary Tree-of-Life using unconventional computational paradigms for biological system representation.", "public_name": "Carlos Vigil-V\u00e1squez", "guid": "9328e885-d949-5a5d-97e4-d9740a81efed", "url": "https://pretalx.com/juliacon-2026/speaker/UHM3PP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QTDH38/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QTDH38/", "attachments": []}, {"guid": "48361b1f-e6b2-50c3-8c5e-17b08f0ddced", "code": "9MGLXG", "id": 92905, "logo": "https://pretalx.com/media/juliacon-2026/submissions/9MGLXG/image_7tkc9Tc.webp", "date": "2026-08-13T12:30:00+02:00", "start": "12:30", "end": "2026-08-13T12:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92905-graphdynamicalsystems-jl-discrete-finite-state-systems-over-graphs", "url": "https://pretalx.com/juliacon-2026/talk/9MGLXG/", "title": "GraphDynamicalSystems.jl: discrete, finite-state systems over graphs", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Julia has great tools for simulating and analyzing many kinds of dynamical systems.\nHowever, discrete, finite-state systems, i.e., systems made of many interacting parts that each have a small number of possible states, are less well supported.  The class of GDS includes many well-known formalisms, such as Boolean networks, cellular automata, and sequential dynamical systems. \nThese models have a long history of use in the biological setting.\nBoolean networks, for example, were originally introduced to model genetic regulatory networks.\nMore recent generalizations of Boolean networks, _qualitative_ networks, have allowed experts to build and reason about large, complex models of signaling pathways.\n\n\n`GraphDynamicalSystems.jl` provides a common backbone for constructing, learning, executing, and analyzing GDS. As these models are 1) dynamical systems, 2) graphs, and 3) compositional in their behavior, they make for a great use case for recombining different packages and ecosystems to create something new\u2014something Julia excels at. In this case, the package hooks into the `JuliaDynamics`, \n`JuliaGraphs`, and soon, the `AlgebraicJulia`, `JuliaReach`, and `SciML` ecosystems. With this package, we hope to stimulate the implementation and development of new methods for learning and analysis of this broad class of systems.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "WGRKMC", "name": "Reuben Gardos Reid", "avatar": "https://pretalx.com/media/avatars/UNH8GG_QXil9zG.webp", "biography": "I am a PhD candidate at TU Delft, where I focus on program synthesis and applying it to scientific discovery. Currently, I am focusing on methods for synthesizing dynamical systems, with applications in biology. Alongside my own research, I work on Herb.jl, a program synthesis framework developed here at TU Delft (see: https://herb-ai.github.io/).", "public_name": "Reuben Gardos Reid", "guid": "5f3f84b1-9474-52eb-b11d-ef96ef5e8d61", "url": "https://pretalx.com/juliacon-2026/speaker/WGRKMC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/9MGLXG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/9MGLXG/", "attachments": []}, {"guid": "b02a2878-25e8-59c6-be52-b8191862e3cd", "code": "UQN7WB", "id": 92682, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UQN7WB/image_eugf1tw.webp", "date": "2026-08-13T12:45:00+02:00", "start": "12:45", "end": "2026-08-13T13:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92682-bayesinteractomics-jl-when-one-bayes-factor-isn-t-enough", "url": "https://pretalx.com/juliacon-2026/talk/UQN7WB/", "title": "BayesInteractomics.jl: When One Bayes Factor Isn't Enough", "subtitle": "", "track": "Julia for Biology and Biology for Julia", "type": "Short talk", "language": "en", "abstract": "Identifying genuine protein-protein interactions from mass spectrometry data requires disentangling real biology from experimental noise. [BayesInteractomics.jl]([url](https://github.com/ma-seefelder/BayesInteractomics.jl)) tackles this by fitting three complementary Bayesian models (detection, enrichment, and dose-response) and combining their evidence through copula mixture models. Built on RxInfer.jl and Copulas.jl, it leverages Julia's type system, multiple dispatch, and threading to analyze thousands of proteins in minutes.", "description": "Mass spectrometry-based interactomics experiments produce lists of hundreds to thousands of candidate protein-protein interactions, but a large fraction of these are non-specific contaminants or experimental artifacts. Existing tools typically apply a single statistical test and threshold -- a t-test on fold changes, or a simple scoring scheme -- discarding the rich multi-dimensional structure of the data. A protein might be modestly enriched but detected with striking consistency, or show a clear dose-response trend that a fold-change filter would miss entirely.\n\n[BayesInteractomics.jl]([url](https://github.com/ma-seefelder/BayesInteractomics.jl)) takes a different approach. For each candidate interaction, the package computes Bayes factors from three independent statistical models that each capture a distinct aspect of the data:\n\n1. A Beta-Bernoulli model that evaluates whether a protein is detected more consistently in bait samples than in controls.\n2. A hierarchical Bayesian model (via RxInfer.jl) that estimates quantitative enrichment (log2 fold change) while sharing information across experimental protocols.\n3. A Bayesian linear regression that tests for dose-response correlation between prey and bait abundance.\n\nRather than multiplying these Bayes factors under a naive independence assumption, the package uses Copulas.jl to model the dependency structure between evidence sources. This matters because enrichment and detection evidence are positively correlated under both hypotheses -- a genuinely enriched protein is also more likely to be consistently detected -- so treating them as independent inflates the combined evidence and drives up false discovery rates. An EM algorithm fits a two-component copula mixture (H0 vs. H1) that accounts for this correlation, selecting among Clayton, Frank, Gumbel, Gaussian, and Joe copula families via model comparison. The result is a joint Bayes factor and calibrated posterior probability for every protein that properly reflects the shared information content across evidence types.\n\nThis talk will cover the statistical design choices, how the Julia ecosystem made them practical, and lessons learned building a research-grade Bayesian analysis package. Specific topics include:\n\n- How RxInfer.jl's reactive message-passing enables fast variational inference for the hierarchical enrichment model, and why this matters when you need to fit the same model thousands of times.\n- Using Copulas.jl for method-of-moments fitting of Archimedean copulas, and how multiple dispatch made it straightforward to support six copula families through a single interface.\n- Automated data curation via the STRING database API, including protein group resolution and synonym mapping\n- Package extensions for optional functionality: a network analysis extension (Graphs.jl, GraphPlot.jl) for building and visualizing interaction networks, and an experimental structural docking extension (BioStructures.jl) -- both loaded only when the user imports the relevant packages.\n- Self-contained HTML reports (client-side Plotly.js and DataTables.js, no additional Julia dependencies) so that collaborators who do not have Julia installed can explore results interactively in a browser.\n\nThe talk is aimed at Julia users interested in Bayesian statistics, scientific computing, or computational biology. No proteomics background is assumed and I will demonstrate a complete analysis from raw data to interactive report on a real dataset.", "recording_license": "", "do_not_record": false, "persons": [{"code": "KB9SBF", "name": "Manuel Seefelder", "avatar": "https://pretalx.com/media/avatars/9MZE3U_nDDcXsi.webp", "biography": "Manuel Seefelder is a postdoctoral researcher in the Department of Gene Therapy at Ulm University Hospital, Germany. His background is in molecular medicine, with a doctorate on the huntingtin-associated protein 40 and its role in Huntington's disease. His current work sits at the intersection of wet-lab research, proteomics and computational method development: he builds Bayesian and deep-learning pipelines for analyzing protein interactome data from mass spectrometry experiments. Julia is his primary research language, and BayesInteractomics.jl grew directly out of the need to rigorously quantify interaction evidence in his own experiments. He also developed ProteinCoLoc, a Bayesian tool for colocalization analysis in fluorescence microscopy (Scientific Reports, 2024), and teaches a workshop on applied Bayesian statistics for PhD students at Ulm University.", "public_name": "Manuel Seefelder", "guid": "320d51b0-b524-5ea1-86b2-1a27e1b20a15", "url": "https://pretalx.com/juliacon-2026/speaker/KB9SBF/"}], "links": [{"title": "GitHub Repository", "url": "https://github.com/ma-seefelder/BayesInteractomics.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UQN7WB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UQN7WB/", "attachments": []}, {"guid": "7a6ad2a6-fdfd-57bb-8606-d5ace5d82653", "code": "ELF3HR", "id": 92604, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ELF3HR/image_XxHRF0G.webp", "date": "2026-08-13T14:30:00+02:00", "start": "14:30", "end": "2026-08-13T14:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92604-interfacialwaves-jl-a-julia-package-for-nonlinear-interfacial-waves", "url": "https://pretalx.com/juliacon-2026/talk/ELF3HR/", "title": "InterfacialWaves.jl , a julia package for nonlinear interfacial waves.", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Short talk", "language": "en", "abstract": "Interfacial water waves are governed by complex Euler equations, that satisfy non-trivial nonlinear travelling and standing wave solutions. Analyzing these solutions and their stability is important for understanding oceanic dynamics, from coastal impacts to mysteries like rogue waves. To streamline this, we introduce **InterfacialWaves.jl**, a Julia package that enables researchers to easily generate nonlinear wave equilibriums and analyze their stability to decode the secrets of breaking and rogue waves", "description": "# InterfacialWaves.jl\n\n**InterfacialWaves.jl** solves primarily for boundary-value and initial-value problems in travelling and standing interfacial wave equations. \n\n### Key Capabilities\nThe package enables users to numerically calculate waves to a very high accuracy. Features include:\n* **Wave Calculations:**\n    * **[Stokes waves](https://en.wikipedia.org/wiki/Stokes_wave)** (travelling waves)\n    * Gravity-Capillary waves (travelling as well as standing waves)\n    * Viscous Gravity-Capillary waves (travelling waves)\n* **Standing Wave Solutions:** Functions to calculate nonlinear, non-trivial standing wave solutions in wall-bounded containers (in both cartesian and axisymmetric settings).  For example the **[video here](https://www.youtube.com/watch?v=g2DojIPyxos)** demonstrates axisymmetric capillary standing waves . \n* **Linear Stability Analysis:** Available for both travelling and standing waves across arbitrary modes.\n* **Symbolic-Numeric Interface:** Allows solving initial-value problems for standing waves by programming standard perturbative methods.\n\n### Documentation\nThe package documentation is expected to be comprehensive description that covers:\n* Basics and preliminaries of nonlinear interfacial waves.\n* Examples showing the usage of methods exported by the package.\n* Descriptions of the important API.\n\n### Dependencies\nThe package is built upon several well-known, publicly available Julia packages, such as:\n`OrdinaryDiffEq.jl`, `NonlinearSolve.jl`, `FFTW.jl`, `ApproxFun.jl`, `BifurcationKit.jl`, `Symbolics.jl`, and `ForwardDiff.jl`.\n\n### Talk Overview\nThis talk is structured to showcase the package's abilities in simulating nonlinear interfacial waves, complete with validations against the known physics of interfacial waves.", "recording_license": "", "do_not_record": false, "persons": [{"code": "FXJRAM", "name": "Nikhil Janardan Yewale", "avatar": null, "biography": "I am a PhD student at IIT Bombay. My research interests are fluid dynamics, numerical methods and complex systems. My PhD work involves studying interfacial waves. I have been one of the major (maybe not that major ! ) contributors to Catalyst.jl. I have also contributed in small proportions to MethodOfLines.jl , DataDrivenDiffEq.jl and Symbolics.jl (very small contributions).", "public_name": "Nikhil Janardan Yewale", "guid": "5bd9e662-39c2-55fd-8729-5994c8cb0bea", "url": "https://pretalx.com/juliacon-2026/speaker/FXJRAM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ELF3HR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ELF3HR/", "attachments": []}, {"guid": "3d786a46-60e5-5fc4-ae27-0b65af1a7607", "code": "QQ37LB", "id": 90573, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QQ37LB/image_0YTpoFX.svg", "date": "2026-08-13T14:45:00+02:00", "start": "14:45", "end": "2026-08-13T15:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-90573-geothermalwells-jl-gpu-accelerated-3d-simulation-of-deep-borehole-heat-exchanger-arrays", "url": "https://pretalx.com/juliacon-2026/talk/QQ37LB/", "title": "GeothermalWells.jl: GPU-Accelerated 3D Simulation of Deep Borehole Heat Exchanger Arrays", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Short talk", "language": "en", "abstract": "I present GeothermalWells.jl, an open-source Julia package for full three-dimensional simulation of deep borehole heat exchangers (DBHEs) and well arrays. Through an operator splitting strategy combining ROCK2, ADI, and semi-Lagrangian methods with vendor-agnostic GPU acceleration, making multi-year well array simulations computationally tractable on a single GPU. Previously considered prohibitively expensive, these simulations open new possibilities for systematic design optimization of geothermal well systems.", "description": "Deep borehole heat exchangers offer a low-risk path to geothermal energy, but simulating arrays of interacting wells over years to decades has been considered computationally prohibitive. This talk presents GeothermalWells.jl, developed during my time at MIT's Julia Lab in collaboration with Alan Edelman, Robert Metcalfe and Hendrik Ranocha.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7QLYPL", "name": "Collin Wittenstein", "avatar": "https://pretalx.com/media/avatars/7QLYPL_fCW8j5j.webp", "biography": "Collin Wittenstein is an incoming PhD student at MIT's Julia Lab. He is completing dual master's degrees in Physics and Computational Sciences, supervised by Hendrik Ranocha, at Johannes Gutenberg University Mainz, Germany, where he previously earned bachelor's degrees in Physics and Mathematics. His research focuses on high-performance numerical methods for PDEs, with applications ranging from dispersive water waves to geothermal energy systems. He is an active contributor to the general Julia open-source ecosystem, and is the author of [GeothermalWells.jl](https://github.com/cwittens/GeothermalWells.jl) and a co-author of [DispersiveShallowWater.jl](https://github.com/NumericalMathematics/DispersiveShallowWater.jl).\nWebsite: [cwittens.github.io](https://cwittens.github.io)", "public_name": "Collin Wittenstein", "guid": "abc6e512-5328-55dd-8224-019a29acf478", "url": "https://pretalx.com/juliacon-2026/speaker/7QLYPL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QQ37LB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QQ37LB/", "attachments": [{"title": "flyer-QQ37LB", "url": "/media/juliacon-2026/submissions/QQ37LB/resources/QQ37LB_MXs3F2S.png", "type": "related"}]}, {"guid": "75c05e08-5aa6-5691-9068-d3025b0d131f", "code": "WFVNXT", "id": 92472, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WFVNXT/image_nb3REW3.webp", "date": "2026-08-13T15:00:00+02:00", "start": "15:00", "end": "2026-08-13T15:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92472-accelerating-pde-timestepping-with-ordinarydiffeqoperatorsplitting", "url": "https://pretalx.com/juliacon-2026/talk/WFVNXT/", "title": "accelerating PDE timestepping with OrdinaryDiffEqOperatorSplitting", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Long talk", "language": "en", "abstract": "For specific classes of time-dependent PDEs it can become handy to split the full problem up into simpler to handle subproblems, such that we can exploit the specific structure of each subproblem during time integration. To achieve this goal we introduce https://github.com/SciML/OrdinaryDiffEqOperatorSplitting.jl\u00a0 a library that allows user to split ODEs and DAEs into sub-problems, where we allow problems to be recursively split. The library coordinates in which order the sub-problems need to be integrated, while each of the sub-problems can be solved which a suitable solver from OrdinaryDiffEq.jl .", "description": "One aspect of partial differential equations that makes efficient solving difficult is that different parts of the equations may have different timescales which traditionally has led to scientists writing their own custom timestepping algorithms.  A better solution to this problem is to have ODE solver libraries define an API by which the user can communicate the desired splitting of the problem to the solver, and then the solver can use appropriate algorithms to solve the individual pieces. This package uses a splitting technique where each component takes a timestep independent of the other components, and then the communicating the state updates between the components. \n\nThis splitting of equations can lead to significant performance increases because the sub-problems can be chosen to have additional structure when compared to the full problem. Splitting can separate non-stiff components of the equations, linear or affine pieces, or simply separating loosely related components to reduce the size of the nonlinear problems that need to be solved. As such, splitting can significantly accelerate simulation.", "recording_license": "", "do_not_record": false, "persons": [{"code": "FQFXWT", "name": "Oscar Smith", "avatar": null, "biography": "I work for JuliaHub at making ODEs go fast.", "public_name": "Oscar Smith", "guid": "961b3129-84bc-53b1-bc1b-41f67e1b5913", "url": "https://pretalx.com/juliacon-2026/speaker/FQFXWT/"}, {"code": "3H9L7T", "name": "Dennis Ogiermann", "avatar": "https://pretalx.com/media/avatars/3H9L7T_foZIcGM.webp", "biography": "Researcher in Computational Cardiology at the [chair of continuum mechanics](https://www.lkm.ruhr-uni-bochum.de/lkm/index.html.en) of Professor Dr.-Ing. Daniel Balzani at the Ruhr University Bochum.\n\nLeading developer of [Thunderbolt.jl](https://github.com/JuliaHealth/Thunderbolt.jl) and developer of [Ferrite.jl](https://github.com/Ferrite-FEM/Ferrite.jl). More detailed information on my contributions to the open source ecosystem can be found at my [GitHub profile](https://github.com/termi-official).", "public_name": "Dennis Ogiermann", "guid": "c02d8cf6-d6b9-54ed-ad46-65c333420dc6", "url": "https://pretalx.com/juliacon-2026/speaker/3H9L7T/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WFVNXT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WFVNXT/", "attachments": []}, {"guid": "70aacfa7-d52b-532c-bf15-fa7f762652a0", "code": "YQF7AE", "id": 92778, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YQF7AE/image_jAtqmbm.webp", "date": "2026-08-13T15:30:00+02:00", "start": "15:30", "end": "2026-08-13T15:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92778-trixiatmo-jl-an-entropy-stable-discontinuous-galerkin-dynamical-core-for-atmospheric-modeling", "url": "https://pretalx.com/juliacon-2026/talk/YQF7AE/", "title": "TrixiAtmo.jl: An Entropy-Stable Discontinuous Galerkin Dynamical Core for Atmospheric Modeling", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Short talk", "language": "en", "abstract": "TrixiAtmo.jl is a numerical simulation package for atmospheric flows, implementing modern discontinuous Galerkin methods in a composable and unified Julia framework. Users can set up complex simulations, from idealized benchmarks to global circulation models, with minimal boilerplate, and easily integrate new formulations, tracers, or microphysics. The package supports multiple formulations of the compressible Euler equations, including the effects of rain and clouds. I present efficient and high-performance implementations and highlight challenges and solutions from both the discretization and coding perspectives.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "Z33RT3", "name": "Marco Artiano", "avatar": "https://pretalx.com/media/avatars/Z33RT3_ugC8LK9.webp", "biography": "I am a PhD student at the Johannes Gutenberg University Mainz working at the Institute of Mathematics, under the supervision of Professor Hendrik Ranocha. I am mainly interested in high-order methods, entropy stable/conservative schemes and applications towards atmospheric flows. My contributions to Julia are mostly in [Trixi.jl](https://github.com/trixi-framework/Trixi.jl), [TrixiAtmo.jl](https://github.com/trixi-framework/TrixiAtmo.jl) and [Ariadne.jl](https://github.com/NumericalMathematics/Ariadne.jl).", "public_name": "Marco Artiano", "guid": "28ab2475-be5c-5403-9d07-153d1ccabbdf", "url": "https://pretalx.com/juliacon-2026/speaker/Z33RT3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YQF7AE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YQF7AE/", "attachments": []}, {"guid": "3d15f28c-8cf8-5aaf-bec4-a7e0a20574d4", "code": "YU8ZKN", "id": 92609, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YU8ZKN/image_4uXCzC0.webp", "date": "2026-08-13T15:45:00+02:00", "start": "15:45", "end": "2026-08-13T16:15:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92609-macchiato-jl-a-freshly-brewed-meshless-pde-package", "url": "https://pretalx.com/juliacon-2026/talk/YU8ZKN/", "title": "Macchiato.jl: a Freshly Brewed Meshless PDE Package", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Long talk", "language": "en", "abstract": "Macchiato.jl is designed to eliminate the need for traditional meshes in physics simulations. Instead, it operates on unstructured point clouds, which define the boundary of the region of interest. The package aims to provide a user-friendly yet robust environment enabling scientists and engineers to conduct meaningful simulations with minimal pre-processing effort. By bypassing the complexities of mesh generation, users are free to focus on their research and applications.", "description": "Established numerical methods such as the Finite Element Method (FEM) and Finite Volume Method (FVM) are routinely used to solve complex partial differential equation (PDE) problems. However, they require significant effort in mesh generation for complex geometries. Mesh generation is a significant bottleneck in practical engineering analysis and design, often becoming the single most time-intensive part of the process for the user. While recent advancements in deep-learning-based methods aim to address these limitations, they are not yet considered a reliable alternative to traditional mesh-based simulations.\n\nAn alternative approach to solving spatio-temporal PDEs is offered by meshless methods, which do not require a mesh and simply rely on a collection of points. Neighboring points influence each other but no further geometrical construction is required and all of the complications attached to a traditional mesh are eradicated. These methods are well-suited to addressing common challenges of mesh-based approaches such as negative volumes, highly skewed elements, inefficiencies in discretization and moving boundaries undergoing large deformations. They are particularly advantageous for applications where the geometry was not produced using geometric modeling software. An example is patient-specific cardiovascular simulations, where the geometry is extracted from a medical image. Meshless methods are also well-suited for the integration of Graph Neural Networks (GNNs) within models, as the neighborhoods of influence are naturally represented as graphs. To this end, Macchiato.jl was created to fill a gap in the Julia PDE community of Eulerian meshless methods.\n\nMacchiato.jl is being actively developed alongside two other Julia packages which are the core dependencies - WhatsThePoint.jl and RadialBasisFunctions.jl. Macchiato.jl exposes the APIs relevant to scientists and engineers, while the other two packages are meant to provide the required numerical machinery. WhatsThePoint.jl is responsible for the initial phase of node generation - it contains a set of useful APIs to generate the node clouds necessary for subsequent discretization. RadialBasisFunctions.jl is a general package implementing anything related to Radial Basis Functions (RBF), designed to provide all the functionality required for implementing the Radial Basis Function-Finite Difference (RBF-FD) method - the meshless discretization scheme upon which Macchiato.jl relies.\n\nThe scope of this presentation is to announce the package to the Julia community and look for collaboration. Our talk will focus on the high-level architecture of the ecosystem, showing examples, and discussing future directions of development and the challenges still ahead.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9WCBLX", "name": "Kyle Beggs", "avatar": "https://pretalx.com/media/avatars/QP8FM7_Jmou8bD.webp", "biography": "I am a scientific software engineer specializing in the development of multiscale and multiphysics models for cardiovascular medicine. My work focuses on creating computational tools to facilitate fundamental medical research and improving clinical outcomes.", "public_name": "Kyle Beggs", "guid": "3d6807de-9be0-546e-991b-fe410be63637", "url": "https://pretalx.com/juliacon-2026/speaker/9WCBLX/"}, {"code": "3BVEZW", "name": "Davide Miotti", "avatar": "https://pretalx.com/media/avatars/HA8MHG_IlenAg2.webp", "biography": "PhD in mechanical engineering, quiet life in north-east Italy, mountains and great coffee factories nearby. I don't get to use Julia at work, but use it in my free time a lot for building a new solver with friends from around the world. It's a fresh take on numerical methods for PDEs that I find really exciting and I'd love to talk more about.", "public_name": "Davide Miotti", "guid": "4bbbf17e-b201-55ef-8852-11953b42595c", "url": "https://pretalx.com/juliacon-2026/speaker/3BVEZW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YU8ZKN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YU8ZKN/", "attachments": []}, {"guid": "f282da43-b74c-5cc1-8b25-03451f51ca78", "code": "YXXEBL", "id": 93203, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YXXEBL/image_xLnZVKZ.webp", "date": "2026-08-13T16:15:00+02:00", "start": "16:15", "end": "2026-08-13T16:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93203-reproducible-parallel-adaptive-multisolver-coupling-of-trixi-jl-and-deal-ii", "url": "https://pretalx.com/juliacon-2026/talk/YXXEBL/", "title": "Reproducible Parallel Adaptive Multisolver Coupling of Trixi.jl and deal.II", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Short talk", "language": "en", "abstract": "We couple Trixi.jl and deal.II in parallel and adaptively on a shared p4est mesh for simulating multiphysics systems that allow a splitting into two (or more) PDEs coupled via source terms depending on each other's state variables following a common Eulerian formulation.", "description": "We consider here specifically the dynamics of self-gravitating astrophysical flows, where the governing equations can be split into the hyperbolic hydrodynamic equations describing the flow and an elliptic Poisson equation describing the gravitational potential. For this problem we show that we can use already existing solvers for elliptic and hyperbolic equations, connect them through a joint hierarchical Cartesian mesh and couple them via their source terms depending on each other's state variables. By performing the coupling of these two systems in this way we obtain a multiphysics solver directly combining efficient solvers for their respective coupled governing equations. This coupling also allows indicators for adaptive mesh refinement to take all state variables of the coupled equations into account, which is also true for time step control. Here we couple Trixi.jl, a high-order discontinuous Galerkin framework for solving hyperbolic conservation laws featuring shock capturing and structure preserving methods, and deal.II, a finite-element library written in C++, on a shared adaptive mesh. We apply the resulting solver to some example problems, including a self-gravitating Sedov blast. Our approach is also extendable to other multiphysics systems following a common Eulerian formulation. By using BinaryBuilder.jl we can provide an accessible way to use and adapt this method and also make our results easily reproducible for other interested people.", "recording_license": "", "do_not_record": true, "persons": [{"code": "T8CSNK", "name": "Vivienne Ehlert", "avatar": null, "biography": "PhD mathematics student at the University of Augsburg", "public_name": "Vivienne Ehlert", "guid": "4a98dc39-b1e1-5899-9f64-33a51691a101", "url": "https://pretalx.com/juliacon-2026/speaker/T8CSNK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YXXEBL/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YXXEBL/", "attachments": []}, {"guid": "e668a5d7-3344-5384-a903-60cd2bdea1d7", "code": "UX3K8A", "id": 92921, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UX3K8A/image_Yp15awR.webp", "date": "2026-08-13T16:30:00+02:00", "start": "16:30", "end": "2026-08-13T17:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92921-wias-pdelib-finite-element-and-finite-volume-based-pde-solvers-and-tooling-components", "url": "https://pretalx.com/juliacon-2026/talk/UX3K8A/", "title": "WIAS-PDELib: Finite-Element and Finite-Volume based PDE solvers and tooling components.", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Long talk", "language": "en", "abstract": "This talk will give an overview on WIAS-PDELib, a github organization which emerged from the development of the finite volume solver [VoronoiFVM.jl](https://github.com/WIAS-PDELib/VoronoiFVM.jl) and the finite element solver [ExtendableFEM.jl](https://github.com/WIAS-PDELib/ExtendableFEM.jl). These \"top level\" packages depend on a number of infrastructure packages which have been moved to WIAS-PDELib in order to allow for sustainable joint maintenance. The talk will focus on the main features and usage examples of [VoronoiFVM.jl](https://github.com/WIAS-PDELib/VoronoiFVM.jl) and [ExtendableFEM.jl](https://github.com/WIAS-PDELib/ExtendableFEM.jl). It will introduce common infrastructure packages, focusing on [ExtendableGrids.jl](https://github.com/WIAS-PDELib/ExtendableGrids.jl) for grid management, [SimplexGridFactory.jl](https://github.com/WIAS-PDELib/SimplexGridFactory.jl) for mesh generation via [Triangulate.jl](https://github.com/JuliaGeometry/Triangulate.jl) and [TetGen.jl](https://github.com/JuliaGeometry/TetGen.jl) backends, [ExtendableSparse.jl](https://github.com/WIAS-PDELib/ExtendableSparse.jl) for straightforward and efficient sparse matrix assembly, and [GridVisualize.jl](https://github.com/WIAS-PDELib/GridVisualize.jl) for grid function visualization with backends for  [Makie.jl](https://github.com/MakieOrg/Makie.jl), [PythonPlot.jl](https://github.com/JuliaPy/PythonPlot.jl), [PlutoVista.jl](https://github.com/j-fu/PlutoVista.jl) and others.  Particular emphasis will be given on the level of integration with the SciML ecosystem ([CommonSolve.jl](https://github.com/SciML/CommonSolve.jl), [LinearSolve](https://github.com/SciML/LinearSolve.jl), [OrdinaryDiffEq](https://github.com/SciML/OrdinaryDiffEq.jl)). Options for interoperability with other PDE relevant packages will be discussed.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "XR3TFU", "name": "J\u00fcrgen Fuhrmann", "avatar": "https://pretalx.com/media/avatars/XR3TFU_542S37m.webp", "biography": "J\u00fcrgen Fuhrmann, PhD, is a senior researcher in  the Numerical Mathematics and Scientific Computing group at Weierstrass Institute for Applied Analysis and Stochastics, Berlin. His research topics include finite volume methods,  numerical simulation in electrochemistry, semiconductors and other fields, and software design and development for partial differential equations. Since 2018, Julia is his main programming language. He regularly teaches Julia based courses on Advanced Topics from Scientific Computing at TU Berlin.", "public_name": "J\u00fcrgen Fuhrmann", "guid": "111f548f-bf01-5421-8b66-c47ae216333c", "url": "https://pretalx.com/juliacon-2026/speaker/XR3TFU/"}, {"code": "NJH3XD", "name": "Patrick Jaap", "avatar": "https://pretalx.com/media/avatars/HVFDHH_4dKMvnl.webp", "biography": "I'm a postdoc researcher at the Weierstrass Institute in Berlin in the scientific computing group.\nMy day-to-day work is all about running finite element simulations for solid mechanics and quantum devices.\nI came into touch with Julia three years ago and I love it since.\nAt WIAS, we have our own Julia PDE solver ecosystem WIAS-PDELib, where I am a core maintainer.\nI'm interested in low-level solver routines, software architecture, coding quality, and experiments with new data types and programming features.", "public_name": "Patrick Jaap", "guid": "a23ad9c5-33f1-5f52-90af-92bca67d3dd0", "url": "https://pretalx.com/juliacon-2026/speaker/NJH3XD/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UX3K8A/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UX3K8A/", "attachments": []}, {"guid": "b77b853b-f184-5bdd-8c08-ba915178b8e6", "code": "S9PVRR", "id": 95390, "logo": "https://pretalx.com/media/juliacon-2026/submissions/S9PVRR/image_pT23enR.webp", "date": "2026-08-13T17:00:00+02:00", "start": "17:00", "end": "2026-08-13T17:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-95390-panel-what-is-missing-for-julia-for-pdes", "url": "https://pretalx.com/juliacon-2026/talk/S9PVRR/", "title": "Panel: What is missing for Julia for PDEs?", "subtitle": "", "track": "Julia for Partial Differential Equations and its Applications", "type": "Long talk", "language": "en", "abstract": "This is a panel/podium discussion on the general topic of what is missing the Julia PDEs ecosystem.\n\nQuestions covered:\n- Performance\n- Tooling \n- Joint infrastructure, interfaces between PDE packages\n- Multitude of PDE and visualization packages\n- Recent developments\n\nPanelists: [Kristoffer Carlsson](https://kristofferc.github.io/#about), [Christopher Rackauckas](https://chrisrackauckas.com/), [Hendrik Ranocha](https://ranocha.de/), and [Gregory Wagner](https://glwagner.github.io/)\n\nModeration: Dennis Ogiermann, J\u00fcrgen Fuhrmann", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "XR3TFU", "name": "J\u00fcrgen Fuhrmann", "avatar": "https://pretalx.com/media/avatars/XR3TFU_542S37m.webp", "biography": "J\u00fcrgen Fuhrmann, PhD, is a senior researcher in  the Numerical Mathematics and Scientific Computing group at Weierstrass Institute for Applied Analysis and Stochastics, Berlin. His research topics include finite volume methods,  numerical simulation in electrochemistry, semiconductors and other fields, and software design and development for partial differential equations. Since 2018, Julia is his main programming language. He regularly teaches Julia based courses on Advanced Topics from Scientific Computing at TU Berlin.", "public_name": "J\u00fcrgen Fuhrmann", "guid": "111f548f-bf01-5421-8b66-c47ae216333c", "url": "https://pretalx.com/juliacon-2026/speaker/XR3TFU/"}, {"code": "CQMYJW", "name": "Arpit Babbar", "avatar": "https://pretalx.com/media/avatars/CQMYJW_Pl9jv2D.webp", "biography": "I am a Humboldt postdoctoral researcher under Professor Hendrik Ranocha in the Numerical Mathematics group at Johannes Gutenberg University, Mainz.\n\nI work with the Julia packages [Tenkai.jl](https://github.com/Arpit-Babbar/Tenkai.jl) and [TrixiLW.jl](https://github.com/Arpit-Babbar/TrixiLW.jl).", "public_name": "Arpit Babbar", "guid": "02c417d9-3d66-5253-836b-f744e3c269a4", "url": "https://pretalx.com/juliacon-2026/speaker/CQMYJW/"}, {"code": "Z33RT3", "name": "Marco Artiano", "avatar": "https://pretalx.com/media/avatars/Z33RT3_ugC8LK9.webp", "biography": "I am a PhD student at the Johannes Gutenberg University Mainz working at the Institute of Mathematics, under the supervision of Professor Hendrik Ranocha. I am mainly interested in high-order methods, entropy stable/conservative schemes and applications towards atmospheric flows. My contributions to Julia are mostly in [Trixi.jl](https://github.com/trixi-framework/Trixi.jl), [TrixiAtmo.jl](https://github.com/trixi-framework/TrixiAtmo.jl) and [Ariadne.jl](https://github.com/NumericalMathematics/Ariadne.jl).", "public_name": "Marco Artiano", "guid": "28ab2475-be5c-5403-9d07-153d1ccabbdf", "url": "https://pretalx.com/juliacon-2026/speaker/Z33RT3/"}, {"code": "3H9L7T", "name": "Dennis Ogiermann", "avatar": "https://pretalx.com/media/avatars/3H9L7T_foZIcGM.webp", "biography": "Researcher in Computational Cardiology at the [chair of continuum mechanics](https://www.lkm.ruhr-uni-bochum.de/lkm/index.html.en) of Professor Dr.-Ing. Daniel Balzani at the Ruhr University Bochum.\n\nLeading developer of [Thunderbolt.jl](https://github.com/JuliaHealth/Thunderbolt.jl) and developer of [Ferrite.jl](https://github.com/Ferrite-FEM/Ferrite.jl). More detailed information on my contributions to the open source ecosystem can be found at my [GitHub profile](https://github.com/termi-official).", "public_name": "Dennis Ogiermann", "guid": "c02d8cf6-d6b9-54ed-ad46-65c333420dc6", "url": "https://pretalx.com/juliacon-2026/speaker/3H9L7T/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/S9PVRR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/S9PVRR/", "attachments": []}], "Muschel \u2014 N2": [{"guid": "f452dca7-5e91-5fe8-9b57-9d292dce87dd", "code": "LDEKJG", "id": 92615, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LDEKJG/image_rhsLwwz.webp", "date": "2026-08-13T10:00:00+02:00", "start": "10:00", "end": "2026-08-13T10:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92615-building-playable-virtual-instruments-in-julia-a-real-time-saxophone-model-controlled-by-sensors", "url": "https://pretalx.com/juliacon-2026/talk/LDEKJG/", "title": "Building Playable Virtual Instruments in Julia: A Real-Time Saxophone Model Controlled by Sensors", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Following the initial presentation of the package `RealTimeAudioDiffEq.jl`, we present a use case demonstrating how the package is used to control a saxophone model in real time with a custom interface. This interface consists of a regular saxophone mouthpiece instrumented with sensors, and the whole setup serves as a basis for virtual instrument design in Julia, as well as for studying instrument behavior and/or player actions in performance situations.", "description": "At JuliaCon 2024 we presented a small package for sound synthesis from ordinary differential equations, named `RealTimeAudioDiffEq.jl`. The aim was to render the solution of continuous dynamical systems audible \u2013in addition to traditional visualization\u2013 and to explore the potential of dynamical systems for sound synthesis and virtual (musical) instrument design. \nContinuing this work, we present a use case using a custom hardware interface for controlling a saxophone model in real time. The model is composed of a system of ordinary differential equations of which we are interested in studying two parameters controlled by the player in the real instrument: blowing pressure and force applied to the reed.\nThe interface consists of a regular saxophone mouthpiece instrumented with a pressure sensor, for measuring blowing pressure and a force sensor, for measuring force applied to the reed. The resulting signals are read by an ADC and sent to the computer through a serial port using an Arduino board. This stream is then read in Julia using `LibSerialPort.jl` and used to control those parameters in the saxophone model in real time.\nBecause the model is explicitly dynamical and the performer controls physically meaningful parameters, this interface provides a direct way to investigate the dynamical origin of complex sonorities such as multiphonics (or bichords), where multiple oscillatory regimes may coexist or compete. In particular, the system offers a concrete experimental handle for exploring transitions between regimes as the control parameters vary, and for relating these transitions to candidate bifurcation scenarios, underlying mode onsets, octave jumps, and the emergence of simultaneous partials.\nWe will show how the real-time loop is organized, how serial I/O and buffering are handled robustly, and how calibration and parameter mapping are implemented so that the model becomes a responsive musical interface. The intent is to provide a concrete template that generalizes beyond the saxophone model, illustrating how `RealTimeAudioDiffEq.jl` can support a broader family of playable dynamical instruments controlled by sensors in real time.", "recording_license": "", "do_not_record": false, "persons": [{"code": "KB3LTL", "name": "Antonio Ortega Brook", "avatar": "https://pretalx.com/media/avatars/RRFYKG_6V50XqG.webp", "biography": "Composer, programmer, and performer with a degree in Electroacoustic Music. PhD candidate in Science and Technology, studying the acoustics of wind instrument multiphonics and sound synthesis based on Dynamical Systems.\n- <https://github.com/antonioortegabrook/RealTimeAudioDiffEq.jl>", "public_name": "Antonio Ortega Brook", "guid": "0d55c3e8-b12e-576c-bc14-68eefdc06e81", "url": "https://pretalx.com/juliacon-2026/speaker/KB3LTL/"}, {"code": "MJCQ93", "name": "Manuel Camilo Eguia", "avatar": "https://pretalx.com/media/avatars/W7KAPS_ZQS7cce.webp", "biography": "Creator of the LAPSo (Laboratorio de Acustica y Percepcion Sonora) at Universidad Nacional de Quilmes (Argentina). Researcher at the Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET Argentina). Contact me me@lapso.org", "public_name": "Manuel Camilo Eguia", "guid": "939e1702-cb85-5750-a03b-2c398b555d87", "url": "https://pretalx.com/juliacon-2026/speaker/MJCQ93/"}, {"code": "YL8UKF", "name": "Mart\u00edn Proscia", "avatar": null, "biography": null, "public_name": "Mart\u00edn Proscia", "guid": "637295f9-c337-5494-9273-d96dac4d6c4c", "url": "https://pretalx.com/juliacon-2026/speaker/YL8UKF/"}, {"code": "TZTEMB", "name": "Dario Ruiz", "avatar": "https://pretalx.com/media/avatars/A3ZBBW_aKLVkTc.webp", "biography": "Saxophonist, composer. Researcher. Interested in acoustic and physical modeling of musical wind instruments. Member of the Laboratory of Acoustics and Sound Perception (UNQ), researcher in the project Sonoridades H\u00edbridas.", "public_name": "Dario Ruiz", "guid": "8dcf1975-b522-5a50-8435-bcfc9a686ae9", "url": "https://pretalx.com/juliacon-2026/speaker/TZTEMB/"}], "links": [{"title": "The `RealTimeAudioDiffEq` package", "url": "https://juliapackages.com/p/realtimeaudiodiffeq", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LDEKJG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LDEKJG/", "attachments": []}, {"guid": "cf3d2471-a7a7-5324-831d-1519d48af059", "code": "CC37CQ", "id": 92483, "logo": "https://pretalx.com/media/juliacon-2026/submissions/CC37CQ/image_P8HGSqT.webp", "date": "2026-08-13T10:15:00+02:00", "start": "10:15", "end": "2026-08-13T10:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92483-the-hidden-path-to-turing-jl-v1-0", "url": "https://pretalx.com/juliacon-2026/talk/CC37CQ/", "title": "The Hidden Path to Turing.jl v1.0", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Turing.jl, a probabilistic programming language, has been undergoing rapid development towards a v1.0 release.\n\nMany new features, fixes, and improvements will have been visible to users \u2014 but arguably the most important things I've learnt are not about code!\n\nIn this talk I'll reflect on what it\u2019s really like to work on open source software, contextualised throughout with recent examples from Turing.jl\u2019s codebase.", "description": "Turing.jl is a probabilistic programming language. Users can define probabilistic models with a simple macro, and then perform inference using a variety of modern techniques, including MCMC sampling, variational inference, and particle methods.\n\nOver the past two years, I and other developers have been redesigning Turing's modelling implementation (technically in the DynamicPPL.jl subpackage), almost completely from scratch. We have introduced completely new data structures, interfaces, and abstractions that can both be extended easily by users, and provide significant performance improvements.\n\nMany of these new features will have been visible to users and the Julia community via the newsletters that we publish regularly. Indeed, there are many software engineering intricacies which are nowadays covered in the DynamicPPL.jl documentation. However, in this talk I'd like to use Turing and DynamicPPL as the _context_, but to focus on all the things that nobody told me when I started working on this project. These include:\n\n- navigating the tension between a paid job and open-source development;\n- whether to trust old wisdom or to break things;\n- why Julia makes it easier and harder to work in open-source;\n- how to build a community of contributors and make development more sustainable.\n\nAs I certainly don't have definitive answers to all of these, I also hope that this can spark some consideration and discussion amongst Julia developers.", "recording_license": "", "do_not_record": false, "persons": [{"code": "MK3VL8", "name": "Penelope Yong", "avatar": "https://pretalx.com/media/avatars/F7NKSD_6E6m46P.webp", "biography": "she/her\n\nhttps://github.com/penelopeysm\n\nAfter a quantum chemistry PhD at Oxford, I worked as a research software engineer at The Alan Turing Institute, which included a two-year stint being a core developer on the Turing.jl ecosystem. I still dabble in MCMC packages from time to time, but nowadays I'm more interested in compilers. I'm now maintaining JuliaFormatter and will be starting a new role at Jane Street working on OCaml libraries after JuliaCon!", "public_name": "Penelope Yong", "guid": "945b1e17-f8e4-5762-a9b6-b309c522527c", "url": "https://pretalx.com/juliacon-2026/speaker/MK3VL8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/CC37CQ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/CC37CQ/", "attachments": []}, {"guid": "57ce99ea-3f21-576a-9860-383992a83f58", "code": "L3QPFT", "id": 91750, "logo": "https://pretalx.com/media/juliacon-2026/submissions/L3QPFT/image_9YkCYwC.webp", "date": "2026-08-13T10:30:00+02:00", "start": "10:30", "end": "2026-08-13T11:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-91750-neural-networks-genetic-algorithms-and-neuroevolution", "url": "https://pretalx.com/juliacon-2026/talk/L3QPFT/", "title": "Neural Networks, Genetic Algorithms, and Neuroevolution", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "**\"Practical Artificial Intelligence in Julia: Build Neural Networks, Genetic Algorithms, and Neuroevolution From Scratch\"** is a new book published by APress and Springer. \n\nThe book is divided into three parts:\n\n- _Neural networks_ are a technique inspired by a simplification of neurons in the brain. Neural networks are useful for identifying complex patterns, classifying data, and predicting outcomes.\n- _Genetic Algorithms_ are a computational metaphor for the biological evolution of species, inspired by the Darwinian principles. Genetic algorithms are useful for finding near-optimal solutions to complex, large-scale, and non-linear optimization and search problems.\n- _Neuroevolution_ is a combination of the two previous parts: a genetic algorithm evolves a neural network. Neural networks produced by Neuroevolution can solve complex problems without being trained using gradient-based methods.\n\nThis talk gives a highlight of these techniques and will demonstrate several applications using the Julia REPL, in particular:\n\n- evolution of an artificial organism able to walk and climb;\n- building an artificial player for a Mario Bros-like game;\n\nThe talk aims to showcase innovative machine learning techniques and applications within the Julia ecosystem.", "description": "The talk will show many examples using the Julia REPL. \nAll source code should be available under an open-source license at the time of the presentation. Presenters will be able to reproduce the demonstrations.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NP37AZ", "name": "Alexandre Bergel", "avatar": "https://pretalx.com/media/avatars/NP37AZ_jJtz6jk.webp", "biography": "[Alexandre Bergel](https://bergel.eu/) is a Computer Scientist at RelationalAI, Switzerland. Until 2022, he was an Associate Professor and researcher at the University of Chile. Alexandre Bergel and his collaborators carry out research in software engineering. His interest includes designing tools and methodologies to improve the overall performance and internal quality of software systems and databases by employing profiling, visualization, and artificial intelligence techniques.\n\nAlexandre Bergel has authored over 170 articles, published in international and peer-reviewed scientific forums, including the most competitive conferences and journals in the field of software engineering. Alexandre has served on over 175 program committees for international events. Several of his research prototypes have been turned into products and adopted by major companies in the semiconductor industry, certification of critical software systems, and the aerospace industry.", "public_name": "Alexandre Bergel", "guid": "7837064b-4f27-516e-b137-f3391bb51a74", "url": "https://pretalx.com/juliacon-2026/speaker/NP37AZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/L3QPFT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/L3QPFT/", "attachments": []}, {"guid": "b8d0956a-88a6-5d53-ba81-7a9463541606", "code": "C8UGPM", "id": 92875, "logo": "https://pretalx.com/media/juliacon-2026/submissions/C8UGPM/image_SK3H2DW.webp", "date": "2026-08-13T11:15:00+02:00", "start": "11:15", "end": "2026-08-13T11:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92875-missing-derivative-the-example-of-betainc-and-betaincinv", "url": "https://pretalx.com/juliacon-2026/talk/C8UGPM/", "title": "Missing derivative: the example of `beta_inc` and `beta_inc_inv`", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Automatic differentiation (AD) is deeply embedded in the Julia ecosystem. Thanks to dual numbers and generic programming, derivatives often \u201cjust work\u201d across packages. However, this is not always the case. In certain situations (e.g., when transcendental functions are evaluated via partial fraction expansions) propagating dual numbers through the implementation may fail, and for good numerical reasons.\n\nIn this talk, we present the case of `beta_inc` and `beta_inc_inv` from `SpecialFunctions.jl`. Their original implementations relied on partial fraction expansions carefully designed for `Float64` evaluation. While this approach yields numerically stable function values, it does not automatically provide correct derivatives under AD. Crucially, differentiating the partial fraction expansion is not equivalent to computing the partial fraction expansion of the derivative \u2014 the latter being significantly more involved.\n\nDrawing from the numerical analysis literature (this challenge is not Julia-specific), we implemented exact derivatives for these functions, as proposed in pull request #506. This work enables the standard automatic differentiation tools to handle these functions seemlessly. \n\nThese derivatives are central to e.g. statistical computing: they are used in the Beta cumulative distribution and quantile functions, the Student\u2019s t cumulative distribution and quantile functions, and most importantly for us the multivariate Student\u2019s t distribution, which has been asked about several time on discourse. Prior to this work, fully differentiable implementations of these models were not available in Distributions.jl.\n\nThis talk goes through the story of `SpecialFunctions.jl`'s pull request #506 titled \u201cExact chainrules derivatives for beta_inc and beta_inc_inv\u201d, which solves all these issues and will hopefully be merged by Juliacon.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "A77EDZ", "name": "Oskar Laverny", "avatar": "https://pretalx.com/media/avatars/E9PVET_vFqKEVA.webp", "biography": "I am currently an associate professor in statistics in Marseille (France). Actuary by formation, I focus my researches on high dimensional statistics and dependence structures estimations, with a lot of applications in insurance, reinsurance, and more recently public health. I do have a taste for numerical code and open-source software, and most of my work is freely available on GitHub.", "public_name": "Oskar Laverny", "guid": "afa9e373-10c2-5b21-b05a-e9ae391128c7", "url": "https://pretalx.com/juliacon-2026/speaker/A77EDZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/C8UGPM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/C8UGPM/", "attachments": []}, {"guid": "0e3fe077-ca07-57a3-ab83-5ce9aaeeaa89", "code": "W3Z3FB", "id": 93394, "logo": "https://pretalx.com/media/juliacon-2026/submissions/W3Z3FB/image_V2K2RFK.webp", "date": "2026-08-13T11:30:00+02:00", "start": "11:30", "end": "2026-08-13T12:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93394-real-time-analysis-of-xfel-data", "url": "https://pretalx.com/juliacon-2026/talk/W3Z3FB/", "title": "Real-time analysis of XFEL data", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "X-ray Free Electron Lasers (XFELs) are the latest generation of light sources, designed specifically to create coherent x-ray radiation for scientific experiments. During these experiments it's critical to get feedback in real-time to assess how the experiment is proceeding. In this talk we will describe how Julia is used for doing such real-time analysis at the European XFEL.", "description": "Light sources are facilities designed to create coherent x-ray radiation for scientific experiments. The majority of light sources around the world are synchrotrons, which use a circular particle accelerator to create radiation. X-ray Free Electron Lasers (XFELs) are the next generation of light sources that can produce orders of magnitude higher peak radiation intensity for experiments. Though compared to synchrotrons they can also produce orders of magnitudes more data to analyze, which presents a significant challenge.\n\nOne of the most powerful XFELs today is the European XFEL in Hamburg, Germany. It's a user facility that invites external scientists to perform experiments in fields ranging from structural biology, to materials science, to attosecond physics. The scientists who come to the facility to do experiments typically get 1 week or less to setup the experiment and take data, so having fast feedback in real-time is critical to assess how the experiment is going. In this talk we will describe how Julia is used for doing such real-time analysis at the European XFEL.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UAEVFK", "name": "James Wrigley", "avatar": "https://pretalx.com/media/avatars/8RD8DY_Jn8xYZf.webp", "biography": "Data scientist at the European XFEL. Likes sleeping.", "public_name": "James Wrigley", "guid": "87fd9f32-ad20-5070-b65a-95bd80d24062", "url": "https://pretalx.com/juliacon-2026/speaker/UAEVFK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/W3Z3FB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/W3Z3FB/", "attachments": []}, {"guid": "b2099262-2437-5bda-adef-4a9c2c117b5c", "code": "XZDBKL", "id": 92688, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XZDBKL/image_GE0vRux.webp", "date": "2026-08-13T12:00:00+02:00", "start": "12:00", "end": "2026-08-13T12:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92688-structuralequationmodels-jl-an-efficient-and-extensible-framework-for-structural-equation-modeling", "url": "https://pretalx.com/juliacon-2026/talk/XZDBKL/", "title": "StructuralEquationModels.jl: An Efficient and Extensible Framework for Structural Equation Modeling", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Structural Equation Models (SEMs) are a powerful statistical framework for disentangling complex relationships between multiple factors that affect empirical data. StructuralEquationModels.jl implements SEMs in Julia, offering greater efficiency and extensibility compared to implementations in other languages. Improved efficiency enables the application of SEM to large networks of variables (such as in genetics) and to fit many models in parallel (such as in neuroimaging). Improved extensibility permits adapting SEMs to specific use cases by modifying loss functions or integrating with various numerical optimization backends.", "description": "Structural Equation Models (SEMs) are a class of statistical models applicable to a wide range of modeling tasks, such as time-series analysis, psychometric questionnaires, mediation models, multiple regression models, and multilevel models. This leads to applications across diverse scientific disciplines, including psychology, genetic epidemiology, social science, and neuroscience.\n\nStructuralEquationModels.jl implements SEMs leveraging the Julia package ecosystem, including advanced numerical optimization algorithms, symbolic algebra systems, and support for sparse matrices. This results in two primary benefits over existing open-source implementations in other languages: efficiency and extensibility.\n\nEfficiency refers to the significantly reduced computation time required to estimate SEM parameters compared to other SEM software packages. Improved efficiency allows using SEM for large networks of variables (such as in genetics) or for fitting many models in parallel (such as in neuroimaging or high-throughput experiments in general). Extensibility is achieved through a high degree of composability enabled by multiple dispatch. It allows users to easily add new features to the software to adapt SEMs to specific use cases, such as employing specialized numerical optimization algorithms for regularized estimation of high-dimensional problems.\n\nTogether, these features make StructuralEquationModels.jl a valuable tool for rapid prototyping, integration with diverse optimization backends, analysis of large networks of thousands of variables, and high-performance computing applications involving millions of models.\n\nThis presentation will\n\n* introduce the fundamentals of structural equation modeling and present selected use cases,\n* show how to use StructuralEquationModels.jl to specify models and analyze data,\n* explain how the package leverages the Julia ecosystem for efficiency and extensibility,\n* compare its computational efficiency to other SEM implementations,\n* and provide an outlook on planned additions to the package.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BWSUBW", "name": "Maximilian Ernst", "avatar": "https://pretalx.com/media/avatars/DNGPAK_uESONQu.webp", "biography": "PhD student at the Center for Lifespan Psychology, Max-Planck-Institute for Human Development, Berlin", "public_name": "Maximilian Ernst", "guid": "0e811e63-8496-52bf-917f-faffdd860452", "url": "https://pretalx.com/juliacon-2026/speaker/BWSUBW/"}, {"code": "KEWMU9", "name": "Aaron Peiket", "avatar": null, "biography": null, "public_name": "Aaron Peiket", "guid": "cf3e27e5-c839-5066-80b8-2a917891e697", "url": "https://pretalx.com/juliacon-2026/speaker/KEWMU9/"}], "links": [{"title": "GitHub", "url": "https://github.com/StructuralEquationModels/StructuralEquationModels.jl", "type": "related"}, {"title": "Preprint", "url": "https://osf.io/preprints/psyarxiv/zwe8g_v1", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XZDBKL/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XZDBKL/", "attachments": [{"title": "flyer-XZDBKL", "url": "/media/juliacon-2026/submissions/XZDBKL/resources/XZDBKL_uv4Vgvm.png", "type": "related"}]}, {"guid": "95857b46-c41a-55db-b2d3-69e5c3d73033", "code": "VXYAQY", "id": 92569, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VXYAQY/image_C1KRiLj.webp", "date": "2026-08-13T12:30:00+02:00", "start": "12:30", "end": "2026-08-13T12:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92569-symbolic-post-hoc-analysis-with-soleposthoc-jl", "url": "https://pretalx.com/juliacon-2026/talk/VXYAQY/", "title": "Symbolic post-hoc analysis with SolePostHoc.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Symbolic learning is a branch of machine learning focused on building classifiers that can be translated into logical rules, making them far more readable than neural networks or other statistical models. While training a symbolic model is a necessary first step, it is the post-processing stage that yields the most relevant insights. We present a live walkthrough of SolePostHoc.jl, a SOLE package dedicated to post-processing, allowing for rule extraction, boosting and model simplification.", "description": "Symbolic learning studies algorithms that produce models expressible as logical rules. Common examples include decision trees and random forests, whose predictions can be traced back to human-readable conditions on the input data. This interpretability makes symbolic models particularly valuable in domains where understanding *why* a model makes a decision matters as much as the decision itself.\n\nThis session is structured as a hands-on demo on the SolePostHoc.jl package, which serves as the post-hoc analysis layer of the SOLE ecosystem. Starting from training a decision tree on a real dataset, we walk through what SolePostHoc.jl allows from that point onward:\n\n- **Rule extraction**: deriving explicit logical rules from a symbolic model through a uniform interface, supporting multiple extraction algorithms;\n- **Rule simplification and manipulation**: collapsing redundant or overlapping rules into more succinct, equally expressive theories; for instance, reducing `IF A AND B THEN C; IF A AND NOT B THEN C` to the simpler (but equivalent) `IF A THEN C`;\n- **Surrogate methods and knowledge distillation**: approximating more complex models with interpretable surrogates, transferring knowledge into transparent representations;\n- **Model boosting and certification**: enhancing model capabilities boosting performance on a specified metric or ensuring the satisfiability of a desired feature or constraint.\n\nArguably, post-hoc analysis is the stage where symbolic learning delivers on its core promise: turning a trained model into usable, understandable knowledge. SolePostHoc.jl aims to give this stage the attention it deserves, providing a reliable and extensible toolkit for anyone interested in interpretability and explainability, from newcomers to experts looking for a fully customizable system.", "recording_license": "", "do_not_record": false, "persons": [{"code": "RGAKE9", "name": "Marco Perrotta", "avatar": "https://pretalx.com/media/avatars/MYUTRS_FqrYiCb.webp", "biography": "Hi, my name is Marco Perrotta. I'm a master student in computer science at the University of Ferrara, where I also work as a collaborator at the Applied Computational Logic and Artificial Intelligence Lab. My main interest is how technology can be used to understand and study language.", "public_name": "Marco Perrotta", "guid": "75d1feef-a60b-5dc5-a476-db7b24ae76c6", "url": "https://pretalx.com/juliacon-2026/speaker/RGAKE9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VXYAQY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VXYAQY/", "attachments": []}, {"guid": "45de81bb-efda-57f6-a7c4-e5cbb2bd99da", "code": "HQJMLW", "id": 92726, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HQJMLW/image_EGote9y.webp", "date": "2026-08-13T12:45:00+02:00", "start": "12:45", "end": "2026-08-13T13:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92726-bayesian-calibration-using-turing-jl-a-flexible-framework-for-experimental-data-assimilation", "url": "https://pretalx.com/juliacon-2026/talk/HQJMLW/", "title": "Bayesian Calibration using Turing.jl: A Flexible Framework for Experimental Data Assimilation", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "We present a Bayesian calibration framework built on `Turing.jl` for statistically rigorous data assimilation. To scale inference with Gaussian Processes, we employ a Bayesian Committee Machine approach, and exploit parallelism across both CPU and GPU backends.\nWe demonstrate the framework on both analytical and real-world data from accelerator physics, highlighting speedups. \nThe result is a practical, flexible toolkit designed for rapid model updating and calibration.", "description": "Data assimilation is an essential step in bridging the gap between simulation and experiment, playing a central role in iterative experimental design. Bayesian Calibration provides a statistically rigorous approach to infer simulation parameters. In experimental settings, i.e. accelerator physics, practitioners need inference frameworks that are not only adaptable but also fast enough to inform the next round of measurements.\n\nWe present a Bayesian calibration framework built on `Turing.jl`, leveraging its composable and easily modifiable model specification to accommodate a wide range of experimental configurations and problem settings. The framework includes hierarchical modelling to capture the full parameter posterior over multiple experiments. To scale inference with Gaussian Processes, we employ a Bayesian Committee Machine approach, and we exploit parallelism across both CPU and GPU backends. Sampling is performed using NUTS and Metropolis-Hastings.\n\nWe demonstrate the framework in both analytic test cases and real-world data from accelerator physics, highlighting achievable speedups, and discussing current limitations and roadblocks for GPU-accelerated sampling in `Turing.jl`. The result is a practical, flexible toolkit designed to sit inside the experimental iteration loop enabling rapid model updating and calibration.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ULEAHS", "name": "Sebastian Heinekamp", "avatar": null, "biography": "I'm a PhD student in the Accelerator Modelling Group at the Paul Scherrer Institute. My work focuses on Bayesian statistics and data assimilation, leveraging Julia and the Turing.jl, in the context of particle accelerator experiments.", "public_name": "Sebastian Heinekamp", "guid": "a81ff891-e376-59ad-bb74-ee3e1d124e72", "url": "https://pretalx.com/juliacon-2026/speaker/ULEAHS/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HQJMLW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HQJMLW/", "attachments": []}, {"guid": "a4f81988-4ac0-5d69-84f2-4798937021f9", "code": "THBSKY", "id": 92698, "logo": "https://pretalx.com/media/juliacon-2026/submissions/THBSKY/image_bEY4tWx.webp", "date": "2026-08-13T14:30:00+02:00", "start": "14:30", "end": "2026-08-13T15:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92698-elemco-jl-a-julia-package-for-electron-correlation-in-molecules-and-materials", "url": "https://pretalx.com/juliacon-2026/talk/THBSKY/", "title": "ElemCo.jl: A Julia Package for Electron Correlation in Molecules and Materials", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Long talk", "language": "en", "abstract": "Efficiently obtaining high-accuracy data for molecules and materials is one of the major challenges in quantum chemistry. We present ElemCo.jl, an efficient, user-friendly, modular, open-source Julia package for performing quantum-chemical calculations, including Hartree-Fock (HF) and post-HF methods, with a focus on Coupled Cluster theory. Particular emphasis will be placed on tensor-decomposed Distinguishable Cluster methods to treat extended systems at substantially reduced computational cost.", "description": "Efficiently obtaining high-accuracy data for molecules and materials is one of the major challenges in quantum chemistry. To address this challenge, we present the Julia package ElemCo.jl [1] for performing both established as well as novel electronic structure methods. The program enables mean-field as well as correlation energy calculations, with an emphasis on Coupled and Distinguishable [2] Cluster methods for ground and excited states. \n\nTo capture the interest of a broader audience, the talk will be divided into two parts: In the first half, an introduction to the package will be given, including an overview of the program's structure, currently available methods, and compatibility with other quantum-chemical programs.\n \nIn the second half, tensor decomposition will be introduced as a tool to substantially reduce the cost of various Distinguishable Cluster methods [3] for extended molecular systems. This is achieved through tensor decomposition of the amplitudes, resulting in a drastic compression of the amplitude space and a reduction in both computational scaling and prefactor, without compromising accuracy. In particular, the decomposition will be applied to DC-CCSDT [4] and EOM-DCSD [5].\n\n[1] GitHub: https://github.com/fkfest/ElemCo.jl and website: https://elem.co.il/\n[2] D.\u2008Kats and F. R.\u2008Manby, _J. Chem. Phys._\u2008139, 021102 (2013).\n[3] C. Rickert, D. Usvyat and D. Kats, _J. Chem. Phys._ 163, 064103 (2025). \n      S. Lambie, C. Rickert, D. Usvyat, A. Alavi and D. Kats, J. Chem. Phys. 163, 111101 (2025).\n[4] D. Kats and A. K\u00f6hn, _J. Chem. Phys._ 150, 151101 (2019).\n[5] V. Rishi, A. Perera, M. Nooijen, R. Bartlett, _J. Chem. Phys._ 146, 144104 (2017).", "recording_license": "", "do_not_record": false, "persons": [{"code": "WFL8DE", "name": "Charlotte Rickert", "avatar": null, "biography": "At the moment, I am pursuing a Master's degree in chemistry with a focus on computational and theoretical chemistry at the Humboldt-Universit\u00e4t zu Berlin (HU). Research is my passion: I enjoy developing low-scaling, high-level wavefunction-based methods such as Local or SVD Coupled Cluster for molecules and periodic systems. In this context, I have been actively doing research since 3.5 years at HU and am currently working as a student research assistant at the Max-Planck Institute for Solid State Research.", "public_name": "Charlotte Rickert", "guid": "c2796695-4114-5c8a-82b5-992ac9fa427a", "url": "https://pretalx.com/juliacon-2026/speaker/WFL8DE/"}, {"code": "SFA7LB", "name": "Daniel Kats", "avatar": null, "biography": "Scientist at the Max Planck Institute for Solid State Research in the Department for Electronic Structure Theory. Main research area: method development to accurately describe electron correlation in molecules and periodic systems.", "public_name": "Daniel Kats", "guid": "88a44826-aede-5d91-8814-c6f531b4a5a8", "url": "https://pretalx.com/juliacon-2026/speaker/SFA7LB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/THBSKY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/THBSKY/", "attachments": [{"title": "flyer-THBSKY", "url": "/media/juliacon-2026/submissions/THBSKY/resources/THBSKY_LWNL2ud.png", "type": "related"}]}, {"guid": "4eee3b3e-835e-5eab-a279-96dc84f0ae74", "code": "BLZJJW", "id": 91943, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BLZJJW/image_3vla3dU.webp", "date": "2026-08-13T15:00:00+02:00", "start": "15:00", "end": "2026-08-13T15:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-91943-ml-accelerated-simulation-of-laser-driven-hydrogen-evolution-with-nqcdynamics-jl-julia-and-python-in-harmony", "url": "https://pretalx.com/juliacon-2026/talk/BLZJJW/", "title": "ML-accelerated simulation of laser-driven hydrogen evolution with NQCDynamics.jl \u2013\u00a0Julia and Python in harmony?", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Long talk", "language": "en", "abstract": "I would like to show you how I combine Python- and Julia-based machine learning models into a scalable molecular dynamics (MD) simulation workflow using the NQCDynamics.jl package. \nMy research focuses on simulating light-driven chemistry on metal surfaces, where non-adiabatic coupling between electronic and nuclear motion requires custom MD methods.\nIf you are in the field of non-adiabatic / excited state dynamics, I hope I can show you why this might be interesting for your own simulations.", "description": "**NQCDynamics.jl**\u00b2 is a non-adiabatic molecular dynamics package our research group has built to facilitate development and large-scale simulations using new **mixed quantum classical dynamics methods** which go beyond the Born-Oppenheimer approximation. \n\nThese methods are necessary to describe the **coupling between light, electrons and phonons at interfaces**, where ultrafast laser pulses can drive chemical reactivity by inducing mode-selective energy transfer more efficiently than comparable thermal heating.\u00b9\nHowever, computationally simulating the multitude of simultaneous processes occurring at different time scales *ab-initio* remains a challenge.\u00b3\nUsing interfaces to **Julia- and Python-based ML models working in tandem**\u2074, I am able to simulate the effect of ultrafast laser pulses on hydrogen evolution from copper surfaces efficiently at a large scale.\n\nNQCDynamics.jl provides a framework for initialising dynamics simulations and propagating the equations of motion using DifferentialEquations.jl, with a number of **interfaces** to the wider Julia- and Python-based **molecular simulation ecosystem**. \n\nAfter sampling rare reactive dynamics from thousands of simulations, I can compare energy partitioning in desorbed hydrogen molecules, concluding that the choice of electronic friction approximation only determines the rate of energy transfer, while the energy distributions of desorbing molecules are governed by the potential energy surface. This suggests that **thermal and laser-driven desorption may yield similar outcomes at low coverage**.\n\n[1]: S. W. Lee, Appl. Surf. Sci. Adv. **16**, 100428 (2023).\n[2]: J. Gardner _et al._, J. Chem. Phys. **156**, 174801 (2022).\n[3]: C. Frischkorn and M. Wolf, Chem. Rev. **106**, 4207 (2006).\n[4]: W. G. Stark _et al._, Phys. Rev. B. **112**, 2 (2025).", "recording_license": "", "do_not_record": false, "persons": [{"code": "TEQD9C", "name": "Alexander Spears", "avatar": "https://pretalx.com/media/avatars/TEQD9C_NtAjo3p.webp", "biography": "I am a third-year Physics PhD student at the University of Vienna in the research group of Prof. Reinhard Maurer. \n\nMy research focuses on the simulation of light-driven hydrogen evolution. A number of experiments using ultra-fast laser pulses have indicated that the transfer of energy from light into molecular degrees of freedom is more selective than under purely thermal conditions, potentially enabling more efficient catalysis.\n\nThe complex interactions between electrons, light and adsorbate molecules at metal surfaces are not well understood, both due to the computational power required to simulate molecular dynamics outside the Born-Oppenheimer approximation, as well as the different time scales of electronic, lattice and electromagnetic responses.\n\nA variety of methods has been developed to include the effects of electron-nuclear coupling in classical molecular dynamics, such as electronic friction or surface hopping.\n\nIn combination with machine-learning methods to calculate interatomic potentials and other parameters, I hope to simulate non-thermal hydrogen surface chemistry in adequate detail at reduced computational cost.\n\nTo better capture light-matter interactions at a sub-picosecond scale, I will attempt to use and improve methods to describe light excitations beyond the methods currently used to verify experimental results.", "public_name": "Alexander Spears", "guid": "8f0e1fca-c3df-52e6-9a9f-93cc01d2149e", "url": "https://pretalx.com/juliacon-2026/speaker/TEQD9C/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BLZJJW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BLZJJW/", "attachments": []}, {"guid": "bae5df67-9935-5fc6-a8c8-9aa4776d1f16", "code": "GXPBHY", "id": 92699, "logo": "https://pretalx.com/media/juliacon-2026/submissions/GXPBHY/image_04TX30m.webp", "date": "2026-08-13T15:30:00+02:00", "start": "15:30", "end": "2026-08-13T16:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92699-computing-transport-coefficients-using-molly-jl", "url": "https://pretalx.com/juliacon-2026/talk/GXPBHY/", "title": "Computing transport coefficients using Molly.jl", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Long talk", "language": "en", "abstract": "Transport coefficients are quantities measuring sensitivities in various fluxes for equilibrium molecular systems subject to thermodynamic forcings driving them out of equilibrium. Typical examples are diffusivity, shear viscosity or thermal conductivity, which enter as parameters in macroscopic models of fluids and materials. Unfortunately, these are notoriously difficult to compute, and there is still a need to develop more efficient algorithms.\n\nWe will present three algorithms to compute transport coefficients in stochastic MD: the Green-Kubo method, the nonequilibrium molecular dynamics (NEMD) method, and the constant-flux approach recently proposed in [this work](https://doi.org/10.1007/s10955-024-03230-x).\n\nWe discuss how [Molly.jl](https://juliamolsim.github.io/Molly.jl/stable/), with its highly extensible design, allows to rapidly implement these methods and assess their relative performance. This leads in particular to discover promising properties of the constant-flux approach, narrowing the gap between mathematical ideas in statistical physics and applications in computational science.\n\nThis is joint work with Gabriel Stoltz.", "description": "#### Computing transport coefficients using Molly.jl\n\nTransport coefficients \u2014 diffusivity, shear viscosity, thermal conductivity \u2014 measure how molecular systems respond to small thermodynamic forcings driving them out of equilibrium. They enter as parameters in macroscopic models such as the Navier\u2013Stokes equations and are essential for bridging atomistic simulations with continuum-scale predictions. Unfortunately, estimating these quantities from molecular dynamics simulations is notoriously expensive: standard methods suffer from large statistical errors and require very long simulation times.\n\nWe present three algorithms to compute transport coefficients for systems governed by stochastic (Langevin) dynamics:\n\n- **Green\u2013Kubo**: an equilibrium method based on integrated time-correlation functions.\n- **NEMD (non-equilibrium molecular dynamics)**: the standard approach of applying a fixed external forcing and measuring the average flux response.\n- **Constant-flux (Norton) method**: a recently proposed dual approach (Blassel & Stoltz, *J. Stat. Phys.*, 2024) that instead *fixes the flux* and measures the average forcing required to maintain it, inverting the usual NEMD philosophy.\n\nWe implement the constant-flux method in `Molly.jl`, via a custom simulator type (`NortonSplitting`) that constructs flux-preserving splitting schemes, which also give natural estimates of the average forcing. The NEMD and Green\u2013Kubo methods rely on custom interaction types and Molly's built-in logging capabilities.\n\nWe discuss how [Molly.jl](https://juliamolsim.github.io/Molly.jl/stable/), with its modular design \u2014 user-definable simulators, interactions, and loggers \u2014 allows all three methods to be implemented with remarkably low overhead, enabling rapid prototyping and benchmarking of novel algorithms. On the test case of shear viscosity for a Lennard\u2013Jones fluid, the constant-flux approach shows promising properties: faster decay of correlations and an anomalous variance concentration rate ( $N^{-5/3}$ vs. the standard $N^{-1}$ ), leading to lower asymptotic variance and better computational efficiency. These findings illustrate how Molly.jl's extensibility helps narrow the gap between mathematical ideas in statistical physics and practical applications in computational science.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7JBA9S", "name": "No\u00e9 Blassel", "avatar": null, "biography": "I am a postdoctoral researcher in the institute of Mathematics of EPFL working within the MatMat group on data-driven methods in quantum chemistry.\n\nI recently defended my PhD thesis in Mathematics at the CERMICS lab of \u00c9cole Nationale des Ponts et Chauss\u00e9es, in the MATHERIALS INRIA research project. During my PhD, I studied and constructed algorithms in molecular dynamics to accelerate the sampling of rare events (such as transitions between metastable configurations) and the computation of transport coefficients.", "public_name": "No\u00e9 Blassel", "guid": "14cab198-304a-5fa3-935f-1f3c162c8407", "url": "https://pretalx.com/juliacon-2026/speaker/7JBA9S/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/GXPBHY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/GXPBHY/", "attachments": []}, {"guid": "0e328457-07d2-50b5-a8ce-3a0d6765cdf7", "code": "SEDPHP", "id": 92638, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SEDPHP/image_JTWVVoX.webp", "date": "2026-08-13T16:00:00+02:00", "start": "16:00", "end": "2026-08-13T16:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92638-boltztrap-jl-thermoelectric-transport-for-the-julia-dft-ecosystem", "url": "https://pretalx.com/juliacon-2026/talk/SEDPHP/", "title": "BoltzTraP.jl: Thermoelectric transport for the Julia DFT ecosystem", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Short talk", "language": "en", "abstract": "Seebeck coefficient, electrical conductivity, and thermal conductivity are key parameters for thermoelectric design. `BoltzTraP.jl` is a Julia port of BoltzTraP2, the standard tool for computing these via the Boltzmann transport equation, ensuring numerical equivalence with Julia-native optimizations. It accepts input from major DFT codes and `DFTK.jl`, enabling an all-Julia workflow from electronic structure to transport properties. In-memory calculations show over 2x speedup over BoltzTraP2.", "description": "Thermoelectric materials convert heat directly into electricity and vice versa, enabling applications in waste heat recovery, solid-state cooling, and remote power generation. The efficiency of thermoelectric devices depends on the figure of merit zT, which is determined by the Seebeck coefficient, electrical conductivity, and thermal conductivity. Accurate prediction of these transport coefficients from first-principles calculations is essential for discovering and optimizing new thermoelectric materials.\n\n[`BoltzTraP.jl`](https://github.com/hsugawa8651/BoltzTraP.jl) is a Julia implementation of the [BoltzTraP2 algorithm](https://dx.doi.org/10.1016/j.cpc.2018.05.010) for calculating semi-classical transport coefficients from density functional theory (DFT) band structures. \nThe package interpolates band energies using a Fourier expansion in reciprocal space, reconstructs the bands on a dense k-point grid via FFT, and computes transport tensors using the Boltzmann transport equation in the constant relaxation time approximation.\n\n`BoltzTraP.jl` is a faithful Julia port that implements the exact same algorithm as BoltzTraP2, producing numerically equivalent results. The motivation for this port is integration with the growing Julia ecosystem for materials science:\n\n- **Julia ecosystem integration**: `BoltzTraP.jl` integrates natively with Julia packages like [DFTK.jl](https://doi.org/10.21105/jcon.00069), enabling direct transport calculations from DFTK self-consistent field results without intermediate files or language bridges. It also accepts output data from major DFT programs including VASP, Wien2K, Quantum Espresso, and ABinit.\n- **Pure Julia, no compilation**: BoltzTraP2 requires compiling a C++ extension, which can fail on some systems\u2014particularly on HPC clusters with non-standard compiler configurations. `BoltzTraP.jl` has no external compiled dependencies.\n- **HPC-friendly**: Julia's package manager handles dependencies without conda/pip conflicts common on shared HPC systems. No C++ compilation means no compiler version mismatches. As pure Julia code, `BoltzTraP.jl` is designed to be highly portable and should run on any system where Julia is available, from personal laptops to high-performance computing (HPC) clusters.\n\n\n## Validation, Performance, Future Directions\n\n\n`BoltzTraP.jl` was developed using reference testing against BoltzTraP2,\nthe de facto standard for computing transport coefficients from DFT calculations, with over 2,000 citations.\nThe accompanying figure compares transport coefficients computed by both codes for silicon at 300 K \nbased on the DFT data calculated by VASP. \nAll transport coefficients (electrical conductivity \u03c3, Seebeck coefficient S, thermal conductivity \u03ba) match within numerical precision (< 1e-6 relative error), \ndemonstrating `BoltzTraP.jl` faithfully reproduces the original Python implementation.\n\n[Benchmarks on a MacBook Pro (Apple M2) show 1.8-3.4x speedup](https://github.com/hsugawa8651/BoltzTraP.jl/blob/main/paper/benchmark.png) in end-to-end calculation (interpolation + integration) for silicon (1102 equivalence classes, 6 bands), enabling rapid screening of thermoelectric properties.\n\nWe are currently expanding the range of materials this package can handle to include collinear magnetic materials. Furthermore, we plan to integrate Wannier.jl to implement Wannier interpolation and reduce eigenvalue misidentification.\n\n[A Google Colab notebook](https://colab.research.google.com/gist/hsugawa8651/1bf32bb4cc3f38074f798fcac5c21d5a/d.ipynb) is available demonstrating the full workflow from band interpolation to transport coefficients, including an all-Julia pipeline with DFTK.jl.\n\nThis package is open to contributions and designed for extensibility.\n\n## Links\n- BoltzTraP.jl: https://github.com/hsugawa8651/BoltzTraP.jl, DOI: 10.5281/zenodo.18605978\n- Docs: https://hsugawa8651.github.io/BoltzTraP.jl/dev/\n- Colab: https://colab.research.google.com/gist/hsugawa8651/1bf32bb4cc3f38074f798fcac5c21d5a/d.ipynb", "recording_license": "", "do_not_record": false, "persons": [{"code": "K3MXFP", "name": "Hiroharu Sugawara", "avatar": null, "biography": "Hiroharu Sugawara is an associate professor in the Graduate School of Systems Design at Tokyo Metropolitan University, Tokyo, Japan. He received his Ph.D. in electronic engineering from the University of Tokyo in 1994. \nHis research focuses on eco-friendly semiconductor functional materials. \nHe has been a Julia user since Julia 0.5. \nHe has been teaching a programming exercise course using the Julia language for university freshmen in the Department of Mechanical Systems Engineering every year since the 2018 academic year.\n\nHe translated Tanmay Bakshi's \"Tanmay Teaches Julia for Beginners\" into Japanese ([ISBN 978-4807920211](https://www.tkd-pbl.com/book/b598314.html)) in 2022.", "public_name": "Hiroharu Sugawara", "guid": "1c4f99fb-cef5-579a-af6c-f206e0f9c60c", "url": "https://pretalx.com/juliacon-2026/speaker/K3MXFP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SEDPHP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SEDPHP/", "attachments": [{"title": "flyer-SEDPHP", "url": "/media/juliacon-2026/submissions/SEDPHP/resources/SEDPHP_DSertJf.png", "type": "related"}]}, {"guid": "68c2c80a-ec87-525a-9807-62b5a994db0c", "code": "ZLZ8FJ", "id": 92722, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZLZ8FJ/image_TMIYMvH.webp", "date": "2026-08-13T16:15:00+02:00", "start": "16:15", "end": "2026-08-13T16:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92722-algorithmic-differentiation-and-error-control-with-dftk", "url": "https://pretalx.com/juliacon-2026/talk/ZLZ8FJ/", "title": "Algorithmic differentiation and error control with DFTK", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Short talk", "language": "en", "abstract": "The Density-Functional ToolKit (DFTK) is a Julia package providing routines to compute the electronic structure of a bulk material and related properties, using plane-wave density functional theory (DFT). Many material properties of interest can be expressed as derivatives of simulation outputs wrt. input parameters, and typically only specific combinations are implemented by DFT codes, as a result of great programming effort to hand-implement all the required derivative terms. In DFTK however, derivatives of **any** output quantity wrt. **any** input parameter can be computed, using algorithmic differentiation (AD) combined with density-functional perturbation theory (DFPT). This results in a general AD-DFPT framework [1] that can only be used to compute both standard and novel derivatives, with promising applications including gradient-based optimization and error propagation.\n\nIn the first part of this talk, I will discuss the key ideas behind this implementation, showing how we offload tedious derivative computations to the AD framework, while keeping the numerics under control thanks to the underlying DFPT solver. The overall strategy is quite general, and should be applicable in other fields as well. In the second part of this talk, I will present new research directions enabled by AD-DFPT. In particular, I will focus on the propagation of model parameter uncertainty \nand estimated numerical errors all the way to predicted physical quantities.\n\n[1]: N. F. Schmitz, B. Ploumhans, M. F. Herbst. _npj Comput Mater_ **12**, 6 (2026). https://doi.org/10.1038/s41524-025-01880-3", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "PLZXRD", "name": "Bruno Ploumhans", "avatar": "https://pretalx.com/media/avatars/BWCJTD_bBOCwQt.webp", "biography": "I am a PhD student from EPFL, Switzerland, working in the Mathematics for Materials Modelling group. With Prof. Michael Herbst we work on numerical simulations to solve the electronic structure problem in solid materials. Talk to me about: algorithmic differentiation, density-functional theory, numerical analysis, quantum chemistry!", "public_name": "Bruno Ploumhans", "guid": "64280bc9-cfad-5ad5-8926-54f74c22cd9a", "url": "https://pretalx.com/juliacon-2026/speaker/PLZXRD/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZLZ8FJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZLZ8FJ/", "attachments": []}, {"guid": "22307884-160a-5be4-b6ff-4327655f155b", "code": "ZPDSRG", "id": 92889, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZPDSRG/image_fBwVqkq.webp", "date": "2026-08-13T16:30:00+02:00", "start": "16:30", "end": "2026-08-13T16:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92889-simulation-of-light-driven-hot-carrier-dynamics-transport", "url": "https://pretalx.com/juliacon-2026/talk/ZPDSRG/", "title": "Simulation of light-driven hot carrier dynamics & transport", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Short talk", "language": "en", "abstract": "Here we present, LightMatter.jl, a flexible and efficient framework for simulations of nonequilibrium electron dynamics triggered by light. By leveraging Julia\u2019s powerful metaprogramming capabilities, it dynamically assembles and propagates scattering equations for different physical processes, offering fine control over accuracy and computational cost. Herein, I present its application in the study of laser-driven electron and phonon equilibration in metals showcasing it's power to model complex nanoscale materials.", "description": "Light-matter interactions are fundamental to a wide range of natural and technological processes, from photosynthesis and vision to photovoltaics and photocatalysis. Understanding how light drives matter out of thermodynamic equilibrium and leads to electronic and phononic transport phenomena is crucial for developing efficient optical sensors, nanolithography, and quantum technologies. These interactions govern key phenomena such as plasmonic excitations, energy transfer, and non-radiative relaxation, all of which play a critical role in spectroscopy, materials science, and nanophotonics. \n\nLightMatter.jl provides a framework for the simulation of the time-dependent evolution of the electronic energy distribution due to laser excitation in metals. The aim of the package is to enable users to design simulations that capture the physics of interest to their required level of theory. LightMatter.jl uses metaprogramming within Julia to construct a custom coupled set of ordinary differential equations which can then be propagated using DiffEq.jl. The metaprogramming also enables users to develop their own methodologies by exchanging components of the expression that describe different physical phenomena for custom functions or approximations. \n\nCurrently the package contains capabilities to perform energy-resolved Boltzmann equations (B. Y. Mueller \\& B. Rethfeld, Phys. Rev. B 2013) , the Two-Temperature Model (S. I. Anisimov et al., Sov. Phys. JETP 1974), the Athermal Electron Model, and time-dependent Schr\u00f6dinger equation (TDSE) for a given Hamiltonian in the dipole approximation. The package is designed in such a way that components of the theories such as lifetimes, parameters and matrix elements can easily be implemented and tested while accessing all the other features.", "recording_license": "", "do_not_record": false, "persons": [{"code": "C83CVK", "name": "Henry Snowden", "avatar": "https://pretalx.com/media/avatars/C83CVK_PxoDJKy.webp", "biography": "I am a PhD student in the Maurer group, primarily focused on developing methods to simulate light-driven surface chemistry. This is a multi-faceted simulation with dependency on accurately capturing a multitude of properties. These include the light-matter simulations themselves, the resulting non-adiabatic dynamics, as well as the electronic structure of the adsorbate and surface in both the ground and excited states. I have a passion for fast, flexible and modern code which is why I love the Julia language and hope to generate a multitude of packages to simulate surface chemistry in Julia. For some examples, see [NQCDynamics.jl](https://nqcd.github.io/NQCDynamics.jl/stable/) and [LightMatter.jl](https://github.com/maurergroup/LightMatter.jl).", "public_name": "Henry Snowden", "guid": "f0aacd31-04dc-56ca-9c75-76b42e1d227d", "url": "https://pretalx.com/juliacon-2026/speaker/C83CVK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZPDSRG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZPDSRG/", "attachments": []}, {"guid": "7b340f42-ee3d-5cf0-99d1-ab855768dec3", "code": "7VLXGG", "id": 92687, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7VLXGG/image_annSsgW.webp", "date": "2026-08-13T17:00:00+02:00", "start": "17:00", "end": "2026-08-13T17:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92687-qcengine-jl-electronic-structure-for-nonadiabatic-dynamics", "url": "https://pretalx.com/juliacon-2026/talk/7VLXGG/", "title": "QCEngine.jl: Electronic Structure for Nonadiabatic Dynamics", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Short talk", "language": "en", "abstract": "I will present QCEngine.jl, an extension to the NQCDynamics.jl ecosystem that enables on-the-fly electronic structure theory calculations that couple to a library of efficient non-adiabatic dynamics methods. QCEngine.jl integrates with NQCModels.jl to enable the evaluation of molecular dynamics on electronic potential energy surfaces of interacting many-body Hamiltonians. I will highlight its use-case by presenting its applications to my research on spin-resolved surface scattering.", "description": "QCEngine.jl is coupled with NQCDynamics.jl to produce models of nonadiabatic energy dissipation during ultrafast molecular dynamics at surfaces. NQCDynamics.jl provides a flexible library of dynamics methods capable to modelling nonadiabatic effects during molecular dynamics in gas phase and at surfaces and in the both the strong and weak coupling regimes.\n\nTogether with QCEngine.jl these codes are able to capture the resultant electron-hole pair excitation and highly inelastic scattering driven by the strong hybridisation between hydrogen and a metallic surface and the resultant phase transition in the hydrogenic spin polarisation. This kind of electronic phase transition leads to strong nonadiabatic effects and divergent energy transfer rates in traditional mean-field dynamics methods like molecular dynamics with electronic friction (MDEF).\n\nNQCDynamics.jl enables us to go beyond these methods and couple orbital surface hopping methods with on-the-fly Hartree-Fock calculations of the spin-transition performed by QCEngine.jl. Using these methods we are able to calculate non-adiabatic energy losses and spin survival probabilites during H/Cu(111) scattering in the presence of on-site correlation in the hydrogenic s-orbital.\n\nGardner et al. J. Chem. Phys. (2022) [https://doi.org/10.1063/5.0089436](https://doi.org/10.1063/5.0089436)\nBox et al. J. Phys. Chem. Lett. (2024) [https://doi.org/10.1021/acs.jpclett.4c02468](https://doi.org/10.1021/acs.jpclett.4c02468)", "recording_license": "", "do_not_record": false, "persons": [{"code": "3RAACY", "name": "Ash Baldwin", "avatar": "https://pretalx.com/media/avatars/8KSGXJ_ayoKx5g.webp", "biography": "Ash is a PhD candidate in physics at the University of Vienna. She works in the Maurer group studying computational materials physics and ultra-fast dynamics at surfaces.", "public_name": "Ash Baldwin", "guid": "397185d6-1c51-51be-b45b-02018271ed49", "url": "https://pretalx.com/juliacon-2026/speaker/3RAACY/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7VLXGG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7VLXGG/", "attachments": []}, {"guid": "1bf54d2b-e05f-5423-a275-1c47e2c5ded9", "code": "ZKCBJY", "id": 93057, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZKCBJY/image_ScYakuu.webp", "date": "2026-08-13T17:15:00+02:00", "start": "17:15", "end": "2026-08-13T17:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93057-extending-dftk-jl-s-features-but-not-its-code-complexity", "url": "https://pretalx.com/juliacon-2026/talk/ZKCBJY/", "title": "Extending DFTK.jl's features, but not its code complexity", "subtitle": "", "track": "JuliaMolSim Minisymposium", "type": "Short talk", "language": "en", "abstract": "Plane-wave density-functional theory (DFT) is one of the most widely employed simulation approaches for modelling materials atomistically, taking an accurate quantum-mechanical description of electrons. Since 2019 we develop the Density-Functional ToolKit (DFTK, https://dftk.org), a Julia-based code for plane-wave DFT. Right now, with about 10k lines of code, the code remains tractable, despite we recently managed to considerably expand its features. Noteworthy recent extensions is the scaling to multiple GPUs as well as advanced and expensive electronic structure models, such as Hybrid DFT or DFT with Hubbard corrections. I will sketch the challenges with respect to keeping code concise despite the feature extension and why we believe this is the right direction in the age of differentiable scientific computing. Despite our goal to avoid hand-optimised code and custom kernels, our code has state-of-the-art performance, which I will illustrate with some recent benchmarks.\n\nThis talk reports on work that has been conducted over the past two years jointly with many DFTK contributors, including Augustin Bussy (ETH Z\u00fcrich), Bruno Ploumhans (EPFL), Antoine Levitt (Universit\u00e9 Paris-Saclay), Tobias Sch\u00e4fer (TU Vienna), Niklas Schmitz (EPFL), Francesco Sicignano (Scuola Normale Superiore, Pisa).", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "DZ7WHZ", "name": "Michael F. Herbst", "avatar": null, "biography": "I am a researcher working on the interdisciplinary edge of mathematics, quantum simulations, materials science and physics, leading the [Mathematics for Materials modelling](https://matmat.org) research group at EPFL. Together with my group we explore how mathematical understanding of algorithms and errors can help to make materials simulations faster and more reliable. For this purpose we develop the [Density-Functional ToolKit](https://dftk.org), a Julia-based code for density-functional theory (DFT), and contribute to the [JuliaMolSim](https://juliamolsim.org) ecosystem to advance the state of Julia-based materials modelling.", "public_name": "Michael F. Herbst", "guid": "a5d6cd44-55b2-5f59-8f0c-f968c51cf3c2", "url": "https://pretalx.com/juliacon-2026/speaker/DZ7WHZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZKCBJY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZKCBJY/", "attachments": [{"title": "flyer-ZKCBJY", "url": "/media/juliacon-2026/submissions/ZKCBJY/resources/ZKCBJY_mKqLAJc.png", "type": "related"}]}], "Muschel \u2014 N3": [{"guid": "e317ba34-1ca9-5d03-bd25-21db60bd33a5", "code": "PGGCJK", "id": 92714, "logo": "https://pretalx.com/media/juliacon-2026/submissions/PGGCJK/image_9tKtFm8.webp", "date": "2026-08-13T10:00:00+02:00", "start": "10:00", "end": "2026-08-13T10:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92714-from-stencils-to-xla-a-reactant-backend-for-parallelstencil-jl", "url": "https://pretalx.com/juliacon-2026/talk/PGGCJK/", "title": "From Stencils to XLA: A Reactant Backend for ParallelStencil.jl", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Long talk", "language": "en", "abstract": "We present an approach for building a Reactant backend for ParallelStencil, a Julia package for high-performance stencil computations. The approach includes the generation of kernel code and data structures that are pre-optimized to serve as optimal input for Reactant to generate efficient and correct GPU, TPU, and CPU code. We report performance of representative stencil mini-apps on recent hardware platforms, including NVIDIA H100 GPUs, evaluate it in absolute terms, and compare it with performance obtained with straightforward implementations using CUDA.jl, KernelAbstractions.jl, and other Julia packages that enable explicit GPU kernel programming.", "description": "Stencil computations are a fundamental class of algorithms in scientific computing, with applications ranging from computational fluid dynamics to image processing. ParallelStencil is a Julia package that enables the conversion of architecture-agnostic, high-level stencil or stencil-like code into high-performance GPU, TPU, and CPU code. To achieve this, two distinct approaches have been developed to build corresponding backends: the first relies on generating hardware-specific low-level code (using CUDA.jl, AMDGPU.jl, Metal.jl, Polyester.jl, or Base.Threads); the second relies on generating generic code that is optimized as input for Reactant, and delegating the hardware-specific code generation to Reactant. Reactant is a Julia package that enables the optimization of Julia functions with MLIR and XLA for high-performance execution on CPUs, GPUs, TPUs and other hardware architectures.\n\nThis contribution focuses on the second approach. We evaluate different approaches to integrate Reactant into ParallelStencil and describe solutions to challenges encountered in generating kernel code and data structures that are pre-optimized to serve as optimal input for Reactant to generate efficient and correct GPU, TPU, and CPU code. We evaluate the performance implications of different code patterns and data structures on the efficiency of the generated code.\n\nWe report the performance of representative stencil mini-apps with ParallelStencil and Reactant combined on recent hardware platforms, including NVIDIA H100 GPUs at the Swiss National Supercomputing Centre (CSCS). The mini-apps include a 3D heat diffusion solver and a 3D Navier-Stokes solver using a staggered grid. We evaluate performance in absolute terms using the effective memory throughput metric, and in relative terms by comparing it with performance obtained with straightforward implementations using CUDA.jl, KernelAbstractions.jl, and other Julia packages that enable explicit GPU kernel programming.\n\nThese results demonstrate the effectiveness of the approach used to build a backend with Reactant and provide insights into how to optimize code and data structures for efficient code generation with Reactant. Furthermore, this work shows how the powerful emerging MLIR- and XLA-based technologies can be integrated into existing domain-scientist-oriented high-performance computing frameworks such as ParallelStencil, providing a path toward broader adoption of these technologies in the HPC community.", "recording_license": "", "do_not_record": false, "persons": [{"code": "QTKXPY", "name": "Samuel Omlin", "avatar": "https://pretalx.com/media/avatars/QTKXPY_Th2goew.webp", "biography": "Computational Scientist and Responsible for Julia computing, at the Swiss National Supercomputing Centre (CSCS), ETH Zurich", "public_name": "Samuel Omlin", "guid": "195c4c1e-6421-5b98-9109-0e493bd51ced", "url": "https://pretalx.com/juliacon-2026/speaker/QTKXPY/"}, {"code": "3PHQR7", "name": "William Moses", "avatar": null, "biography": "William (Billy) Moses is an Assistant Professor at the University of Illinois in the Computer Science and Electrical and Computer Engineering departments. He received a Ph.D. in Computer Science from MIT, where he also received his M.Eng in electrical engineering and computer science (EECS) and B.S. in EECS and physics. William's research involves creating compilers and program representations that enable performance and use-case portability, thus enabling non-experts to leverage the latest in high-performance computing and ML. He is known as the lead developer of Enzyme, a tool for LLVM/MLIR capable of differentiating code in a variety of languages; Polygeist, a polyhedral compiler and C++ frontend for MLIR; and Reactant, a tool for enabling existing scientific code to run on distributed ML accelerators. He has also worked on the Tensor Comprehensions framework for synthesizing high-performance GPU kernels of ML code, the Tapir compiler for parallel programs, and compilers that use machine learning to better optimize. He is a recipient of the 2026 SIAM Supercomputing Early Career Prize, the 2024 SIGHPC Doctoral Dissertation Award, a DOE Computational Science Graduate Fellowship and the Karl Taylor Compton Prize, MIT's highest student award.", "public_name": "William Moses", "guid": "61825905-08d2-5378-b9a1-2c0c12765d55", "url": "https://pretalx.com/juliacon-2026/speaker/3PHQR7/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/PGGCJK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/PGGCJK/", "attachments": [{"title": "flyer-PGGCJK", "url": "/media/juliacon-2026/submissions/PGGCJK/resources/PGGCJK_67wmODu.png", "type": "related"}]}, {"guid": "2923b117-e856-5c4c-a073-15a4f44fc772", "code": "LHPDZM", "id": 93446, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LHPDZM/image_NLvZhiq.webp", "date": "2026-08-13T10:30:00+02:00", "start": "10:30", "end": "2026-08-13T10:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93446-what-s-new-in-chmy-jl-tensor-expressions-and-automatic-optimisation-of-finite-difference-codes", "url": "https://pretalx.com/juliacon-2026/talk/LHPDZM/", "title": "What's new in Chmy.jl: tensor expressions and automatic optimisation of finite-difference codes", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Short talk", "language": "en", "abstract": "[Chmy.jl](https://github.com/PTsolvers/Chmy.jl) is a Julia package for developing scalable, architecture-agnostic finite-difference codes. It provides modules for structured staggered grids, differential and interpolation operators, and boundary conditions. Chmy.jl v0.2 introduces functionality for expressing equations in a coordinate-independent tensor form and automatically generating kernels from lists of expressions, with kernel reordering and fusion to maximise memory throughput.", "description": "On modern hardware, most physics computations are memory-bound rather than compute-bound, making memory throughput the primary performance metric rather than the number of floating-point operations. While optimising for a specific architecture is challenging, we use effective memory throughput as a simple heuristic for assessing the performance of architecture-agnostic code.  At the core of Chmy.jl is a domain-specific language (DSL) for expressing operations on structured grids. This allows users to write finite-difference approximations to PDEs in a math-like notation, which is then lowered to efficient Julia code executable on CPUs, GPUs, and distributed clusters.\n\nTo maximise performance, we apply a constrained combinatorial optimisation algorithm that determines the ordering and grouping of Chmy.jl expressions into kernels that maximise effective memory throughput. The resulting kernels are generated using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl). With this approach, we achieve 80\u201395% of peak memory throughput for kernels implementing linear and nonlinear Poisson and Stokes equations, which are key building blocks in computational fluid mechanics.", "recording_license": "", "do_not_record": false, "persons": [{"code": "Z83NH3", "name": "Ivan Utkin", "avatar": null, "biography": "I'm an applied mathematician working in the field of computational glaciology. My interests include GPU computing, supercomputing, computational fluid dynamics, numerical analysis, to name a few.", "public_name": "Ivan Utkin", "guid": "13d1405e-4e1b-50fe-9c9a-d24fd538b020", "url": "https://pretalx.com/juliacon-2026/speaker/Z83NH3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LHPDZM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LHPDZM/", "attachments": []}, {"guid": "3b6ec587-a135-5049-ada6-68e9bba08597", "code": "Y7LGHP", "id": 92894, "logo": "https://pretalx.com/media/juliacon-2026/submissions/Y7LGHP/image_wGIaTTK.webp", "date": "2026-08-13T10:45:00+02:00", "start": "10:45", "end": "2026-08-13T11:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92894-trixiparticles-jl-on-gpus-a-deep-dive-into-simulating-fluid-dynamics-of-carbon-fiber-fins", "url": "https://pretalx.com/juliacon-2026/talk/Y7LGHP/", "title": "TrixiParticles.jl on GPUs: A Deep Dive into Simulating Fluid Dynamics of Carbon Fiber Fins", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Short talk", "language": "en", "abstract": "[TrixiParticles.jl](https://github.com/trixi-framework/TrixiParticles.jl) is an open-source framework for accessible particle-based multiphysics. In this talk, we demonstrate how to leverage modern, GPU-centric HPC hardware for complex fluid\u2013structure interaction (FSI) simulations. Using a carbon-fiber freediving fin as a case study, we discuss the numerical challenges of extremely stiff and thin blades, and present performance benchmarks across different CPU and GPU architectures to showcase cross-platform efficiency.", "description": "[TrixiParticles.jl](https://github.com/trixi-framework/TrixiParticles.jl) is an open-source numerical simulation framework designed for accessible particle-based multiphysics simulations and implemented in Julia as part of the Trixi Framework. Two years after [introducing the package at JuliaCon 2024](https://youtu.be/ReLkKUV4kTw?is=aEQmXo9GWj3d36dI), we return to demonstrate how the framework leverages modern HPC hardware to support both rapid prototyping and complex applications.\n\nIn the first part of the talk, we present the latest developments in GPU usability and performance.\nAs modern clusters become increasingly GPU-centric, simulation software must adapt to utilize this hardware effectively. We show how TrixiParticles.jl allows users to seamlessly run simulations on GPUs with minimal code changes, and present benchmarks comparing modern dual-socket CPU nodes against various GPU architectures.\n\nIn the second part, we take a \"deep dive\" into a challenging real-world application: the hydrodynamics of a carbon-fiber freediving fin. Simulating the propulsion generated by a foot-driven, extremely stiff, and thin blade presents significant numerical difficulties, particularly regarding Fluid-Structure Interaction (FSI). We will discuss numerical instabilities caused by the high stiffness and thin geometry of the carbon-fiber blade.", "recording_license": "", "do_not_record": false, "persons": [{"code": "QANXLQ", "name": "Erik Faulhaber", "avatar": "https://pretalx.com/media/avatars/E7WVLW_aFX1Pud.webp", "biography": "PhD Student in the Numerical Simulation Group at University of Cologne, Germany.", "public_name": "Erik Faulhaber", "guid": "d04f1294-f010-5c4f-9475-aa9b33fb3ee2", "url": "https://pretalx.com/juliacon-2026/speaker/QANXLQ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/Y7LGHP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/Y7LGHP/", "attachments": [{"title": "flyer-Y7LGHP", "url": "/media/juliacon-2026/submissions/Y7LGHP/resources/Y7LGHP_OEgnpTf.png", "type": "related"}]}, {"guid": "acb5e14c-fea4-5b1d-b1a8-54cc4fd2ff0c", "code": "99LHV3", "id": 92880, "logo": "https://pretalx.com/media/juliacon-2026/submissions/99LHV3/image_onKjUgH.webp", "date": "2026-08-13T11:15:00+02:00", "start": "11:15", "end": "2026-08-13T11:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92880-scalable-agent-based-modeling-understanding-and-addressing-partitioning-challenges", "url": "https://pretalx.com/juliacon-2026/talk/99LHV3/", "title": "Scalable Agent-Based Modeling: Understanding and Addressing Partitioning Challenges", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Short talk", "language": "en", "abstract": "Effective partitioning is important for the scalability of agent-based modeling (ABM) on HPC systems, but existing methods do not meet the specific challenges of complex ABMs. These usually involve heterogeneous agent types with phase-based execution, dynamic population changes, and moving agents. This talk presents ongoing research developing specialized partitioning algorithms for distributed ABMs, including a benchmark framework and the development of specialized Julia packages.", "description": "Agent-based models (ABMs) simulate complex systems through the interaction of individual agents. Scaling these simulations to millions of agents requires advanced parallel computing methods due to the highly flexible structure of ABMs and their interactions.\n\nCompared to static computational problems such as mesh-based finite element simulations, where the computational load and communication patterns remain the same throughout execution, ABMs present a special challenge for load balancing due to their dynamic nature and the complex interactions of the agents:\n\n- ABMs can involve heterogeneous agent types that act at different phases of the simulation. Even when the overall partitioning appears good, individual agent types may be severely imbalanced across processes, leading to idle computational resources during individual phases of the execution.\n\n- Communication between agents can also take place between agents who are spatially far apart.\n\n- Dynamic population changes alter the load distribution through addition and removal of agents.\n\n- Agent movement can create hotspots and ghost zones that were not present in the initial partitioning,\n\nSince standard partitioning algorithms do not take into account the dynamics and complex structure that are inherent in complex ABM scenarios, their results are not always satisfactory.\n\n\n**Talk structure**:\n\n1. **Brief Vahana.jl introduction**: A brief overview of Vahana.jl, an HPC ABM framework based on graph dynamical systems, with a focus on the new features implemented since version 1.0, as presented at JuliaCon 2023.\n\n2. **ABM-specific partitioning challenges**: Why general-purpose partitioning approaches fail for ABMs with heterogeneous agent types, dynamic populations, and complex agent behaviors/movement patterns\n\n3. **Synthetic benchmarking model**: Presentation of a purpose-built synthetic ABM that systematically captures the partitioning challenges specific to ABMs. This model provides a flexible environment for evaluating and comparing different load balancing strategies and different HPC ABM frameworks in general.\n\n4. **Outlook**: Overview of our ongoing early-stage work on developing novel partitioning algorithms specifically tailored to ABM characteristics. These algorithms will be released both as integrated Vahana.jl features and as independent Julia packages.", "recording_license": "", "do_not_record": true, "persons": [{"code": "DHWB8G", "name": "Steffen F\u00fcrst", "avatar": null, "biography": "Steffen F\u00fcrst, having studied mathematics with a focus on economics and social science, has spent the past 15 years working on various agent-based models. In the most recent 5 years, his focus has been particularly on high-performance computing environments.", "public_name": "Steffen F\u00fcrst", "guid": "cc822e4a-8c0d-5bdb-bff0-a0f1f8bd8588", "url": "https://pretalx.com/juliacon-2026/speaker/DHWB8G/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/99LHV3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/99LHV3/", "attachments": []}, {"guid": "4d6cbb78-c1b3-5b67-97a9-8194b35a0380", "code": "VFZ9X7", "id": 90798, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VFZ9X7/image_rUjWsOV.webp", "date": "2026-08-13T11:30:00+02:00", "start": "11:30", "end": "2026-08-13T11:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-90798-asynchronous-field-particle-coupling-for-multiphase-cloud-simulation-using-heterogeneous-hpc", "url": "https://pretalx.com/juliacon-2026/talk/VFZ9X7/", "title": "Asynchronous Field-Particle Coupling for Multiphase Cloud Simulation using Heterogeneous HPC", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Short talk", "language": "en", "abstract": "Efficient simulation of multiphase flows remains a major challenge, particularly for cloud microphysical processes in which interactions between turbulent airflow and suspended droplets must be resolved in detail. We present a novel asynchronous two-way coupled Euler\u2013Lagrange simulation framework that exploits heterogeneous computing architectures to achieve unprecedented scalability.\n\nThe proposed method executes Eulerian field calculations on CPUs using the OpenFOAM software package, coupled asynchronously to Lagrangian particle tracking on GPUs implemented in Julia, minimizing computational idling times and synchronization barriers. Data transfers are initiated immediately upon data availability, with Eulerian source terms predicted from previous time steps and subsequently corrected to ensure conservation of mass and momentum. Particles are organized into cache-friendly chunks with maintained bounding boxes, enabling dynamic load balancing across GPUs and optimized CPU-GPU data transfers.\n\nComprehensive testing on a local workstation and on a EuroHPC JU supercomputer revealed dramatic improvements: the algorithm achieves excellent scalability up to 256 billion droplets. The overall time-to-solution improved by a factor of 4.5, while energy efficiency improved 3.4 times compared to established methods. Weak and strong scaling tests demonstrated very good efficiency and speedup using up to 2500 cores paired with 256 GPUs.\n\nThe software is available in a public GIT repository at https://github.com/Wikki-GmbH/SCALE-TRACK", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "7M8QLH", "name": "Henrik Rusche", "avatar": null, "biography": "Rusche, Henrik\nComputational Fluid Dynamics of Dispersed Two-phase Flows at High Phase Fractions.\nPhD thesis, Imperial College London, 2002. A foundational work on multiphase flow modeling that underpins much of his later OpenFOAM development contributions.\n\nShanmugasundaram, R. k., Rusche, Henrik, Windt, C., Kirca, \u00d6., Sumer, B. M., & Goseberg, N.\nTowards the Numerical Modelling of Residual Seabed Liquefaction Using OpenFOAM.\nOpenFOAM\u00ae Journal, Vol. 2:16, 2022. Develops a finite-volume OpenFOAM solver for seabed liquefaction analysis.\n\nRanjith Khumar Shanmugasundaram, Henrik Rusche, Christian Windt, V. S. \u00d6zg\u00fcr Kirca, B. Mutlu Sumer, & Nils Goseberg.\nNumerical Modeling of Wave-Induced Seabed Liquefaction: A Drift-Flux Model for Liquefied Soil.\nJournal of Waterway, Port, Coastal and Ocean Engineering, Vol. 151, No. 6, 2025. Advanced numerical model for seabed soil liquefaction simulation.\n\nRusche, Henrik, Jasak, H., Popovac, M.\nImplementation and Numerical Stabilisation of Adjoint Flow and Turbulence Model in OpenFOAM.\nProceedings of the European Conference on Computational Fluid Dynamics (ECFD VI), 2014. A notable contribution to turbulence modeling and adjoint flow methods in OpenFOAM.\n\nRusche, Henrik\nRecent Developments in OpenFOAM.\nProceedings of the III International Conference \u201cCloud computing. Education. Research. Development.\u201d, Moscow, 2013. A summary of key advancements in the OpenFOAM framework to that date.", "public_name": "Henrik Rusche", "guid": "3e209786-4ca3-54ec-87af-adb443203cac", "url": "https://pretalx.com/juliacon-2026/speaker/7M8QLH/"}], "links": [{"title": "public GIT repository", "url": "https://github.com/Wikki-GmbH/SCALE-TRACK", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VFZ9X7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VFZ9X7/", "attachments": [{"title": "flyer-VFZ9X7", "url": "/media/juliacon-2026/submissions/VFZ9X7/resources/VFZ9X7_fB2FUEp.png", "type": "related"}, {"title": "Schematic execution timeline for one GPU driven by a host CPU and a slave CPU. Asynchronous overlap of Eulerian and Lagrangian calculations is clearly visible.", "url": "/media/juliacon-2026/submissions/VFZ9X7/resources/Figure_2-1-5_ZO8BpL0.png", "type": "related"}, {"title": "Snapshot of droplets in the 81m\u00b3 cloud chamber. Two side walls and the top are removed for illustration purposes and only a selection of droplets is shown. The size of the droplets is scaled up proportional to their diameter, but with an additional scaling factor to make them visible.", "url": "/media/juliacon-2026/submissions/VFZ9X7/resources/Figure_4_FjM0hrN.png", "type": "related"}]}, {"guid": "7bd82f38-ed99-5f20-863f-edb2b65fc441", "code": "SHEA7J", "id": 92914, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SHEA7J/image_Azu0a7x.webp", "date": "2026-08-13T11:45:00+02:00", "start": "11:45", "end": "2026-08-13T12:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92914-petsc-jl", "url": "https://pretalx.com/juliacon-2026/talk/SHEA7J/", "title": "PETSc.jl", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Short talk", "language": "en", "abstract": "[PETSc](https://petsc.org/release/) is a widely used scientific computing library that allows you to write software that runs on massively parallel supercomputers.  PETSc is written in C but provides interfaces for Fortran and Python. Its Julia interface,  \n[PETSc.jl ](https://github.com/JuliaParallel/PETSc.jl), has been around for some time but only ported a small part of the library. Recently, this has changed and since version 0.4.0 it provides an interface to (nearly) the full PETSc library.\n\nThere are a number of advantages compared to other attempts:\n\n- It can be very easily installed by typing \"add PETSc\" in the Julia package manager and is distributed with both precompiled PETSc binaries ` PETSc_jll ` (for linux and Mac) and MPI.\n\n- We provide both a high-level and a low-level interface. The low-level interface automatically wraps nearly the full PETSc library with over 3000 functions, whereas the high-level interface gives a more Julia-like experience but is currently limited to part of the library.\n\n- Automatic testing and CI/CD is performed with currently >50\u2019000 tests.\n\n- Users can combine features from Julia, the Julia ecosystem, such as automatic differentiation and plotting with PETSc solvers. The resulting codes are much more compact than their counterparts in lower-level languages; yet, users still have access to all PETSc features, such as multigrid solvers for DMDA or DMStag grids.\n\n- It allows running code on both a local workstation and on a large HPC system.\n\nIn the presentation, I will summarise some of the work done to achieve this and show scalability results of typical codes. I will also compare the timing with native compiled code.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "3ATAEN", "name": "Boris Kaus", "avatar": "https://pretalx.com/media/avatars/NHMXEV_6khG9rS.webp", "biography": "Professor of Geodynamics and Geophysics at the Johannes Gutenberg University Mainz (Germany). Interested in using computational models to understand geoscientific processes such as the formation of fault zones, mountain belts, magmatic processes, volcanic eruptions as well as using computational models to estimate the long-term stability of geological reservoirs.", "public_name": "Boris Kaus", "guid": "aa6e6b37-a5b0-5255-bc5e-3b332e15d506", "url": "https://pretalx.com/juliacon-2026/speaker/3ATAEN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SHEA7J/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SHEA7J/", "attachments": []}, {"guid": "a94882b2-1bf3-5238-9118-406af99e417d", "code": "BLDX9J", "id": 93478, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BLDX9J/image_ysARU79.webp", "date": "2026-08-13T12:00:00+02:00", "start": "12:00", "end": "2026-08-13T12:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93478-spry-jl-native-high-performance-networking-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/BLDX9J/", "title": "Spry.jl: Native High Performance Networking in Julia", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Long talk", "language": "en", "abstract": "We present Spry.jl, a package for high performance networking natively in Julia. Traditional HPC has long used abstractions like MPI or SHMEM to manage communication. Spry.jl directly interfaces with the low level Libfabrics and UCX libraries enabling fast, scalable networking that complements Julia's dynamism, performance, and workflow flexibility rather than limiting it. We also provide an overview of the high level interfaces of Spry like distributed arrays, channels, and object stores.", "description": "Spry.jl frees Julia HPC codes from the shackles of MPI, SHMEM, and other HPC abstractions built for a world where HPC used C and Fortran. These older codes enable scaling and portability but are increasingly limiters on performance. This is particularly true in Julia where our task based parallelism conflicts with MPI's strict serialization semantics and heavy interface.\n\nSpry.jl instead directly targets the networking libraries that MPI, SHMEM, and more rely on. Spry.jl is a metapackage which combines several different subpackages for different capabilities:\n\n1. Bootstraps.jl manages processes, sets up and tears down network topologies, manages events between processes, and provides a key-value store for important metadata.\n2. Fabrics.jl and UCX.jl are low-level networking libraries that expose user-friendly but direct access to the OpenFabricsInterface and UCX. These libraries provide extremely lightweight abstractions over vendor specific network hardware like HPE Slingshot, Mellanox Infiniband, AWS EFA, Infiniband Verbs, Omnipath, TCP, and more.\n3. Swarms.jl enables (overlapping) groups of processes, the equivalent of communicators in MPI. Built on top of Bootstraps.jl\n4. Specific subpackages for the high level interfaces: GlobalArrays.jl, RemoteChannels.jl, GlobalObjects.jl, and more...\n\nWe also provide a strong benchmarking story, with comparisons against traditional libraries like MPI and SHMEM, Charm as well as newer libraries and languages like Chapel, LCI, HPX, etc.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TMKBTN", "name": "Raye Kimmerer", "avatar": "https://pretalx.com/media/avatars/TMKBTN_zLnjJpI.webp", "biography": "Unga Bunga", "public_name": "Raye Kimmerer", "guid": "0fc60143-2b46-5865-abe6-2c46fcd20ddc", "url": "https://pretalx.com/juliacon-2026/speaker/TMKBTN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BLDX9J/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BLDX9J/", "attachments": []}, {"guid": "dfd029a1-fbcf-5eb1-a8aa-dd07d9c61277", "code": "AUBUSN", "id": 92943, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AUBUSN/image_KGjbqSS.webp", "date": "2026-08-13T12:30:00+02:00", "start": "12:30", "end": "2026-08-13T12:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92943-bridging-the-gap-between-dagger-jl-and-hpc-interconnects", "url": "https://pretalx.com/juliacon-2026/talk/AUBUSN/", "title": "Bridging the Gap between Dagger.jl and HPC Interconnects", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Short talk", "language": "en", "abstract": "While Julia\u2019s `Dagger.jl` provides a productive framework for task-based parallelism using Directed Acyclic Graphs (DAGs), its default reliance on TCP-based `Distributed.jl` limits performance on low-latency HPC interconnects. To bridge this gap, we developed `MPIAcceleration`, a strategic extension that replaces standard transport with an MPI-aware backend. By leveraging `MPI.jl` and non-blocking communication, we enable Dagger to use specialized hardware such as InfiniBand and Slingshot while maintaining a simple, high-level API.", "description": "Dagger's MPIAcceleration works seamlessly with the current scheduler, allowing task graphs to be executed across MPI ranks with minimal modifications. It only requires a single line of code: `Dagger.accelerate!(:mpi)`.\n\nWhen this feature is enabled, each MPI rank is integrated into Dagger's Processor/Memory Space model, which ensures that tasks are executed close to where their data resides. This rank-aware placement helps to minimize communication overhead. Additionally, remote data transfers happen transparently, providing handles on the appropriate ranks.", "recording_license": "", "do_not_record": false, "persons": [{"code": "T7JPBN", "name": "Yan Guimar\u00e3es", "avatar": "https://pretalx.com/media/avatars/XUNYHW_b8ETgbc.webp", "biography": "Yan Guimar\u00e3es is a Software Engineering student at the University of Bras\u00edlia (UnB) and a contributor to Dagger.jl whose work focuses on high-performance computing and distributed task scheduling. As a Google Summer of Code 2025 contributor with MIT\u2019s JuliaLab, he designed and implemented an MPI-based backend for Dagger\u2019s DAG scheduler, improving its ability to run efficiently on HPC systems. He evaluated this work on the Aurora exascale supercomputer at Argonne National Laboratory, demonstrating the performance benefits of MPI-based communication for distributed linear algebra workloads. Yan is the first author of \u201c[Productive Scalable Distributed Task Scheduling Using an MPI-based Backend](https://sc25.supercomputing.org/proceedings/posters/poster_files/post102s2-file3.pdf),\u201d which he presented at the ACM Student Research Competition at SC25. His broader research interests include scalable runtime systems, distributed computing, JIT compilation, and MLIR-based compiler optimizations for high-performance computing.", "public_name": "Yan Guimar\u00e3es", "guid": "7a27422d-dbe6-587b-a09a-23df9f23ab77", "url": "https://pretalx.com/juliacon-2026/speaker/T7JPBN/"}, {"code": "VXYFQN", "name": "Felipe Tom\u00e9", "avatar": "https://pretalx.com/media/avatars/JLJBPE_ZnCs9zO.webp", "biography": "Consultant at MIT's JuliaLab, Co-maintainer of Dagger. My interests span from more broad topics such as the accessibility and educational initiatives for parallel computing to Applied Physics and Numerical Linear Algebra.", "public_name": "Felipe Tom\u00e9", "guid": "1cd1cbc0-bbe9-50b6-8986-e160a69196aa", "url": "https://pretalx.com/juliacon-2026/speaker/VXYFQN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AUBUSN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AUBUSN/", "attachments": []}, {"guid": "7ac57a69-0d1d-5589-a950-270907f7e1fe", "code": "YGSNMR", "id": 93151, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YGSNMR/image_FD9bhid.webp", "date": "2026-08-13T12:45:00+02:00", "start": "12:45", "end": "2026-08-13T13:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93151-sketch-me-an-hpc-program-stencils-with-dagger-jl", "url": "https://pretalx.com/juliacon-2026/talk/YGSNMR/", "title": "Sketch me an HPC program: Stencils with Dagger.jl", "subtitle": "", "track": "Julia for HPC Minisymposium", "type": "Short talk", "language": "en", "abstract": "Stencil operations are a cornerstone in many fields, including fluid and gas flow simulations, machine learning/AI, computer graphics, image processing, and many more. Stencil operations (also known as windowed operations) allow a normal elementwise operation to additionally access neighboring elements, instead of just the currently-selected element. The are a number of stencil computation libraries in Julia, such as ImageFiltering.jl, ParallelStencil.jl, Stencils.jl, and now Dagger.jl (the focus of this talk). Dagger in particular makes it easy to define stencil operations that run across multiple CPUs, multiple GPUs, and across multiple nodes, and supports many kinds of boundary conditions, arbitrary numbers of dimensions, and flexible neighborhood sizing. We will discuss and compare the differences between the various stencil libraries, and see how easy it is to write parallel stencils in each library. We will also look at Dagger\u2019s stencil performance in a variety of microbenchmarks.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "GRFD9D", "name": "Julian P Samaroo", "avatar": "https://pretalx.com/media/avatars/GRFD9D_X04BPZD.webp", "biography": "Julian is a Research Software Engineer at MIT's JuliaLab, where he focuses on improving Julia's support for HPC and GPU computing. Julian has previously authored and maintained the AMDGPU.jl package (for programming AMD's GPUs from Julia), and now focuses his efforts on maintaining and developing the Dagger.jl package, to improve the state of productive parallel programming.", "public_name": "Julian P Samaroo", "guid": "545e5d52-47fb-56ff-99ce-9ee52d0dc560", "url": "https://pretalx.com/juliacon-2026/speaker/GRFD9D/"}, {"code": "VXYFQN", "name": "Felipe Tom\u00e9", "avatar": "https://pretalx.com/media/avatars/JLJBPE_ZnCs9zO.webp", "biography": "Consultant at MIT's JuliaLab, Co-maintainer of Dagger. My interests span from more broad topics such as the accessibility and educational initiatives for parallel computing to Applied Physics and Numerical Linear Algebra.", "public_name": "Felipe Tom\u00e9", "guid": "1cd1cbc0-bbe9-50b6-8986-e160a69196aa", "url": "https://pretalx.com/juliacon-2026/speaker/VXYFQN/"}, {"code": "KXJRE9", "name": "Rabab Alomairy", "avatar": null, "biography": "Rabab Alomairy is a postdoc in the Julia Lab located in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). Rabab's research is centered around task-based numerical libraries and applications, performance optimizations for multicore/manycore architectures and hardware accelerators, dynamic runtime systems, GPU programming, and machine learning and artificial intelligence.", "public_name": "Rabab Alomairy", "guid": "dac358f5-7836-5c2a-bed5-4f8290231c31", "url": "https://pretalx.com/juliacon-2026/speaker/KXJRE9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YGSNMR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YGSNMR/", "attachments": []}, {"guid": "c4f37515-8001-5784-9c93-37dfeb177d53", "code": "VC7Q39", "id": 92800, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VC7Q39/image_4Ooz3Cq.webp", "date": "2026-08-13T14:30:00+02:00", "start": "14:30", "end": "2026-08-13T14:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92800-multi-gpu-algorithms-with-dagger-jl", "url": "https://pretalx.com/juliacon-2026/talk/VC7Q39/", "title": "Multi-GPU Algorithms with Dagger.jl", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "Multi-GPU execution is the future - as data sizes grow, and as more work is pushed to the GPU, a single GPU no longer suffices. Unfortunately, programming an algorithm for multi-GPU is more complicated than single-GPU - you now have to deal with the complexity of multi-device data movement and multi-stream synchronization, which puts more burden on the algorithm author and takes away from just writing the algorithm in the simplest, most readable manner. Thankfully, Dagger.jl makes programming multi-GPU algorithms much easier with its Datadeps framework, which lets you focus on writing the algorithm at a high level while Dagger handles the details of managing multiple GPUs.\n\nThis talk will explain the problems around multi-GPU programming, and show how Dagger handles them. We will show how the Datadeps framework makes it much easier to write algorithms which naturally support multi-GPU execution, and show the tools that Dagger and Datadeps provide to make algorithm design a breeze.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "GRFD9D", "name": "Julian P Samaroo", "avatar": "https://pretalx.com/media/avatars/GRFD9D_X04BPZD.webp", "biography": "Julian is a Research Software Engineer at MIT's JuliaLab, where he focuses on improving Julia's support for HPC and GPU computing. Julian has previously authored and maintained the AMDGPU.jl package (for programming AMD's GPUs from Julia), and now focuses his efforts on maintaining and developing the Dagger.jl package, to improve the state of productive parallel programming.", "public_name": "Julian P Samaroo", "guid": "545e5d52-47fb-56ff-99ce-9ee52d0dc560", "url": "https://pretalx.com/juliacon-2026/speaker/GRFD9D/"}, {"code": "VXYFQN", "name": "Felipe Tom\u00e9", "avatar": "https://pretalx.com/media/avatars/JLJBPE_ZnCs9zO.webp", "biography": "Consultant at MIT's JuliaLab, Co-maintainer of Dagger. My interests span from more broad topics such as the accessibility and educational initiatives for parallel computing to Applied Physics and Numerical Linear Algebra.", "public_name": "Felipe Tom\u00e9", "guid": "1cd1cbc0-bbe9-50b6-8986-e160a69196aa", "url": "https://pretalx.com/juliacon-2026/speaker/VXYFQN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VC7Q39/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VC7Q39/", "attachments": []}, {"guid": "1042e566-41fd-5d5c-bd1f-260c98e616f1", "code": "WGCTSX", "id": 92455, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WGCTSX/image_BFZFAyZ.webp", "date": "2026-08-13T14:45:00+02:00", "start": "14:45", "end": "2026-08-13T15:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92455-hardware-agnostic-linear-programming-on-the-gpu", "url": "https://pretalx.com/juliacon-2026/talk/WGCTSX/", "title": "Hardware-agnostic linear programming on the GPU", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "This talk introduces a new package called CoolPDLP.jl, which implements state-of-the-art parallel algorithms for large-scale linear programming. Thanks to Julia's flexible GPU ecosystem, these algorithms run on various kinds of accelerators, accepting arbitrary matrix and number types.", "description": "A linear program (LP) is an optimization problem whose objective and constraints are affine functions of the decision variables. While the simplex method is a very efficient way to solve LPs on the CPU, it is hard to parallelize efficiently, limiting its scalability. In recent years, first-order methods for LP have emerged, which require only matrix-vector multiplication. These primal-dual (PD) approaches allow practitioners to fully leverage advances in modern hardware accelerators such as GPUs and TPUs when tackling large-scale LPs. Their potential was first demonstrated by the PDLP algorithm [1] and its subsequent GPU translation [cuPDLP.jl](https://github.com/jinwen-yang/cuPDLP.jl) [2]. However, with the notable exception of [MPAX](https://github.com/MIT-Lu-Lab/MPAX) [3], existing implementations are usually written using CUDA, and thus limited to NVIDIA hardware. Furthermore, they seldom support batching, and do not allow custom matrix or number types.\n\nThanks to the flexibility of the Julia ecosystem, we developed a new package called [CoolPDLP.jl](https://github.com/JuliaDecisionFocusedLearning/CoolPDLP.jl), which implements the PDLP algorithm in a backend-agnostic fashion. It supports arbitrary matrix and number types, which enables experiments with handrolled sparse matrix formats or reduced precision. It also provides default cross-platform sparse matrices built atop [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl), for backends where sparse linear algebra is not sufficiently developed. Special care is given to minimizing allocations and preserving type stability. A [JuMP.jl](https://github.com/jump-dev/JuMP.jl) interface is also included.\n\nThe JuliaCon talk will introduce the main features of our package, present a few benchmarks on different hardware families, and conclude with future perspectives such as batched solving.\n\n---\n\n[1] D. Applegate et al., \u201cPDLP: A Practical First-Order Method for Large-Scale Linear Programming,\u201d Jan. 13, 2025, arXiv: arXiv:2501.07018. doi: 10.48550/arXiv.2501.07018.\n[2] H. Lu and J. Yang, \u201ccuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia,\u201d Operations Research, vol. 73, no. 6, pp. 3440\u20133452, Nov. 2025, doi: 10.1287/opre.2024.1069.\n[3] H. Lu, Z. Peng, and J. Yang, \u201cMPAX: Mathematical Programming in JAX,\u201d Dec. 12, 2024, arXiv: arXiv:2412.09734. doi: 10.48550/arXiv.2412.09734.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HWJDBJ", "name": "Guillaume Dalle", "avatar": "https://pretalx.com/media/avatars/PJVBLC_t336z2c.webp", "biography": "Researcher at \u00c9cole des Ponts (France) in the transportation department. Interested in operations research, graph algorithms, automatic differentiation and high-performance computing.\nWebsite: <https://gdalle.github.io/>", "public_name": "Guillaume Dalle", "guid": "b9e8303c-91d5-5bad-b51b-95a5e39edc78", "url": "https://pretalx.com/juliacon-2026/speaker/HWJDBJ/"}, {"code": "Z9SEXA", "name": "Michael Klamkin", "avatar": "https://pretalx.com/media/avatars/TC8F9Q_4c36EPT.webp", "biography": "ML PhD @ AI4OPT / Georgia Tech", "public_name": "Michael Klamkin", "guid": "97413fed-b173-5aca-8563-dff3b9c47d23", "url": "https://pretalx.com/juliacon-2026/speaker/Z9SEXA/"}, {"code": "MUW7BP", "name": "Simeon Schaub", "avatar": "https://pretalx.com/media/avatars/LWE98T_R3EkBeu.webp", "biography": "PhD student at ENPC (Paris, France)\n\nInterested in computational mathematics, programming language design, automatic differentiation and compilers.\n\nGitHub: https://github.com/simeonschaub", "public_name": "Simeon Schaub", "guid": "e817590a-5090-58c1-932c-014496660a09", "url": "https://pretalx.com/juliacon-2026/speaker/MUW7BP/"}], "links": [{"title": "Package repository", "url": "https://github.com/JuliaDecisionFocusedLearning/CoolPDLP.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WGCTSX/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WGCTSX/", "attachments": []}, {"guid": "eab6dda0-e20d-5ac1-b741-3521ba30a956", "code": "UFPKBT", "id": 93407, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UFPKBT/image_Sfw4WWt.webp", "date": "2026-08-13T15:00:00+02:00", "start": "15:00", "end": "2026-08-13T15:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93407-julia-meets-again-the-fpga-higher-level-synthesis-methodology-for-heterogeneous-hardware-and-software-architectures", "url": "https://pretalx.com/juliacon-2026/talk/UFPKBT/", "title": "Julia meets (again) the FPGA : Higher-level synthesis methodology for heterogeneous hardware and software architectures", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Long talk", "language": "en", "abstract": "The modular compiler of Julia allows non-standard compilation flows and heteregeneous targets. Field Programmable Gate Array (FPGAs) are one of them, and Julia is an excellent fit for High-Level Synthesis (HLS). We present an HLS toolchain that takes advantage of the MLIR tracing system Reactant.jl and CIRCT HLS flow. This approach enables flexible design-space exploration and rapid prototyping of FPGA designs.", "description": "Julia has achieved excellent results in non-standard compilation flows, notably because of the modularity of its toolchain. Flexible middle-end and back-end enables, for instance, automatic differenciation with Enzyme.jl or heteregeneous target with GPUCompiler.jl such as GPUs. Field Programmable Gate Arrays (FPGAs) are another interesting accelerator, as they are used in high-throughput and energy efficient contexts.\n\nConfigurate FPGAs usually involves using a Hardware Description Language (HDL), but they require solid hardware knowledge. High Level Synthesis (HLS) has therefor been introduced to fill the gap between software paradigms and HDLs. Several HLS approaches have been developed in the Julia ecosystem [1,2,3].\nState-of-the-art HLS tools, such as Vitis HLS, used a Domain-Specific Language (DSL) based on C++; it is handle by a custom Clang front-end to produce an LLVMIR which is used by the HLS engine. This IR is not ideal because of it low level nature: Multi-Layer IR (MLIR) [4] have been used instead.\n\nThere are two classes of MLIR front-ends:\n- static ones, which translate JuliaIR to an MLIR representation, they work by matching semantics (type system, 1/0-indexing) between JuliaIR and MLIR dialects. Notably, only a subset of Julia programs can be handled, for instance, type stability is required. Based on Brutus.jl, both [2] and [3] develop a custom MLIR front-end for HLS purpose, they differ in the choosen HLS engine. [2] uses ScaleHLS [5] which is a framework build on top of VitisHLS. [3] uses a MLIR based HLS engine proposed by CIRCT [6].\n\n- dynamic ones, the idea is to generate a MLIR program from an execution trace. This method targets higher-level dialect which handle tensor semantics. Reactant.jl [7] is an example of such a tracing approach. Unlike static methods, contraints on the Julia program are less strict, and this flexibily fit nicely to the Julia ecosystem.\n\nIn a similar approach to Hardware.jl, we propose an HLS toolchain based on CIRCT HLS that takes advantage of the Reactant.jl tracing system. We use the flexibily of the Julia compiler to create mechanisms that enable high-level design-space exploration for HLS. In particular, this presentation focuses on numerical applications and their use by hardware accelerators, in particular, showing the interest of Julia for rapid prototyping.\n\n\n\n[1] B. Biggs, I. McInerney, E. C. Kerrigan, and G. A. Constantinides, High-level Synthesis using the Julia\nLanguage, Feb. 2022. arXiv: 2201.11522 [cs].\n\n[2] G. Lounes, R. Gerzaguet, M. Gautier, Flexible front-end for high-level synthesis leveraging heterogeneous compilation, Jan. 2025.\n\n[3] B. Short, I. McInerney, J. Wickerson, A High-level Synthesis Toolchain for the Julia Language, Dec. 2025. arXiv: 2512.15679 [cs]\n\n[4] C. Lattner, M. Amini, U. Bondhugula, et al., MLIR: A Compiler Infrastructure for the End of Moore\u2019s\nLaw, Feb. 2020. arXiv: 2002.11054 [cs].\n\n[5] H. Ye, C. Hao, J. Cheng, et al., ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level\nIntermediate Representation, Dec. 2021. arXiv: 2107.11673 [cs].\n\n[6] https://github.com/llvm/circt\n\n[7] https://github.com/EnzymeAD/Reactant.jl", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZQHNFX", "name": "Ga\u00ebtan LOUNES", "avatar": "https://pretalx.com/media/avatars/ZMNTFE_kovWI3D.webp", "biography": "3rd year+ PhD student at IRISA (Institute for Research in Computer Science and Random Systems) interested in compilers, MLIR and FPGA.", "public_name": "Ga\u00ebtan LOUNES", "guid": "621899b4-50fa-56d0-a560-f9692e6d86cc", "url": "https://pretalx.com/juliacon-2026/speaker/ZQHNFX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UFPKBT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UFPKBT/", "attachments": []}, {"guid": "ccfad660-b1c0-5369-90ef-e72b7fa269f4", "code": "TATRTG", "id": 93411, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TATRTG/image_ulWeWXl.webp", "date": "2026-08-13T15:30:00+02:00", "start": "15:30", "end": "2026-08-13T16:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93411-gpu-acceleration-in-the-quantumkithub-ecosystem", "url": "https://pretalx.com/juliacon-2026/talk/TATRTG/", "title": "GPU acceleration in the QuantumKitHub ecosystem", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Long talk", "language": "en", "abstract": "QuantumKitHub's various packages provide low- and high-level tooling for the implementation of (among other things) tensor network algorithms. These algorithms are highly amenable to GPU-based acceleration, but there are many stumbling blocks along the way. In the past year we have been actively working to add GPU support to the whole stack of TN-related packages, and in this talk we will discuss the performance benefits and challenges thus far, our roadmap, and how this work can benefit the wider JuliaGPU developer and user community.", "description": "The tensor network algorithms we want to accelerate often involve large (100s of GB or more) objects on which we need to perform batched matmul, factorizations such as (randomized) SVD or QR, and permutations. Depending on the physical system, we may also be working with tensors that are extremely block-sparse, but with many irregularly sized blocks. These factors cause us to need robust and efficient multi-GPU primitives, and the various computing centers we work with generally support either NVIDIA or AMD hardware, but not both. For these reasons, we have been developing a cross-platform set of extensions to the existing packages which leverage the existing JuliaGPU implementations where possible, but because of the various and sometimes strange use-cases we are able to generate, also involve hand-written solutions.", "recording_license": "", "do_not_record": false, "persons": [{"code": "YYDPCZ", "name": "Katharine Hyatt", "avatar": null, "biography": "I am a Julia contributor since 2015. I work mostly on GPUs, quantum packages, and linear algebra.", "public_name": "Katharine Hyatt", "guid": "729bd10c-f6f5-53bf-b7b6-fff8ee7a09ae", "url": "https://pretalx.com/juliacon-2026/speaker/YYDPCZ/"}, {"code": "DP3BCC", "name": "Lukas Devos", "avatar": null, "biography": "Software Research Fellow at the Flatiron Institute, CCQ, studying tensor network methods and algorithms for classical and quantum physics simulations.", "public_name": "Lukas Devos", "guid": "118e4bd4-be2d-5977-ba8c-2662d450fb33", "url": "https://pretalx.com/juliacon-2026/speaker/DP3BCC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TATRTG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TATRTG/", "attachments": []}, {"guid": "7c5fd208-746b-5f29-8089-5e70fe38a40f", "code": "EFQ8YD", "id": 90359, "logo": "https://pretalx.com/media/juliacon-2026/submissions/EFQ8YD/image_zM99p2S.webp", "date": "2026-08-13T16:00:00+02:00", "start": "16:00", "end": "2026-08-13T16:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-90359-kernelforge-jl-fast-flexible-gpu-computing-toward-portability", "url": "https://pretalx.com/juliacon-2026/talk/EFQ8YD/", "title": "KernelForge.jl: Fast, Flexible GPU Computing Toward Portability", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "GPU vendor libraries like cuBLAS deliver excellent performance but come with hard constraints: limited type support, fixed operators, and single-vendor hardware. The Julia GPU ecosystem addresses portability through an abstraction layer: KernelAbstractions.jl lets developers write kernels that compile across CUDA, AMD, Intel, and Apple backends. But abstraction currently comes at a cost: KA.jl lacks the intrinsics needed for fully optimized performance. Warp operations on extended types, vectorized memory access, and explicit memory ordering for inter-workgroup communication are missing. We introduce [KernelForge.jl](https://epilliat.github.io/KernelForge.jl), a Julia package proving that portable GPU code can match vendor-optimized performance. To make this possible, we developed [KernelIntrinsics.jl](https://epilliat.github.io/KernelIntrinsics.jl), which exposes the missing primitives (currently CUDA-only, though the approach extends to other backends). KernelForge.jl provides kernels for matrix-vector and vector-matrix products with arbitrary operators and bitstype elements, mapreduce over 1D and 2D arrays, prefix scan, and copy operations. Each is implemented as a single kernel using vectorized loads/stores to saturate memory bandwidth as much as possible, warp-level reductions, and strong memory ordering for correct inter-workgroup synchronization. Benchmarks show that KernelForge.jl matches or exceeds both proprietary CUDA functions and NVIDIA's CUB library. The kernels are stable and tested, though views and strided arrays are not yet supported. The goal is straightforward: open-source GPU code that is efficient, flexible, and eventually portable.", "description": "This talk presents [KernelForge.jl](https://epilliat.github.io/KernelForge.jl), a Julia package for high-performance GPU computing. We first survey the current Julia GPU landscape\u2014backend packages and abstraction layers\u2014and identify what's missing for peak performance. We then introduce the intrinsics we developed in [KernelIntrinsics.jl](https://epilliat.github.io/KernelIntrinsics.jl) to fill these gaps. Finally, we show how KernelForge.jl uses these primitives to match or exceed vendor-optimized libraries like cuBLAS and CUB.\n\n## I The Current Julia GPU Landscape\n\nThe Julia GPU ecosystem is organized into two complementary layers: backend packages (CUDA.jl, AMDGPU.jl, oneAPI.jl) and abstraction packages (GPUArrays.jl, KernelAbstractions.jl, AcceleratedKernels.jl) that enable cross-architecture portability.\n\nOn the backend side, CUDA.jl relies on `libcuda` for memory copies and cuBLAS for vector dot products or matrix multiplications\u2014these are highly efficient. However, cuBLAS is proprietary, supports only a restricted set of types and operators, and is available only for NVIDIA GPUs.\n\nThis raises a central question: is it possible to write open-source functions that are efficient, flexible, and portable?\n\n### Abstraction Side\n\nKernelAbstractions.jl (KA.jl) provides tools to write kernels that can be compiled for multiple backends. This works through method overriding: each backend implements its own version of core methods (e.g., `_synchronize`) using the `@device_override` macro. At compile time, KA.jl specializes the kernel based on the backend context and argument types, following standard Julia dispatch. The code is then converted into an LLVM intermediate representation before being compiled into low-level asm (PTX for CUDA).\n\nGPUArrays.jl uses KA.jl notably for vector copy operations, which KA.jl makes straightforward to implement. AcceleratedKernels.jl provides reduction, scan, and sort functions built on KA.jl, achieving reasonable performance.\n\nOur goal with KernelForge.jl is to outperform current cross-architecture libraries (AcceleratedKernels, but also Kokkos and Raja in C++), as well as CUDA.jl and native proprietary CUDA libraries, and to demonstrate that it is possible to develop open-source code that is efficient, flexible, and portable at the same time.\n\n## II Intrinsics Currently Missing from the Julia Ecosystem\n\nIn practice, CUDA, AMD, and Intel GPUs share a similar architecture. Cores (called SMs in CUDA) schedule groups of threads. Each group is composed of warps which are set of 32 perfectly synchronized threads. Warp size varies in function of the architecture, but the principle is the same. Warps communicate through shared memory; blocks communicate through global memory. The relationship between threads and memory can be seen as a producer/consumer model: threads issue read or write requests to global memory, which returns data according to its bandwidth. For compute-bound workloads like matrix multiplication, performance bottlenecks lie in optimizing computation within threads, since memory has time to keep up. But for memory-bound workloads like copy or prefix scan, the bottleneck shifts to memory access optimization. \nUnfortunately, KA.jl does not currently expose the full set of intrinsics needed to achieve peak GPU performance, particularly on the memory access side. For KernelForge.jl, we have been developing these intrinsics in [KernelIntrinsics.jl](https://epilliat.github.io/KernelIntrinsics.jl) (currently available only for the CUDA backend, though extension to other backends is feasible):\n\n- **Warp operations with extended types:** GPUs execute threads in warps of 32 (CUDA) or similar groups (AMD, Intel). CUDA.jl does not support warp operations on extended types such as quaternions or NTuples.\n\n- **Vectorized loads/stores:** Loading multiple `Float32` values simultaneously enables faster memory bandwidth saturation, yielding substantial performance gains, especially when data fits within L2 cache.\n\n- **Fences and memory ordering:** Explicit control over memory ordering is essential for correct and efficient inter-group communication on GPUs. In particular, this avoids kernel relaunching and global synchronization between blocks. Performance gain is particularly noticeable for kernels such as prefix scan, for which we use a decoupled lookback algorithm.\n\nOur intrinsics design draws inspiration from UnsafeAtomics.jl (for its structural approach) and CUDA.jl.\n\n\n## III KernelForge.jl and Our Objectives\n\nKernelForge.jl demonstrates that abstracted kernels built with KA.jl can achieve backend-level efficiency, at least on CUDA. We provide kernels for matrix-vector and vector-matrix products (supporting general operators and bitstype elements), mapreduce over 1D and 2D arrays, prefix scan, and copy operations that match libcuda-level bandwidth through vectorized loads/stores. Benchmarks available at [KernelForge.jl](https://epilliat.github.io/KernelForge.jl) show performance matching or exceeding proprietary CUDA functions and CUB. The package includes correctness tests and the kernels are stable, though views and strided arrays are not yet supported.\n\nKernelForge.jl builds on KA.jl and [KernelIntrinsics.jl](https://epilliat.github.io/KernelIntrinsics.jl) (CUDA-only for now). Each operation is implemented with a single kernel, using vectorized loads and stores to saturate memory bandwidth, warp-level reductions for faster intra-warp computations, and strong memory ordering to enable correct inter-workgroup communication.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7KTZYQ", "name": "Emmanuel Pilliat", "avatar": "https://pretalx.com/media/avatars/7KTZYQ_XM93IyJ.webp", "biography": "I am an assistant professor in statistics at ENSAI (Rennes), working on machine learning and high-dimensional statistics. My current fields of research include crowdsourcing, change-point detection, bandit theory, dimension reduction, and clustering. My PhD focused on change-point detection and ranking problems. \nI am also interested in high-performance computing with Julia. I develop [Luma.jl](https://epilliat.github.io/Luma.jl/), a package for portable GPU primitives like matrix-vector operations, prefix sum, mapreduce and copy that aim to match vendor-optimized performance, and [KernelIntrinsics.jl](https://epilliat.github.io/KernelIntrinsics.jl/stable/), which provides currently missing low-level intrinsics.", "public_name": "Emmanuel Pilliat", "guid": "b2a56bae-2b79-51f3-ad01-96117b3b16fe", "url": "https://pretalx.com/juliacon-2026/speaker/7KTZYQ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/EFQ8YD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/EFQ8YD/", "attachments": []}, {"guid": "7659c066-4d58-5f4e-a113-c5a488614711", "code": "7ZFWC8", "id": 89345, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7ZFWC8/image_5a0riXq.webp", "date": "2026-08-13T16:15:00+02:00", "start": "16:15", "end": "2026-08-13T16:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-89345-separablefunctions-jl", "url": "https://pretalx.com/juliacon-2026/talk/7ZFWC8/", "title": "SeparableFunctions.jl", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "This package provides a collection of functions which can be written in a form separated by coordinates.\nAn example is a multidimensional Gaussian, a parabolic potential or a complex valued plane wave.\nTypically the separable functions are combined by a multiplication but there are also examples using other operators to combine them. Upon construction the separable parts are pre-calculated and a Julia-generic `Base.Broadcast.Broadcasted` object, which behaves a bit like a lazy array. It seamlessly merges with other broadcasting operations. The package, albeit being CUDA-agnostic, is fully capable of working with `CuArray` objects, creating a pre-calculated 1-dimensional `CuArray` for each dimension which then takes part in the broadcasting. The use of `SeparableFuctions.jl` significantly speeds up calculations and saves on-board memory. It is currently used in a number of other packages, for example `StructuredIlluminationMicroscopy.jl` which reconstructs optical images supported by acceleration via `CUDA.jl`(See also the Computational Physics Minisymposium).", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "CNV3SE", "name": "Rainer Heintzmann", "avatar": "https://pretalx.com/media/avatars/CNV3SE_lYiMnOM.webp", "biography": "I am heading a department at the Leibniz Institute of Photonic Technology, where our research focuses on imaging cellular function at high resolution. We develop new light microscopy techniques to measure multidimensional information in small biological objects such as cells, cellular organelles or other small structures of interest.\n\nComputer-based reconstruction methods, in particular in Julia, are a core focus and support many of our developments.", "public_name": "Rainer Heintzmann", "guid": "838d872b-70f8-5f6c-9acd-36f5a0289cf6", "url": "https://pretalx.com/juliacon-2026/speaker/CNV3SE/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7ZFWC8/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7ZFWC8/", "attachments": []}, {"guid": "1a797e5b-e67f-56fd-8f10-a7300b741387", "code": "W7FRKU", "id": 92661, "logo": "https://pretalx.com/media/juliacon-2026/submissions/W7FRKU/image_a9z5UJp.webp", "date": "2026-08-13T16:30:00+02:00", "start": "16:30", "end": "2026-08-13T16:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92661-the-gpu-acceleration-of-speedyweather-jl-the-friendly-and-flexible-climate-model", "url": "https://pretalx.com/juliacon-2026/talk/W7FRKU/", "title": "The GPU acceleration of SpeedyWeather.jl, the friendly and flexible climate model", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "Fortran climate models are being adapted to GPUs by automatically translating loop-by-loop into a kernel. In Julia, we have more flexibility to develop the climate model [SpeedyWeather.jl](https://github.com/SpeedyWeather/SpeedyWeather.jl) for the GPU. Many parts are easy to accelerate, leverage multiple dispatch on the GPU and a high level of kernel fusion for modularity and performance, while being optionally hardware-specific. The spherical harmonic transforms remain a complex bottleneck but we employ a multi-algorithm approach with custom linear algebra kernels using Reactant, Fourier and Legendre transforms.", "description": "Climate modelling continues to rely widely on CPUs as large code bases are not adapted to run on GPUs. Yet, climate models require high-performance computing to reach societally relevant resolutions and increased accuracy in weather and climate prediction. Here, we present [SpeedyWeather.jl](https://github.com/SpeedyWeather/SpeedyWeather.jl) an atmospheric model with dynamic representations of ocean, land and sea ice allowing for global climate simulations. We rely on the Julia-stack for GPU accelerated computing to support Nvidia, AMD and Apple GPUs and report our experience: What works, what does not, what is easy, what is difficult. Our spherical harmonics transform library SpeedyTransforms.jl implements a multi-algorithm approach: Leveraging Fourier and Legendre transforms of varying lengths which remain difficult to scale at low and high resolution. Alternatively, we implemented custom LinearAlgebra kernels for complex-real matrix-matrix multiplies which are easier to optimize using Reactant.jl. Many custom kernels are written for other parts of SpeedyWeather. The so-called parameterizations, the representation of unresolved physical processes such as radiation, precipitation or surface fluxes required further attention: We leverage multiple dispatch on the GPU and a high level of kernel fusion to achieve both flexibility and performance. We employ hardware-specific optimizations with little additional code, for example, changing loop orders between CPU and GPU. The parameterizations contain many different components (one for each physical process) that a SpeedyWeather user would want to compose in many various ways, flexibly switching or modifying them. Our implementation yields both: Flexibility and performance, and new developers can easily write extensions while hiding by default much of the GPU specifics from them. Most parts of SpeedyWeather are easily accelerated by 100x or more on a single GPU compared to single CPU but some bottlenecks remain in the algorithmically complex transforms.\n\nAuthors:\n\nMilan Kl\u00f6wer (1), Maximilian Gelbrecht (2, 3), Niklas Viebig (1, 4)\n\n1. University of Oxford, UK\n2. Potsdam Institute for Climate Impact Research, Germany\n3. Technical University of Munich, Germany\n4. ETH Z\u00fcrich, Switzerland", "recording_license": "", "do_not_record": false, "persons": [{"code": "A9SQSW", "name": "Milan Kl\u00f6wer", "avatar": "https://pretalx.com/media/avatars/A9SQSW_nKoqOOA.webp", "biography": "Milan Kl\u00f6wer is a NERC Independent Research Fellow at the University of Oxford. He did his postdoc at the Massachusetts Institute of Technology (MIT) working on climate model development in Julia. He started SpeedyWeather.jl, a global atmospheric model designed as a research playground to develop prototype ideas on machine-learned representations of climate processes and computationally efficient climate models. He also works on low precision computing, data compression and information theory, predictability of weather and climate, and software engineering.", "public_name": "Milan Kl\u00f6wer", "guid": "da34690e-e394-5c01-a2f8-6876c357d29c", "url": "https://pretalx.com/juliacon-2026/speaker/A9SQSW/"}, {"code": "UWLWET", "name": "Niklas Viebig", "avatar": "https://pretalx.com/media/avatars/787YWJ_j6AIbZs.webp", "biography": "Master\u2019s student in Physics at ETH Zurich, currently completing my Master\u2019s thesis in the [climate modeling group ](https://climate-modelling.github.io)at AOPP, University of Oxford. Im researching differentiable programming and systematic parameter calibration for Earth system models, with interests in exoplanet climates, high-performance computing, and scientific software engineering.", "public_name": "Niklas Viebig", "guid": "6da790fe-d52e-57e6-9775-5fda4d6081c0", "url": "https://pretalx.com/juliacon-2026/speaker/UWLWET/"}, {"code": "FRX3ZM", "name": "Maximilian Gelbrecht", "avatar": null, "biography": "Researching differentiable programming and machine learning for Earth system models and dynamical systems", "public_name": "Maximilian Gelbrecht", "guid": "73255bbe-2284-5620-b0a1-2c3519a50514", "url": "https://pretalx.com/juliacon-2026/speaker/FRX3ZM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/W7FRKU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/W7FRKU/", "attachments": [{"title": "flyer-W7FRKU", "url": "/media/juliacon-2026/submissions/W7FRKU/resources/W7FRKU_ZiiYoLr.png", "type": "related"}]}, {"guid": "51abb5a2-b98f-59e8-8a41-d7bf1bd6ab2c", "code": "FX73BU", "id": 92428, "logo": "https://pretalx.com/media/juliacon-2026/submissions/FX73BU/image_chXO3Qz.webp", "date": "2026-08-13T16:45:00+02:00", "start": "16:45", "end": "2026-08-13T17:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92428-tile-based-gpu-programming-with-cutile-jl", "url": "https://pretalx.com/juliacon-2026/talk/FX73BU/", "title": "Tile-Based GPU Programming with cuTile.jl", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "CUDA is well known for its SIMT programming model, available in Julia through CUDA.jl. This year, NVIDIA introduces cuTile, a new tile-based programming model for writing high-performance GPU kernels, with automatic tensor core utilization. cuTile.jl brings this model to Julia, compiling Julia kernels through a custom pipeline to Tile IR bytecode. In this talk, we'll cover the programming model, the compiler design, and performance benchmarks on Blackwell GPUs.", "description": "CUDA Tile is a significant evolution in GPU programming, so it's important that Julia GPU developers have access to it. This talk will introduce the cuTile programming model, why it matters, and how cuTile.jl makes it possible to write tile-based GPU kernels directly in Julia.\n\nSpecifically, the talk will cover:\n- The programming model: what tile-based programming is, how it abstracts over threads and warps, and how it enables automatic tensor core utilization.\n- The compiler pipeline: what the Tile IR bytecode is, and how we target it from Julia using a custom compiler.\n- Performance benchmarks: how cuTile.jl performs on Blackwell GPUs, and how it compares to Python's cuTile.\n- The relationship between cuTile.jl and CUDA.jl, and how they can complement each other for different types of GPU programming.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9YQMAK", "name": "Tim Besard", "avatar": "https://pretalx.com/media/avatars/9YQMAK_BNCDK1H.webp", "biography": "Tim Besard is a software engineer at JuliaHub, where he leads GPU support and development for the Julia programming language. He holds a Ph.D. in computer science engineering from Ghent University, Belgium, and has been a key contributor to Julia's GPU ecosystem since 2014. Tim maintains several foundational GPU packages including CUDA.jl, GPUArrays.jl, GPUCompiler.jl, and LLVM.jl, which together form the backbone of GPU computing in Julia.", "public_name": "Tim Besard", "guid": "63c32f70-0ed1-5b19-9487-52b2891214e7", "url": "https://pretalx.com/juliacon-2026/speaker/9YQMAK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/FX73BU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/FX73BU/", "attachments": []}, {"guid": "77fd8834-2c7c-5f64-9039-70212808bd38", "code": "BPEJLA", "id": 93460, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BPEJLA/image_ZtMdx4H.webp", "date": "2026-08-13T17:00:00+02:00", "start": "17:00", "end": "2026-08-13T17:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93460-what-s-new-in-cuda-jl-besides-cutile", "url": "https://pretalx.com/juliacon-2026/talk/BPEJLA/", "title": "What's new in CUDA.jl (besides CuTile)?", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "Even more improvements and features have been since this package was discussed last year at JuliacCon 2025. This talk will highlight some of the more meaningful user-facing feature additions, performance and quality of life improvements, as well as significant bug fixes.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "YYDPCZ", "name": "Katharine Hyatt", "avatar": null, "biography": "I am a Julia contributor since 2015. I work mostly on GPUs, quantum packages, and linear algebra.", "public_name": "Katharine Hyatt", "guid": "729bd10c-f6f5-53bf-b7b6-fff8ee7a09ae", "url": "https://pretalx.com/juliacon-2026/speaker/YYDPCZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BPEJLA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BPEJLA/", "attachments": []}, {"guid": "32ed0c9c-d0b4-5ea0-ad4b-9b004b989b21", "code": "X3GGMU", "id": 93448, "logo": "https://pretalx.com/media/juliacon-2026/submissions/X3GGMU/image_5c58HBl.webp", "date": "2026-08-13T17:15:00+02:00", "start": "17:15", "end": "2026-08-13T17:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93448-what-s-new-in-metal-jl", "url": "https://pretalx.com/juliacon-2026/talk/X3GGMU/", "title": "What's new in Metal.jl", "subtitle": "", "track": "Julia, GPUs, and Accelerators", "type": "Short talk", "language": "en", "abstract": "Major improvements and features have been implemented in Metal.jl since this package was last discussed at JuliaCon. This talk will highlight some of the more meaningful user-facing feature additions, performance and quality of life improvements, as well as significant bug fixes.", "description": "Many researchers use Apple silicon devices as their day-to-day computer. Metal has the potential to remove the need for a separate computer to run certain computations and analyses. We\u2019ll cover improvements to support for the core metal API, potential showstopper issues that have been fixed, as well as well as some heavily requested features that were recently added that makes Metal.jl a viable option for an increasing number of use-cases.", "recording_license": "", "do_not_record": false, "persons": [{"code": "JWP7DS", "name": "Christian Guinard", "avatar": null, "biography": "I have been contributing to the Julia ecosystem since 2022. I help maintain Metal.jl, as well contribute to the rest of the JuliaGPU ecosystem and related repositories.", "public_name": "Christian Guinard", "guid": "13d60743-8c2f-56a0-bfe9-734302368f39", "url": "https://pretalx.com/juliacon-2026/speaker/JWP7DS/"}], "links": [{"title": "Metal.jl Github", "url": "https://github.com/JuliaGPU/Metal.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/X3GGMU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/X3GGMU/", "attachments": []}], "Alte Mensa \u2014 Audi Max": [{"guid": "6f3a667a-5299-51cd-97a2-bc31ae9a3458", "code": "BNPWFP", "id": 93441, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BNPWFP/image_c9TIiVL.webp", "date": "2026-08-13T12:45:00+02:00", "start": "12:45", "end": "2026-08-13T13:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93441-jlpigraf-jl-a-package-for-data-retrieval-and-table-merging-with-the-epigraf-api", "url": "https://pretalx.com/juliacon-2026/talk/BNPWFP/", "title": "Jlpigraf.jl, a package for data retrieval and table merging with the Epigraf API", "subtitle": "", "track": "Bringing Julia to the Computational Humanities and Social Sciences", "type": "Short talk", "language": "en", "abstract": "We present the Jlpigraf package for interacting with the Epigraf API. [Epigraf ](https://github.com/digicademy/epigraf) is an open-source research platform designed for the collection, annotation, linking, and publication of multimodal text data. Its data model supports research databases ranging from epistolary editions to social media corpora. Currently, Epigraf is primarily used for editing epigraphic data \u2014 inscriptions in connection with the objects to which they are attached. The platform includes a publication system for various document formats, such as Word and TEI, as well as structured JSON, XML, and CSV data and triples in TTL, JSON-LD, and RDF/XML. Thus, Epigraf is building a bridge between traditional editorial work and computational humanities.\n\nUntil now, the [Rpigraf](https://github.com/datavana/rpigraf) R package is provided to interact with the Epigraf API. The Jlpigraf package brings a significant portion of Rpigraf's functionality to the Julia programming language. The goal is twofold. Firstly, implementing the package leverages the performance and syntax advantages of Julia for data analysis and processing in the humanities. Secondly, Epigraf originated in a specific humanities research context, and we are working to open the platform up to other disciplines and research communities.\n\nEpigraf uses the [Relational Article Model](https://epigraf.inschriften.net/help/coreconcepts/model) (RAM) to internally manage the database content. The basic building blocks are articles grouped into projects and subdivided into sections that contain items. Items hold the specific data such as text, image file names or references to categories. Articles are linked to category systems directly via tagging them in items or via in-text annotations. Examples of category systems include bibliographic references, persons and locations, authority data, and in-text annotation vocabularies. The RAM's building blocks are then mapped to the specific research domain.\n\nThe Jlpigraf package has the following core tasks (work in progress):\n    1. Interacting with the Epigraf API, including authentication, data fetching, and data manipulation.\n    2. Extracting, joining and transforming RAM data.\n    3. Handling hierarchical data and tree structures.\nThus, the package relieves users of frequently recurring tasks. It provides easy access to the corpora stored in Epigraf. A basic understanding of the Relational Article Model's structure is sufficient to work with the data. In the proposed talk, we will provide a brief introduction to the topic and the Relational Article Model. Then, we will explain the objectives of the package using application examples. Finally, we will discuss the process of translating from R to Julia, emphasizing relevant language features and the Julia package ecosystem.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZMSNFG", "name": "Georg Hertkorn", "avatar": null, "biography": "Since 2024: Research assistant at the Digital Academy of the Academy of Sciences and Literature Mainz; Project: German Inscriptions Online\n\n2020 to 2023: Research assistant at the Lower Saxony Academy of Sciences in G\u00f6ttingen; Project: Knowledge Aggregator Middle Ages and Early Modern Period\n\n2005-2012: Project Coordinator at STAR AG, a technical documentation and corporate communications service provider in Ramsen (CH)\n\n2004: PhD Thesis: Microscopic modelling of time-dependent traffic flow patterns\n\n1928 to 2005: Project manager in the Traffic in Conurbations project, German Aerospace Centre, Berlin\n\n1989 to 1996: Studied physics at the University of Konstanz", "public_name": "Georg Hertkorn", "guid": "d1973e45-3dc7-5fd2-9a32-46c5096f7086", "url": "https://pretalx.com/juliacon-2026/speaker/ZMSNFG/"}], "links": [{"title": "Jlpigraf package repository", "url": "https://github.com/datavana/Jlpigraf.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BNPWFP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BNPWFP/", "attachments": []}, {"guid": "3125d842-88ce-5b4c-9dc3-158bbf09c04b", "code": "JXMW8H", "id": 90998, "logo": "https://pretalx.com/media/juliacon-2026/submissions/JXMW8H/image_KP8ZbNz.webp", "date": "2026-08-13T14:30:00+02:00", "start": "14:30", "end": "2026-08-13T15:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-90998-hyperloglog-over-rsa-anonymously-counting-users", "url": "https://pretalx.com/juliacon-2026/talk/JXMW8H/", "title": "HyperLogLog Over RSA: Anonymously Counting Users", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "We have long wanted to be able to count unique clients using Julia and various packages, but privacy concerns have prevented us from doing so. A new protocol combining two magical technologies\u2014HyperLogLog cardinality estimation and RSA public key encryption\u2014allows accurate estimation of client counts while provably preserving anonymity. This protocol, dubbed \"HyperLogLog Over RSA\" is now built into Julia's Pkg client, allowing us to finally get reliable client counts.", "description": "This talk walks through the problem of anonymous client counting, how having each client generate a unique random ID serves as a gold standard in terms of functionality but has terrible privacy properties. We introduce HyperLogLog, a brilliant technique for estimating cardinalities of unique values in small, fixed memory. It also provides a good first cut method for anonymous client counting, but has two major flaws:\n\n1. HLL has good privacy _on average_ but some clients are uniquely identifiable\u2014it is not uniformly anonymous.\n2. HLL values are trivially forgeable: a malicious client can arbitrarily inflate estimates with a fixed amount of effort.\n\nWe discuss how to address these two issues and incrementally arrive at a design where HyperLogLog values are randomly generated by each client in already encrypted form, so the client can neither decipher nor bias its HLL value. If this encrypted value were sent every time, it would uniquely identify a client, so a crucial trick of this protocol is that each client can randomize the value it sends in such a way that any two clients with the same HLL are indistinguishable, thereby preserving privacy. This is combined with \"sharding\" HLL values based `on` the package being requested, so that clients cannot be tracked across packages, and uniform anonymity is restored across packages.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9NNRE7", "name": "Stefan Karpinski", "avatar": "https://pretalx.com/media/avatars/9NNRE7_UASzy0Z.webp", "biography": "Julia co-creator and JuliaHub/Dyad co-founder: https://juliahub.com", "public_name": "Stefan Karpinski", "guid": "028e4611-bde2-5948-8dc0-e5d7b301e181", "url": "https://pretalx.com/juliacon-2026/speaker/9NNRE7/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/JXMW8H/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/JXMW8H/", "attachments": []}, {"guid": "2f2f5c95-7441-5829-8e8e-bd1370fa72b5", "code": "LCUBGM", "id": 92885, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LCUBGM/image_QDY2U4M.webp", "date": "2026-08-13T15:00:00+02:00", "start": "15:00", "end": "2026-08-13T15:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92885-making-your-julia-code-compliant-for-usage-in-pharmaceutical-industry", "url": "https://pretalx.com/juliacon-2026/talk/LCUBGM/", "title": "Making your Julia code compliant for usage in pharmaceutical industry", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "As the pharmaceutical industry shifts toward Model-Informed Drug Development (MIDD) and complex pharmacometric simulations, the Julia programming language has emerged as a frontrunner due to the availability of specialized ecosystem for Pharmacometrics (Through Pumas, SciML and JuliaDiff packages) and support for High Performance Computing, and interoperability.\n\nHowever, deploying Julia in a GxP-regulated environment requires more than just efficient algorithms; it demands strict adherence to software validation, reproducibility, and data integrity standards such as FDA 21 CFR Part 11 and GAMP 5.\n\nThis talk outlines a strategic framework for making Julia code \"pharma-ready.\" We explore the use of Julia\u2019s native packages to maintain Reproducibility, the implementation of testing suites for the generation of validation reports and we will also explore how to use literate programming in Julia to generate documentation and reports . By leveraging Julia\u2019s unique features, we show how developers can create high-performance workflows that satisfy both computational demands and regulatory scrutiny.", "description": "Non-compliant software cannot be used in the pharmaceutical industry because it directly compromises patient safety, data integrity, and product quality. In a highly regulated sector (e.g., FDA, EMA, WHO), software must undergo rigorous validation to prove it consistently operates as intended. Non-compliant, invalidated systems lead to legal, financial, and safety risks.\n\nAchieving compliance in a regulatory setting isn't about the language itself, but the processes wrapped around it. To make Julia code compliant for pharmaceutical use, developers must focus on following aspects\n\n1.  Reproducibility:- Software reproducibility in the pharmaceutical industry refers to the ability of a computerized system to consistently generate, process, and output the same results, data, or product quality metrics whenever it is used, regardless of who is operating it or when it is operated. This is a critical component of GAMP 5 and makes sure software used in GxP (Good Practice) environments consistently adheres to FDA 21 CFR Part 11 and EU GMP Annex 11 regulations. In this talk, we will show how to ensure reproducibility in your Julia program using Pkg.jl and other external Julia packages.\n\n2. Documentation:- We will show how to use literate programming in Julia using Literate.jl or Weave.jl to generate reports where code, assumptions, and results live in a single document.\n\n3. Validation:- Regulatory bodies require proof that the software performs its intended functions. These proofs are provided in form of following reports\n\n    - Installation Qualification (IQ) report: Provides evidence that the Julia app and its dependent libraries are installed correctly in the production environment.\n\n    - Operational Qualification (OQ) report: Provides evidence that your Julia app operates correctly according to specifications and functional requirements. \n\n    - Performance Qualification (PQ) report: Provides evidence that your Julia app operates consistently and reproducibly under routine, real-world conditions to meet predefined specifications, this report also includes a conclusion confirming the Julia app is ready for performing its routine production tasks.\n\n     In the talk, we will show you how to generate these pieces of evidence with respect to your Julia app and show an example of these reports.\n\nAll the recommendations that will be discussed in this talk; stem from eight years of developing and maintaining Julia-based solutions which were deployed in regulated pharmaceutical environments.", "recording_license": "", "do_not_record": false, "persons": [{"code": "MUZVCW", "name": "Harsha Byadarahalli Mahesh", "avatar": "https://pretalx.com/media/avatars/8LAJ8S_BeIJFnY.webp", "biography": "I am a Senior Software Engineer with 10 years of experience at JuliaHub. Over the past decade, I have transitioned from developing core Julia-based desktop products (JuliaPro, Pumas) to my current role on the Operations team, I specialize in developing, deploying, and maintaining robust Julia-based software solutions. My background includes collaborating with the Pumas team to streamline the build processes for the Pumas and DeepPumas JuliaHub apps.\n\nCurrently, I am a member of the operations team, where I manage deployments and upgrades for our enterprise clients. Alongside overseeing the broader platform release process (With respect to compliance), I actively develop and maintain the RStudio and WindowsWorkstation apps. I also work closely with the compliance team to ensure the JuliaHub platform meets the strict regulatory standards required by our pharmaceutical enterprise customers.", "public_name": "Harsha Byadarahalli Mahesh", "guid": "ecd5dbfd-ae7a-56fe-ad27-6fbd491b9483", "url": "https://pretalx.com/juliacon-2026/speaker/MUZVCW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LCUBGM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LCUBGM/", "attachments": []}, {"guid": "debbf451-7f0c-51d9-a8d6-ca1245a1ed09", "code": "R8VPMR", "id": 88939, "logo": "https://pretalx.com/media/juliacon-2026/submissions/R8VPMR/magic-logo-and-text_EWL3sc6.svg", "date": "2026-08-13T15:15:00+02:00", "start": "15:15", "end": "2026-08-13T15:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-88939-a-new-way-of-creating-julia-web-apps", "url": "https://pretalx.com/juliacon-2026/talk/R8VPMR/", "title": "A new way of creating Julia web apps", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "For better or worse, we live in a world where being online is essential for being known. This applies not only to people, but also to ideas, algorithms, and software created by Julia programmers. Your research may be groundbreaking and your algorithms elegant, but if they are not accessible online, they are unlikely to be seen or used.\n\nMany researchers recognize this and try to publicize their work through papers, talks, and blog posts. However, the most effective way to communicate a computational idea is not just to describe it, but to let people *interact* with it. A web app allows users to explore a model, test an algorithm, or visualize results directly, making the value of the work immediately clear.\n\nThe problem is that most scientists and researchers lack the time or expertise to build web applications. Traditional web frameworks lower the barrier somewhat, but they still require knowledge of web development concepts and introduce friction when all you want is a simple, interactive interface to your Julia code.\n\n[Magic.jl](https://github.com/nidoro/Magic.jl) is designed to remove this friction. Aimed at scientists and researchers, it provides a simple, Julia-native way to turn algorithms and solutions into interactive web apps, in the same spirit that Streamlit does for Python.", "description": "[Magic.jl](https://github.com/nidoro/Magic.jl) has a distinctive design that sets it apart from existing web app frameworks in the Julia ecosystem. Most current frameworks assume a traditional web architecture based on request\u2013response cycles, declarative user interfaces, and reactive interactions. *Magic.jl*, by contrast, is built around direct execution of Julia code. A *Magic* web app is simply a Julia script that is rerun from top to bottom on every user interaction. This script-centered design makes the execution flow easy to reason about and aligns naturally with how scientists and researchers typically write and think about code.\n\nIn this talk, we will:\n- Introduce Magic's core concepts and features\n- Discuss possible use cases\n- Demonstrate it with example web applications\n- Explain how it compares to other web app frameworks", "recording_license": "", "do_not_record": false, "persons": [{"code": "BKF3XX", "name": "Davi Doro", "avatar": "https://pretalx.com/media/avatars/BKF3XX_vxTNNDE.webp", "biography": "I've been programming for a living and for fun for more than 10 years now. I have a master in Operations Research, with a focus on heuristics and mathematical optimization. And I play the keyboard at my local church Esperan\u00e7a in Belo Horizonte, Brazil.", "public_name": "Davi Doro", "guid": "a6151b48-34b5-5d21-903d-567f03c12ae7", "url": "https://pretalx.com/juliacon-2026/speaker/BKF3XX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/R8VPMR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/R8VPMR/", "attachments": [{"title": "flyer-R8VPMR", "url": "/media/juliacon-2026/submissions/R8VPMR/resources/R8VPMR_ZanOF2H.png", "type": "related"}]}, {"guid": "93d719a9-08b2-5c6e-81f3-b5373547da25", "code": "MQKGAM", "id": 92537, "logo": "https://pretalx.com/media/juliacon-2026/submissions/MQKGAM/image_nB2VU1V.webp", "date": "2026-08-13T15:45:00+02:00", "start": "15:45", "end": "2026-08-13T16:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92537-speedrand-jl-how-to-not-implement-your-own-prng-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/MQKGAM/", "title": "SpeedRand.jl - How to (not?) Implement your own PRNG in Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "SpeedRand.jl is a fully-implemented alternative toy PRNG that implements (more or less) the full suite of Julia's informally-documented AbstractRNG interface, with a terrible, humorous twist.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "8PXS3T", "name": "Kevin Qing", "avatar": null, "biography": "Kevin Qing is a data scientist who has spent far too much time trying to solve problems that don't exist.", "public_name": "Kevin Qing", "guid": "c244d0e7-c7ad-5745-b339-da15e77c0b85", "url": "https://pretalx.com/juliacon-2026/speaker/8PXS3T/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/MQKGAM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/MQKGAM/", "attachments": []}, {"guid": "36dff363-3848-5f75-8dbf-e1f344931fe3", "code": "YWEB3Y", "id": 92831, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YWEB3Y/image_OZ7iyXH.webp", "date": "2026-08-13T16:00:00+02:00", "start": "16:00", "end": "2026-08-13T16:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92831-how-to-teach-an-online-julia-course", "url": "https://pretalx.com/juliacon-2026/talk/YWEB3Y/", "title": "How to teach an online Julia course", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "In this talk, I reflect on my experience designing, delivering, and openly releasing a university course - High Performance Computing in Julia - whose materials have since found an audience beyond my own students.\n\nI will begin with the story behind the course: why Julia was the natural choice for teaching high performance computing, and how the course developed over the years of teaching. From there, I will offer a practical account of what it takes to build an online course, the software choices considered and used, along with alternative approaches that other educators may prefer.\n\nWhether you are an educator considering Julia for your own teaching, or a community member thinking about sharing your expertise through educational material online, this talk should have something interesting for you.", "description": "The talk will give a background on the High Performance Computing in Julia course, its aims and a description of its structure.\n\nI will then dive into the technical details of how the course is put together, covering, but not limited to:\n- Producing and publishing online lecture notes\n- Creating animations and visual aids using code\n- Recording, editing and publishing videos\n- Distributing and marking assessments\n\nIn each of these topics, I will provide the software and techniques used, as well as the alternatives considered that may be more applicable to other educators.", "recording_license": "", "do_not_record": false, "persons": [{"code": "E3FS9B", "name": "Dr. Jamie Mair", "avatar": "https://pretalx.com/media/avatars/J7NXYR_ontLfBd.webp", "biography": "Dr. Jamie Mair is a Research & Teaching Fellow at the University of Nottingham. He teaches courses to undergraduates and postgraduates on the topic of High Performance Computing and Reinforcement Learning. Throughout his research career, Jamie has advocated for the use of Julia in academic research, and has published several research packages in Julia. He has delivered several talks and presentations aiming to introduce Julia to a wider audience.\n\nJamie received his PhD in Machine Learning and Statistical Physics, along with his BSc in Theoretical Physics from the University of Nottingham.", "public_name": "Dr. Jamie Mair", "guid": "f6a16cfa-be8f-5f5a-870c-7fb1dd4a45aa", "url": "https://pretalx.com/juliacon-2026/speaker/E3FS9B/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YWEB3Y/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YWEB3Y/", "attachments": []}, {"guid": "7ecdc71f-d7b6-5df5-a775-561dcc5e399a", "code": "SZPFSX", "id": 92489, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SZPFSX/image_45OSqgd.webp", "date": "2026-08-13T16:15:00+02:00", "start": "16:15", "end": "2026-08-13T16:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92489-juliacheck-industrial-grade-static-code-analysis-for-julia", "url": "https://pretalx.com/juliacon-2026/talk/SZPFSX/", "title": "JuliaCheck: Industrial-Grade Static Code Analysis for Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "JuliaCheck.jl is an extensible, rule-based static code analyzer for Julia, developed by [TIOBE ](www.tiobe.com)(in collaboration with [ASML](www.asml.com)). Built on JuliaSyntax.jl, it enforces configurable coding standards (from style and structure to security) and integrates with TIOBE's TiCS quality framework, bringing enterprise-grade code quality measurement to Julia for the first time.", "description": "As Julia matures and finds adoption in safety-critical and industrial environments, the need for rigorous, automated code quality tooling grows accordingly. While the ecosystem offers excellent tools like JET.jl and Aqua.jl, there remains a clear gap: configurable, rule-driven static analysis that integrates with enterprise software quality frameworks.\nA natural candidate for this role is semgrep, currently recommended by JuliaHub for Julia static analysis. However, semgrep relies on its own Julia parser, which is not always in sync with the latest JuliaSyntax developments, meaning it can silently fail or produce incorrect results on modern Julia code. [JuliaCheck.jl](https://github.com/tiobe/JuliaCheck.jl) takes a different approach: it is built entirely in Julia and uses JuliaSyntax.jl directly for AST traversal, ensuring it stays current with the language itself and benefits from the same parser that underpins Julia's own tooling ecosystem.\nJuliaCheck.jl provides a dynamic rule engine where each check is a self-contained, selectively enableable unit. Beyond detecting violations, it lets users filter results and generate comprehensive violation reports (consumable as highlighted terminal text, structured JSON, or plain text) making it a natural fit for CI/CD pipelines.\nThe rule set at the core of JuliaCheck is being developed jointly by TIOBE and ASML. On top of this, users can define and load their own custom rules to enforce project-specific standards.\nWhat further distinguishes JuliaCheck is its integration with TIOBE's TiCS framework, one of the most widely adopted software quality platforms in enterprise environments. Julia projects can now be measured against ISO/IEC 25010-aligned quality metrics alongside C++, Java, and Python \u2014 enabling organizations with mixed-language codebases to maintain consistent quality standards across their entire portfolio.\nThe talk will cover the architecture, the ASML/TIOBE rule catalog, custom rule authoring, violation filtering and reporting, TiCS integration, a live demo on a real Julia codebase, showing a roadmap for moving it to the Julia OpenSource registry and the upcoming features (e.g., bring JET.jl results directly into the TiCS dashboard).", "recording_license": "", "do_not_record": false, "persons": [{"code": "8BMA3P", "name": "Evangelos Paradas", "avatar": null, "biography": "I am Evangelos Paradas from Thessaloniki, Greece. I am physicist, holding a PhD in Particle Physics. The trip into the algorithms' world, started during my PhD, as I was responsible for a few algorithms of the High Level trigger of the CMS experiment at CERN.\nIn this context, the algorithms were written in C++. After a few years I moved to the Netherlands, working at ASML as Algorithm  Deployment architect.", "public_name": "Evangelos Paradas", "guid": "a66edfc4-fc98-5dff-a266-fef0a5fb3058", "url": "https://pretalx.com/juliacon-2026/speaker/8BMA3P/"}, {"code": "MTEN3N", "name": "Paul Jansen", "avatar": "https://pretalx.com/media/avatars/3PVT33_r3uQfK0.webp", "biography": "Paul Jansen (1967) graduated from the University of Amsterdam in computing science and philosophy (both cum laude). At Philips Research he was a computer scientist in the field of compiler construction and domain-specific languages. After a brief stay at Atos Origin and QA Systems, he founded TIOBE Software in 2000. Paul Jansen is the driving force behind the definition of the TIOBE Quality Indicator (TQI) and the famous TIOBE index that is published every month.", "public_name": "Paul Jansen", "guid": "dd2204ee-eaab-54db-952a-940879d54708", "url": "https://pretalx.com/juliacon-2026/speaker/MTEN3N/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SZPFSX/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SZPFSX/", "attachments": []}, {"guid": "353199ec-1ac3-51a0-999f-66ddbee276d1", "code": "HTGCKD", "id": 89941, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HTGCKD/image_gbwTOVQ.webp", "date": "2026-08-13T16:30:00+02:00", "start": "16:30", "end": "2026-08-13T16:45:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-89941-directed-hypergraph-structures-for-complex-network-analysis-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/HTGCKD/", "title": "(Directed) Hypergraph Structures for Complex Network Analysis in Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Many important networks have beyond-binary relations that make graph structures inefficient or insufficient representations. Hypergraphs, the generalizations of traditional graphs, are needed to study such complex networks. In this talk, we will discuss hypergraph modeling in Julia, primarily focusing on SimpleDirectedHypergraphs.jl, a recently developed package for complex networks with n-ary directional relations.", "description": "Commonly, researchers and engineers represent various networks \u2014 ecological, social, computer, and more \u2014 as graphs. However, graphs can encode only binary relations (i.e., edges connect exactly two vertices), making them inappropriate to model many complex networks of technical and research significance. For these cases \u2014 including modeling relational databases, financial transfers, and chemical reaction networks \u2014 more general hypergraphs are essential tools. In hypergraphs, hyperedges can connect arbitrary numbers of vertices, bypassing the key binary restriction of graphs.\n\nIn this talk, we will discuss efforts to represent and model hypergraphs, especially directed hypergraphs, in Julia. We will begin by introducing hypergraphs and their practical utility for network modeling before shifting to hypergraph representations and tools in Julia.\n\nWe will briefly discuss SimpleHypergraphs.jl, developed by Przemys\u0142aw Szufel and colleagues.[1] We will introduce the sparse representation of hypergraphs as matrices, the connection with Graphs.jl, and mention various features and analyses available for undirected hypergraphs.\n\nThe bulk of the talk will focus on directed hypergraphs, implemented in the recently developed package SimpleDirectedHypergraphs.jl. We will demonstrate how to construct directed hypergraphs by-hand, from external data, and randomly. We will further introduce a number of algorithms included in SimpleDirectedHypergraphs.jl, in particular focusing on heuristic and exact pathfinding. Our discussion will conclude with a small application involving random and real-world chemical reaction networks.\n\nSimpleDirectedHypergraphs.jl is the first package for directed hypergraph construction and analysis in Julia. To the best of our knowledge, general-purpose directed hypergraph packages are also absent in many high-level programming languages used in mathematics and science (e.g., R, MATLAB), with most available packages focusing exclusively (e.g., hypernetx[2], [HyperG](https://cran.r-project.org/web/packages/HyperG/refman/HyperG.html)) or mainly on undirected hypergraphs (e.g., directed hypergraphs are, at the time of this writing, an \"experimental feature\" in the Python package [XGI](https://xgi.readthedocs.io/en/stable/api/core/xgi.core.dihypergraph.html)). Beyond being an addition to the Julia ecosystem, SimpleDirectedHypergraphs.jl is thus positioned to benefit network science and network scientists broadly.\n\nNotes:\n[1]: Antelmi et al., arXiv:2002.04654 2020, DOI: 10.48550/arXiv.2002.04654\n[2]: Praggastis et al., arXiv:2310.11626 2023, DOI: 10.48550/arXiv.2310.11626", "recording_license": "", "do_not_record": false, "persons": [{"code": "GBXDYV", "name": "Evan Walter Clark Spotte-Smith (they/them)", "avatar": "https://pretalx.com/media/avatars/GBXDYV_ddpR4oj.webp", "biography": "I'm a teacher-scholar currently based in Dublin, Ireland, where I work at University College Dublin as an Ad Astra Fellow & Assistant Professor of Digital Chemistry. I am also an Adjunct Professor of Chemical Engineering at Carnegie Mellon University. Though my training is in materials science and my professional affiliations are in chemistry and chemical engineering, my research interests are broad, including not only areas of the chemical sciences (e.g., sustainable chemistry, catalysis, electrochemistry, chemical reaction networks) but also network science, data science, pedagogy, philosophy (philosophy of science and ethics), mathematics (combinatorics), and more.\n\nI founded and am currently working to build up the Community of Researchers Assessing Chemical Transformations and Exploring Reactivity (CoReACTER), an anti-oppressive, democratic research collective. I am an active supporter of open-source software, both through my own development efforts and in my work as a Topic Editor for the Journal of Open Source Software. I care deeply about teaching the next generation of scientists and researchers, and I actively seek out opportunities to mentor others, particularly those from marginalized backgrounds.\n\nOutside of my academic work, I love reading, writing, drinking tea, and hiking.", "public_name": "Evan Walter Clark Spotte-Smith (they/them)", "guid": "6228c493-37a9-5c73-a3aa-4b6a34586f38", "url": "https://pretalx.com/juliacon-2026/speaker/GBXDYV/"}, {"code": "AWWXHH", "name": "Zhenya Barannik", "avatar": "https://pretalx.com/media/avatars/AWWXHH_YbdJdLq.webp", "biography": "Radiochemist, spectroscopist, and data scientist (kinda). Now a PhD student studying chemical reaction networks and machine learning on directed hypergraphs.", "public_name": "Zhenya Barannik", "guid": "5dffd474-6a70-5ca5-a517-a44007aefad9", "url": "https://pretalx.com/juliacon-2026/speaker/AWWXHH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HTGCKD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HTGCKD/", "attachments": []}, {"guid": "3b23282e-acd9-5ca0-8033-5fc8bb15416f", "code": "ZBQKTY", "id": 92867, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZBQKTY/image_ZFVfxXM.webp", "date": "2026-08-13T17:00:00+02:00", "start": "17:00", "end": "2026-08-13T17:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92867-what-s-new-in-copulas-jl", "url": "https://pretalx.com/juliacon-2026/talk/ZBQKTY/", "title": "What's new in Copulas.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Since 2022, Copulas.jl has provided native support for dependence modeling in Julia. Copulas are multivariate distribution functions on the unit hypercube that allow practitioners to model dependence structures separately from marginal behavior. By building on the Distributions.jl framework, Copulas.jl integrates seamlessly with Julia\u2019s probabilistic and statistical ecosystem.\n\nIn this talk, we review the major design improvements and new features introduced since the first public releases of Copulas.jl. The package now offers a broad collection of classical parametric copula families, along with tools for evaluating distribution functions and densities, computing dependence measures such as Kendall\u2019s tau and Spearman\u2019s rho, estimating parameters via inversion of moments or maximum likelihood, and fitting models to data.\n\nA key feature of the package is the Sklar type, inspired by Sklar\u2019s Theorem, which enables users to construct full multivariate models by combining copulas with arbitrary marginal distributions. These composite models are fully compatible with the Distributions.jl API, making them directly usable in downstream tools such as Turing.jl for Bayesian inference.\n\nWe conclude with practical examples showcasing how the new features of Copulas.jl enable advanced dependence modeling workflows entirely in native Julia.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "A77EDZ", "name": "Oskar Laverny", "avatar": "https://pretalx.com/media/avatars/E9PVET_vFqKEVA.webp", "biography": "I am currently an associate professor in statistics in Marseille (France). Actuary by formation, I focus my researches on high dimensional statistics and dependence structures estimations, with a lot of applications in insurance, reinsurance, and more recently public health. I do have a taste for numerical code and open-source software, and most of my work is freely available on GitHub.", "public_name": "Oskar Laverny", "guid": "afa9e373-10c2-5b21-b05a-e9ae391128c7", "url": "https://pretalx.com/juliacon-2026/speaker/A77EDZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZBQKTY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZBQKTY/", "attachments": []}, {"guid": "f893ecec-ea23-55ac-8150-9848f3d1ef37", "code": "G7CLZ7", "id": 93667, "logo": "https://pretalx.com/media/juliacon-2026/submissions/G7CLZ7/image_XSGLfh9.webp", "date": "2026-08-13T17:15:00+02:00", "start": "17:15", "end": "2026-08-13T17:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93667-how-to-extend-peridynamics-jl-for-your-own-research", "url": "https://pretalx.com/juliacon-2026/talk/G7CLZ7/", "title": "How to Extend Peridynamics.jl for Your Own Research", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Peridynamics.jl is a Julia package for dynamic fracture simulations that supports multithreading and MPI for high-performance computing. This talk focuses on how researchers in peridynamics can adapt the package to their own models rather than on the internal development of the code base. We show how Julia's multiple dispatch and type system make it straightforward to implement custom material models, damage criteria, and boundary conditions, while the package handles parallelism and I/O. The goal is to give researchers a practical starting point for their own extensions, attracting more users to the package and to the Julia ecosystem in general.", "description": "Peridynamics is a nonlocal continuum formulation that is particularly useful for simulating crack propagation, fragmentation, and similar discontinuous phenomena. We developed Peridynamics.jl with high-performance computing in mind, but for the peridynamics community it is just as important that the package can be adapted to specific research questions without too much effort.\n\nIn this talk, we show how the extension interface of the package works in practice. To implement a new material model, users only need to define the routines that describe the constitutive behavior. Parallel execution, halo exchange between MPI ranks and threads, and output are handled by the package. The same approach can be used for custom contact laws, damage criteria, boundary conditions, and post-processing callbacks. Since extensions are ordinary Julia types and methods, they compose naturally with the models that are already available. For example, users can set up multi-material simulations that combine built-in and user-defined formulations without modifying the package itself. Users can also use the built-in methods for parameter studies and post-processing, allowing for a seamless workflow from model definition to results analysis.\n\nWe go through specific examples and show how a constitutive formulation can be turned into a working parallel simulation with only a small amount of Julia code. We also discuss the design decisions behind this extensibility and how Julia helps make Peridynamics.jl a flexible research tool for the peridynamics community.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7V8ELT", "name": "Kai Partmann", "avatar": "https://pretalx.com/media/avatars/KDTKRE_x8UtjQH.webp", "biography": "PhD Candidate, University of Siegen, Germany\n\nSpecializing in solid mechanics, with a focus on dynamic fracture, peridynamics, phase-field modeling, and continuum mechanics.", "public_name": "Kai Partmann", "guid": "2ee4a74a-b56b-5bed-b091-55e9a2423797", "url": "https://pretalx.com/juliacon-2026/speaker/7V8ELT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/G7CLZ7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/G7CLZ7/", "attachments": [{"title": "flyer-G7CLZ7", "url": "/media/juliacon-2026/submissions/G7CLZ7/resources/G7CLZ7_IRqFD1d.png", "type": "related"}]}], "Alte Mensa \u2014 Atrium Maximum": [{"guid": "fb69e0a8-e3b5-541c-8854-79427ee18721", "code": "SRHZCN", "id": 92449, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SRHZCN/image_bT2eB9W.webp", "date": "2026-08-13T10:00:00+02:00", "start": "10:00", "end": "2026-08-13T10:30:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92449-accuracy-of-mathematical-functions-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/SRHZCN/", "title": "Accuracy of Mathematical Functions in Julia", "subtitle": "", "track": "Approximate Computing in Numerical Linear Algebra", "type": "Long talk", "language": "en", "abstract": "Basic computer arithmetic operations, such as +, \u00d7, or \u00f7 are correctly rounded, whilst mathematical functions such as ex, ln(x), or sin(x) in general are not, meaning that separate implementations may provide different results when presented with an exact same input, and that their accuracy may differ. We present a methodology and a software tool that is suited for exhaustive and non-exhaustive testing of mathematical functions of Julia in various floating-point formats. The software tool is useful to the users of Julia, to quantise the level of accuracy of the mathematical functions and interpret possible effects of errors on their scientific computation codes that depend on these functions. It is also useful to the developers and maintainers of the functions in Julia Base, to test the modifications to existing functions and to test the accuracy of new functions. The software (a test bench) is designed to be easy to set up for running the accuracy tests in automatic regression testing. Our focus is to provide software that is user friendly and allows to avoid the need for specialised knowledge of floating-point arithmetic or the workings of mathematical functions; users only need to supply a list of formats, choose the rounding modes, and specify the input space search strategies based on how long they can afford the testing to run. We have utilized the test bench to determine the errors of a subset of mathematical functions in the latest version of Julia, for binary16, binary32, and binary64 IEEE 754 floating-point formats, and found 0.49 to 0.51ULPs in binary16, and 0.5 to 2.4ULPs of error in binary32 and binary64. The functions that may be correctly rounded (error of 0.5ULP) in all the three formats are sqrt and cbrt. The following functions may be correctly rounded only for binary16: sinh, asin, cospi, sinpi, atanh, log2, tanh.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "QCXNQM", "name": "Mantas Mikaitis", "avatar": "https://pretalx.com/media/avatars/QCXNQM_DuMpPb7.webp", "biography": "I am an Associate Professor in the School of Computer Science at the University of Leeds. Before this I was a Research Associate with the Numerical Linear Algebra Group at the University of Manchester, working with Professor Nicholas J. Higham. I received a B.Sc. (Hons.) degree in Computer Science in 2016 and a PhD degree in Computer Science in 2020, both from the University of Manchester. My research interests include various aspects of computer arithmetic.\n\nWebsite: https://mmikaitis.github.io", "public_name": "Mantas Mikaitis", "guid": "470a7252-4b03-500b-86a7-b2c53c974082", "url": "https://pretalx.com/juliacon-2026/speaker/QCXNQM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SRHZCN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SRHZCN/", "attachments": []}, {"guid": "2c10d068-80f5-56e1-b7e9-e44e909ec966", "code": "UZXK9Y", "id": 92860, "logo": "https://pretalx.com/media/juliacon-2026/submissions/UZXK9Y/image_94m8VBp.webp", "date": "2026-08-13T10:30:00+02:00", "start": "10:30", "end": "2026-08-13T11:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92860-structured-iterative-approximations-in-numerical-multi-linear-algebra", "url": "https://pretalx.com/juliacon-2026/talk/UZXK9Y/", "title": "Structured iterative approximations in numerical (multi-)linear algebra", "subtitle": "", "track": "Approximate Computing in Numerical Linear Algebra", "type": "Long talk", "language": "en", "abstract": "We present recently developed iterative methods for approximating matrices and tensors with structural constraints such as rank and sparsity level and pattern, extending to settings where data is incomplete or indirectly observed, with or without noise. These methods aim to solve canonical problems of numerical (multi-)linear algebra, namely approximate matrix inversion and low-rank matrix and tensor approximation, when structural constraints are imposed on the approximation. While the presented results revolve around aspects of sparsity, if time permits, we extend the presentation to other structural features such as non-negativity. We also present how this work contributes to the ongoing development of the repositories ApproximateMatrixInverses.jl, StructuredLowRankMatrices.jl, and StructuredLowRankTensors.jl.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "FTGFUH", "name": "Nicolas Venkovic", "avatar": null, "biography": "Postdoctoral Scientific Staff Member @ Chair of Computational Mathematics, TU Munich", "public_name": "Nicolas Venkovic", "guid": "c99f0efc-b62d-5a64-b903-d3093e4d0771", "url": "https://pretalx.com/juliacon-2026/speaker/FTGFUH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/UZXK9Y/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/UZXK9Y/", "attachments": []}, {"guid": "c6c4899f-f135-5a3c-9abc-e6c2a7e952c0", "code": "SHTJ3F", "id": 91337, "logo": "https://pretalx.com/media/juliacon-2026/submissions/SHTJ3F/image_NswkzZx.webp", "date": "2026-08-13T11:15:00+02:00", "start": "11:15", "end": "2026-08-13T11:45:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-91337-matrixequations-jl-a-continuous-effort-to-achieve-performance-and-genericity", "url": "https://pretalx.com/juliacon-2026/talk/SHTJ3F/", "title": "MatrixEquations.jl - a continuous effort to achieve performance and genericity", "subtitle": "", "track": "Approximate Computing in Numerical Linear Algebra", "type": "Long talk", "language": "en", "abstract": "This presentation discusses the development of [MatrixEquations.jl](https://github.com/andreasvarga/MatrixEquations.jl), a comprehensive Julia package for solving specialized matrix equations, including Lyapunov, Sylvester, and Riccati types. While these equations are fundamental to control systems analysis and synthesis, their utility extends across numerous scientific domains. The package\u2019s impact is substantial: it serves as a critical dependency for over 60 packages within the Julia ecosystem and averages over 2,000 monthly downloads.\n\nReflecting on its status as my most successful software project to date, I look forward to sharing insights into its development\u2014specifically regarding two distinct implementation challenges: achieving peak numerical performance by leveraging optimized, hardware-specific libraries, and providing generic functionality that supports Julia\u2019s abstract type system to work seamlessly with arbitrary-precision and non-standard number types.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "R7DFKR", "name": "Andreas Varga", "avatar": null, "biography": "Andreas Varga received the diploma in control engineering in 1974 and the Ph.D. degree in electrical engineering in 1981, both from the University \"Politechnica\" of Bucharest (Romania). From 1974 to 1993 he have held various research positions at the Institute of Informatics Bucharest and at the Ruhr-University of Bochum. From 1990 to 1992 he worked at the Ruhr-University of Bochum as visiting research fellow in the framework of a fellowship award of the Alexander von Humboldt Foundation. From 1993 until his retirement in 2015 he worked at the German Aerospace Center (DLR) in Oberpfaffenhofen, where he was a Senior Scientist of the Institute of System Dyanmics and Control. Andreas Varga has been a visiting fellow at the Kyoto University (1994), California Institute of Technology (2000), Australian National University (2000), University of Hong Kong (2000), and University of Umea (2002, 2008).\n\nThe main research interests of Dr. Varga include the numerical methods for linear systems analysis and design (with special emphasis on model and controller reduction, descriptor systems, periodic systems, fault detection), and robust numerical software for computer aided control system design (CACSD). He authored two books, coauthored three books, coedited two books, published over 65 papers in refereed journals or book chapters, and have over 155 conference publications. During his active career (1974-2015) he was involved in several CACSD related software projects, being the developer of over 20 software packages implemented in Fortran and MATLAB. After his retirement he focussed on implementing free software in the Julia language, being the main author of  7 Julia packages. \n\nAndreas Varga became in 2003 a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) \"for contributions to the development of numerical methods for computer aided analysis and design of control systems\". He served as  Associate Editor for the IEEE Transactions on Automatic Control between 1997-1999 and served as Program Chairman or General Chair of several IEEE sponsored conferences (e.g., CACSD, CCA, ISIC, SYSTOL). \n\n[Private homepage](https://sites.google.com/view/andreasvarga/home)", "public_name": "Andreas Varga", "guid": "c43b692c-307f-5e9b-83a8-aec3e1016036", "url": "https://pretalx.com/juliacon-2026/speaker/R7DFKR/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/SHTJ3F/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/SHTJ3F/", "attachments": []}, {"guid": "86f752f7-20ba-5b2b-8801-2759faf85285", "code": "AV3GA8", "id": 92072, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AV3GA8/image_w0v6aHY.webp", "date": "2026-08-13T11:45:00+02:00", "start": "11:45", "end": "2026-08-13T12:15:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92072-differentialriccatiequations-jl-solving-matrix-equations-with-low-rank-solutions", "url": "https://pretalx.com/juliacon-2026/talk/AV3GA8/", "title": "DifferentialRiccatiEquations.jl: Solving matrix equations with low-rank solutions", "subtitle": "", "track": "Approximate Computing in Numerical Linear Algebra", "type": "Long talk", "language": "en", "abstract": "Solving large matrix equations can be very expensive. For many applications, however, the dense solutions can be well approximated by low-rank factorizations: products of two or three matrices with a substantially smaller inner dimension. And certain quantities can yet again be computed as a linear function of the solution, meaning one does not even need to assemble the low-rank factors. DifferentialRiccatiEquations.jl is a package to cater all these use cases for differential and (continuous-time) algebraic Riccati and Lyapunov equations.", "description": "The differential Riccati equation arises, for example, in optimal control of the linear-quadratic regulator. DifferentialRiccatiEquations.jl (DRE.jl) started as a port of some low-rank Rosenbrock methods written in MATLAB (with the aim to distribute the computations using the parareal method; but this is a story for another day). At every Rosenbrock step one has to solve an algebraic Lyapunov equation, which can be done using the Alternating-Direction Implicit (ADI) method. Therefore, the package quickly evolved into a test bed for improving the low-rank Lyapunov ADI method, from GPU support to mixed-precision low-rank factorizations.", "recording_license": "", "do_not_record": false, "persons": [{"code": "LRQ8W8", "name": "Jonas Schulze", "avatar": "https://pretalx.com/media/avatars/LRQ8W8_2fIULGn.webp", "biography": "- PhD student at the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany\n- Works in mixed precision for matrix equations with low-rank solution\n- @jonas-schulze on GitHub", "public_name": "Jonas Schulze", "guid": "ccdabc28-3d08-55fd-a1be-fabbeb19d6c0", "url": "https://pretalx.com/juliacon-2026/speaker/LRQ8W8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AV3GA8/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AV3GA8/", "attachments": []}, {"guid": "9135c881-0241-55f1-a6cb-1ef7e6668cf7", "code": "NXU8WC", "id": 92412, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NXU8WC/image_lBtxVPX.webp", "date": "2026-08-13T12:15:00+02:00", "start": "12:15", "end": "2026-08-13T12:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92412-hierarchical-precision-and-recursion-for-accelerating-symmetric-linear-solves-on-mxus", "url": "https://pretalx.com/juliacon-2026/talk/NXU8WC/", "title": "Hierarchical Precision and Recursion for Accelerating Symmetric Linear Solves on MXUs", "subtitle": "", "track": "Approximate Computing in Numerical Linear Algebra", "type": "Short talk", "language": "en", "abstract": "We implemented a mixed-precision, nested recursive Cholesky algorithm for GPU Matrix Processing Units (NVIDIA H200, AMD MI300X) using Julia. With a hierarchical precision method, we maximize throughput while maintaining numerical stability. Our recursive SYRK achieves a 14x speedup over cuBLAS, leading to a 5.32x overall speedup for Cholesky over cuSOLVER FP64. The solver leverages Julia\u2019s multiple dispatch to provide a portable interface for HPC.", "description": "Symmetric linear solves are fundamental to a wide range of scientific and engineering applications, from climate modeling and structural analysis to machine learning and optimization. These workloads often rely on Cholesky (POTRF) decomposition and its supporting operations - triangular solves (TRSM) and symmetric rank-k updates (SYRK) - which together form the computational core for solving symmetric positive definite systems. To accelerate these kernels, we present a portable, mixed-precision solver designed for Matrix Processing Units (MXUs), including NVIDIA Tensor Cores (H200) and AMD Matrix Cores (MI300X). Our algorithm builds on a nested recursive formulation in which Cholesky exposes parallelism through recursive decomposition of its TRSM and SYRK subproblems, incorporating the first recursive GPU implementation of SYRK. This structure yields a hierarchical recursion that maximizes GEMM throughput while enabling fine-grained control over numerical precision. We introduce a custom recursive data structure that assigns low-precision FP16 arithmetic to large off-diagonal blocks, while preserving high precision on diagonal blocks to ensure numerical stability. To mitigate the limited dynamic range of FP16, we integrate a lightweight block-wise quantization scheme that prevents numerical overflow.\nThe solver is implemented in Julia, leveraging array programming, multiple dispatch, and dynamic type inference to enable seamless expression of mixed-precision computation. This design provides a high-level, hardware-agnostic interface while efficiently interfacing with low-level vendor libraries for backend portability. On H200, our recursive FP64 SYRK achieves a 14x speedup over cuBLAS, while mixed-precision delivers up to 27.0x speedup in SYRK and 5.3x in TRSM over full-precision baselines. This results in a 5.32x overall speedup for Cholesky versus cuSOLVER FP64, with 100x better accuracy than pure FP16 while retaining 88% of its peak speedup. Comparable performance and accuracy trends are observed on MI300X, demonstrating broad applicability across GPUs.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NWXNRM", "name": "Vicki Carrica", "avatar": "https://pretalx.com/media/avatars/NYS9S8_bjzQwKB.webp", "biography": "Vicki Carrica is a Computer Science and Engineering undergraduate at the Massachusetts Institute of Technology (MIT) graduating in 2027. As a researcher in the MIT Julia Lab, she contributes to the development of high-performance linear algebra routines, focusing on GPU acceleration and algorithmic efficiency.", "public_name": "Vicki Carrica", "guid": "79f8e53d-6d8a-5e05-8732-6aa6d16d7401", "url": "https://pretalx.com/juliacon-2026/speaker/NWXNRM/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NXU8WC/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NXU8WC/", "attachments": []}, {"guid": "b16f2426-ca1f-5508-bc9e-dc1e5de98e6b", "code": "J3MRUE", "id": 104401, "logo": "https://pretalx.com/media/juliacon-2026/submissions/J3MRUE/image_MolMA1h.webp", "date": "2026-08-13T12:30:00+02:00", "start": "12:30", "end": "2026-08-13T13:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-104401-approximate-computing-community-panel", "url": "https://pretalx.com/juliacon-2026/talk/J3MRUE/", "title": "Approximate Computing Community Panel", "subtitle": "", "track": "Approximate Computing in Numerical Linear Algebra", "type": "Birds of Feather (BoF)", "language": "en", "abstract": "Open discussion for the community attending the mini, not to be recorded.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "LRQ8W8", "name": "Jonas Schulze", "avatar": "https://pretalx.com/media/avatars/LRQ8W8_2fIULGn.webp", "biography": "- PhD student at the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany\n- Works in mixed precision for matrix equations with low-rank solution\n- @jonas-schulze on GitHub", "public_name": "Jonas Schulze", "guid": "ccdabc28-3d08-55fd-a1be-fabbeb19d6c0", "url": "https://pretalx.com/juliacon-2026/speaker/LRQ8W8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/J3MRUE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/J3MRUE/", "attachments": []}, {"guid": "714fee62-384a-5d62-b7d9-e7a87550d290", "code": "XS9BT7", "id": 92712, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XS9BT7/image_n2m34mC.webp", "date": "2026-08-13T14:30:00+02:00", "start": "14:30", "end": "2026-08-13T15:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92712-juliadecisionfocusedlearning-a-practical-introduction-to-decision-focused-learning-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/XS9BT7/", "title": "JuliaDecisionFocusedLearning: A Practical Introduction to Decision-Focused Learning in Julia", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Decision-Focused Learning (DFL) is a field at the intersection of machine learning and combinatorial optimization. It integrates prediction with combinatorial decision-making by embedding optimization algorithms directly into machine learning pipelines. This talk presents the **JuliaDecisionFocusedLearning** ecosystem, focusing on `DecisionFocusedLearningBenchmarks.jl` and `DecisionFocusedLearningAlgorithms.jl`, two new packages that provide a high-level and generic interface for using state-of-the-art DFL methods.", "description": "This talk provides a practical introduction to Decision-Focused Learning (DFL) in Julia. For a recent survey of the field, see https://arxiv.org/abs/2601.10583.\nRather than focusing on theoretical details, the goal is to use a small example problem to showcase the capabilities of the **JuliaDecisionFocusedLearning** ecosystem. \n\n**JuliaDecisionFocusedLearning** started with `InferOpt.jl`, which provides the core building blocks for constructing differentiable combinatorial optimization layers and associated loss functions. We introduce two new higher-level packages:\n- [`DecisionFocusedLearningBenchmarks.jl`](https://github.com/JuliaDecisionFocusedLearning/DecisionFocusedLearningBenchmarks.jl)\n  - Provides a growing collection of benchmark combinatorial decision problems.\n  - Includes all necessary components to build and train a DFL policy for each problem.\n  - Offers a general interface for defining custom problems.\n  - Facilitates reproducibility and experimentation by allowing the same algorithm to be applied across multiple benchmarks with minimal changes.\n- [`DecisionFocusedLearningAlgorithms.jl`](https://github.com/JuliaDecisionFocusedLearning/DecisionFocusedLearningAlgorithms.jl)\n  - Implements generic versions of state-of-the-art DFL training algorithms.\n  - Provides compatibility with problems defined through the benchmarks interface.\n  - While some classical DFL approaches can be implemented directly using the lower-level tools provided by InferOpt.jl, recent state-of-the-art methods have become increasingly complex. This package introduces higher-level implementations to make these advanced algorithms accessible without requiring users to master the technical details.\n\nThe goal of the talk is to demonstrate how users can leverage these packages to define a problem, select a training strategy, and train a policy for their own problem in a few lines of code, without necessarily needing deep expertise in DFL.", "recording_license": "", "do_not_record": false, "persons": [{"code": "YWXCYP", "name": "L\u00e9o Baty", "avatar": null, "biography": "Research engineer in combinatorial optimization and machine learning. Member of JuliaDecisionFocusedLearning.", "public_name": "L\u00e9o Baty", "guid": "a85c0452-0d8f-5926-9b96-b457223b1d1a", "url": "https://pretalx.com/juliacon-2026/speaker/YWXCYP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XS9BT7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XS9BT7/", "attachments": []}, {"guid": "84e2ebf4-9071-5f92-929e-d3037cada990", "code": "HBY8MD", "id": 92822, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HBY8MD/image_820CSft.webp", "date": "2026-08-13T15:00:00+02:00", "start": "15:00", "end": "2026-08-13T15:30:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92822-teaching-opaque-machine-learning-models-plausible-and-actionable-explanations", "url": "https://pretalx.com/juliacon-2026/talk/HBY8MD/", "title": "Teaching Opaque Machine Learning Models Plausible and Actionable Explanations", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "[CounterfactualTraining.jl](https://github.com/JuliaTrustworthyAI/CounterfactualTraining.jl) leverages [CounterfactualExplanations.jl](https://github.com/JuliaTrustworthyAI/CounterfactualExplanations.jl) to make opaque machine learning models like artificial neural networks more 1) explainable, 2) sensitive to actionability constraints and 3) adversarially robust. The package is part of the [Taija](https://www.taija.org/) ecosystem for Trustworthy AI in Julia and the engine behind our [IEEE SaTML 2026](https://satml.org/accepted-papers/) paper titled *[Counterfactual Training: Teaching Models Plausible and Actionable Explanations](https://arxiv.org/abs/2601.16205)*.", "description": "In our research [paper](https://arxiv.org/abs/2601.16205), we propose a novel training regime termed counterfactual training that leverages counterfactual explanations to increase the explanatory capacity of models. \n\n## Counterfactual Explanations and Algorithmic Recourse\n\nCounterfactual explanations (CE) have emerged as a popular post-hoc explanation method for opaque machine learning models and artificial intelligence (AI): they inform how factual inputs would need to change in order for a model to produce some desired output. To be useful in real-world decision-making systems, counterfactuals should be plausible with respect to the underlying data and actionable with respect to the feature mutability constraints. This facilitates the use of CE for the purpose of algorithmic recourse (AR): helping individuals subject to opaque AI to turn negative outcomes into positive ones. Much existing research has therefore focused on developing post-hoc methods to generate counterfactuals that meet these desiderata.\n\nIn Julia, CE and AR can be generated and benchmarked using Taija's [CounterfactualExplanations.jl](https://github.com/JuliaTrustworthyAI/CounterfactualExplanations.jl).\n\n## Counterfactual Training\n\nIn our latest research, we instead hold models directly accountable for the desired end goal: counterfactual training employs counterfactuals during the training phase to minimize the divergence between learned representations and plausible, actionable explanations. We demonstrate empirically and theoretically that our proposed method facilitates training models that deliver inherently desirable counterfactual explanations and additionally exhibit improved adversarial robustness. \n\nOur new [CounterfactualTraining.jl](https://github.com/JuliaTrustworthyAI/CounterfactualTraining.jl) package was developed during the research process. To run large-scale experiments, it leverages [CounterfactualExplanations.jl](https://github.com/JuliaTrustworthyAI/CounterfactualExplanations.jl)'s support for multi-processing CE.\n\n## Real-World Impact\n\nOur approach and package enables researchers and practitioners to train more trustworthy models without changing their architecture. If, for example, a particular problem lends itself to using an artificial neural network, you can improve its trustworthiness through counterfactual training, instead of training it conventionally.\n\n## Limitations\n\nSince this package was developed during the research process, it was designed to fit that purpose. While the package is fully functional, its user-facing API, documentation and performance have room for improvement. Through this talk, we hope to receive feedback and ideas from the community and attract contributors.\n\n## Further Reading\n\nThis work is the culmination of Patrick's PhD, from which he recently graduated. The development of Taija has played a key role in his PhD. If you're interested in getting a broader picture, you may find his [thesis](https://www.patalt.org/thesis/) and [defence talk](https://www.patalt.org/content/talks/posts/2026-defence/) useful.", "recording_license": "", "do_not_record": false, "persons": [{"code": "EMBAGL", "name": "Patrick Altmeyer", "avatar": "https://pretalx.com/media/avatars/8DGYCX_qJRPla8.webp", "biography": "I'm a visiting researcher at Delft University of Technology where I recently graduated from my Ph.D. in Trustworthy Artificial Intelligence and Finance. My research revolves around Counterfactual Explanations and Probabilistic Machine Learning. Previously, I worked as an Economist for the Bank of England.\n\nI started working with Julia at the beginning of PhD in late 2021 and have since developed and used various packages, some of which I presented at JuliaCon 2022, 2023 and 2024. These packages now have a common home called [Taija](https://github.com/JuliaTrustworthyAI), which stands for Trustworthy Artificial Intelligence in Julia.\n\nYou can find out more about my work on my [website](https://www.patalt.org/).", "public_name": "Patrick Altmeyer", "guid": "04bb8e84-6a64-51a5-a00c-d4f7166dd08f", "url": "https://pretalx.com/juliacon-2026/speaker/EMBAGL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HBY8MD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HBY8MD/", "attachments": [{"title": "flyer-HBY8MD", "url": "/media/juliacon-2026/submissions/HBY8MD/resources/HBY8MD_KDheURZ.png", "type": "related"}]}, {"guid": "8c9eb6b3-fcf0-5184-ad96-b03b14f834fa", "code": "JFXJHC", "id": 92827, "logo": "https://pretalx.com/media/juliacon-2026/submissions/JFXJHC/image_wv8vpuI.webp", "date": "2026-08-13T15:45:00+02:00", "start": "15:45", "end": "2026-08-13T16:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92827-handle-your-handles", "url": "https://pretalx.com/juliacon-2026/talk/JFXJHC/", "title": "Handle your handles", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Handles can be used instead of explicitly using pointers or references to access objects. This is done by storing the objects of a given type in a `Memory` or `Vector` and using the handle as an index. With a suitable abstraction, which Handles.jl provides, it turns out that this combines attractive properties like being faster than object references in certain situations and being safer than regular indexing for a static set of objects. Therefore, handles provide a pattern which might be interesting to use in large parts of the Julia ecosystem where efficiency matters and certain constraints hold.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "UGCEZ9", "name": "Patrick H\u00e4cker", "avatar": null, "biography": "- Studies of Electrical Engineering and Information Science at University Stuttgart\n- PhD in statistical signal processing at University Stuttgart\n- Working for Bosch in different roles on perception systems for road and rail vehicles", "public_name": "Patrick H\u00e4cker", "guid": "d48bc7be-b514-52b8-ac3f-91753799ff14", "url": "https://pretalx.com/juliacon-2026/speaker/UGCEZ9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/JFXJHC/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/JFXJHC/", "attachments": []}, {"guid": "a9ab218b-4454-52fb-8f16-0d52a7d98014", "code": "8H9T9C", "id": 91885, "logo": "https://pretalx.com/media/juliacon-2026/submissions/8H9T9C/image_6KkJFCS.webp", "date": "2026-08-13T16:00:00+02:00", "start": "16:00", "end": "2026-08-13T16:30:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-91885-district-scale-energy-system-simulation-with-resie", "url": "https://pretalx.com/juliacon-2026/talk/8H9T9C/", "title": "District-scale energy system simulation with ReSiE", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "We introduce the inner workings and use of ReSiE, a new package for the simulation of sector-coupled energy systems with complex non-linear control schemes on the scale of city districts. The package is based on a different mathematical approach than comparable tools in the field of energy system modelling, which typically use MILP. The strengths and weaknesses of this approach are discussed. Furthermore we present some \"lessons learned\" and how Julia was leveraged to implement the package.", "description": "The [ReSiE package](https://github.com/QuaSi-Software/resie) is the simulation engine of a larger collection of tools for the planning, modelling, simulation and analysis of sector-coupled energy systems on the scale of city districts. The typical use case of the package is the early planning stage of a project, when limited information is available, yet important decisions have to be made concerning which technologies to employ and how the energy system components are connected and controlled.\n\nAs an \"engine\", its chief concern is the calculation of power, heat and other energy flows for each timestep, as well as the necessary post-processing to perform economical analysis of results, which is required for optimisation and parameter variation studies. Using the engine therefore does benefit from additional tools to create the necessary input configuration, but does not require the user to program any code.\n\nComparable tools in the field of energy system modelling and simulation often make use of mathematical models of linear optimisation and mixed-integer linear programming (MILP). In contrast, ReSiE uses a different approach based on aspects of systems analysis, agent-based simulation and graph theory. This incurs several dis-/advantages compared to established tools:\n* Optimisation is separated from simulation, however only black-box optimisation is possible\n* No limitation on the complexity of the individual energy system component models. For example the ground-coupled heat storage model uses a finite-volume method to co-simulate heat transport into the ground.\n* Complex non-linear control mechanisms involving the interaction of multiple components are possible, however new strategies have to be implemented as code\n* Highly flexible system topology with no hard limitations on size and depth of the network, however edge cases can occur that require deliberation\n\nDevelopment of the package was the first major project using Julia of all involved developers, which lead to several stumbling blocks along the way. The most severe of these are discussed alongside which Julia-specific considerations were observed.", "recording_license": "", "do_not_record": false, "persons": [{"code": "8HK8UA", "name": "Etienne Ott", "avatar": "https://pretalx.com/media/avatars/8HK8UA_av59QiZ.webp", "biography": "Etienne Ott works as researcher and software developer at siz energieplus. With 10 years of experience as software developer and a focus on software for mathematical modelling, simulation and technical monitoring in the field of energy systems and the built environment, he is involved in projects developing the tools for complex analyses of district energy systems and the performance of buildings.", "public_name": "Etienne Ott", "guid": "c8fbf596-b19e-50ee-b2bc-481bb1d57293", "url": "https://pretalx.com/juliacon-2026/speaker/8HK8UA/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/8H9T9C/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/8H9T9C/", "attachments": []}, {"guid": "f8826253-dda6-5846-9460-8c2e92b382b5", "code": "7QZCDB", "id": 102516, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7QZCDB/image_CMCoVjK.webp", "date": "2026-08-13T19:00:00+02:00", "start": "19:00", "end": "2026-08-13T21:00:00+02:00", "duration": "02:00", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-102516-juliacon-2026-poster-session", "url": "https://pretalx.com/juliacon-2026/talk/7QZCDB/", "title": "JuliaCon 2026 Poster Session", "subtitle": "", "track": "General", "type": "Poster", "language": "en", "abstract": "This is the poster session for all posters! It will take place at 19:00 13 August 2026. Come see all the great posters we have this year and enjoy some refreshments.", "description": "", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7QZCDB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7QZCDB/", "attachments": []}]}}, {"index": 5, "date": "2026-08-14", "day_start": "2026-08-14T04:00:00+02:00", "day_end": "2026-08-15T03:59:00+02:00", "rooms": {"Tent \u2014 RW1": [{"guid": "a1232fa9-ce5a-5ab7-af0b-14d120036be8", "code": "LCY7JK", "id": 93941, "logo": "https://pretalx.com/media/juliacon-2026/submissions/LCY7JK/image_d9UzDhk.webp", "date": "2026-08-14T08:45:00+02:00", "start": "08:45", "end": "2026-08-14T09:45:00+02:00", "duration": "01:00", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93941-breaking-the-non-recurring-cost-curve-using-model-based-methodologies", "url": "https://pretalx.com/juliacon-2026/talk/LCY7JK/", "title": "Breaking The Non-Recurring Cost Curve Using Model Based Methodologies", "subtitle": "", "track": "General", "type": "Keynote", "language": "en", "abstract": "Sponsor Keynote: Boeing and JuliaHub.\n\nBreaking the non-recurring cost curve requires a fundamental shift in how complex systems are designed, analyzed, verified, and integrated. Traditional development approaches often discover design defects late in the lifecycle, when correction is more expensive, schedules are more constrained, and integration risk is highest. Model Based Development (MBD) changes this dynamic by moving defect discovery earlier and extending the verification window through the use of high-fidelity design models, executable simulations, early functional prototyping, and model-driven validation. This presentation explores how model-based methodologies enable earlier requirements validation, improved design maturity, greater standardization, earlier verification test readiness, and higher-fidelity system integration. By placing detailed system design models at the center of the development process, organizations can reduce late-cycle rework, improve first-pass quality, accelerate integration maturity, and ultimately reduce non-recurring engineering cost while improving delivery confidence.\n\nSubsequently, this session will showcase Dyad, by Juliahub, demonstrating how Dyad enables easy and efficient model based development, leveraging and agentic UI and the Julia programming language.", "description": "Breaking the non-recurring cost curve requires a fundamental shift in how complex systems are designed, analyzed, verified, and integrated. Traditional development approaches often discover design defects late in the lifecycle, when correction is more expensive, schedules are more constrained, and integration risk is highest. Model Based Development (MBD) changes this dynamic by moving defect discovery earlier and extending the verification window through the use of high-fidelity design models, executable simulations, early functional prototyping, and model-driven validation. This presentation explores how model-based methodologies enable earlier requirements validation, improved design maturity, greater standardization, earlier verification test readiness, and higher-fidelity system integration. By placing detailed system design models at the center of the development process, organizations can reduce late-cycle rework, improve first-pass quality, accelerate integration maturity, and ultimately reduce non-recurring engineering cost while improving delivery confidence.\n\nSubsequently, this session will showcase Dyad, by Juliahub, demonstrating how Dyad enables easy and efficient model based development, leveraging and agentic UI and the Julia programming language.", "recording_license": "", "do_not_record": false, "persons": [{"code": "EHCBFL", "name": "Gary Mansouri", "avatar": null, "biography": "Gary Mansouri is a Technical Fellow, Chief Architect of Systems MBE (Model Based Engineering) and a Boeing designated expert and senior adviser in model-based detailed systems design with primary focus in multi-disciplinary dynamic systems behavioral modeling and simulation, integrated vehicle systems, systems architectures, control systems, actuators and sensors and embedded systems.", "public_name": "Gary Mansouri", "guid": "d3aa7bae-3715-5041-9b19-b2285f16d950", "url": "https://pretalx.com/juliacon-2026/speaker/EHCBFL/"}, {"code": "WUWQQ3", "name": "Chris Rackauckas", "avatar": "https://pretalx.com/media/avatars/WUWQQ3_otHw1Wk.webp", "biography": "Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.", "public_name": "Chris Rackauckas", "guid": "5ecf5886-9c68-55ca-8dcf-c4f85742c1ca", "url": "https://pretalx.com/juliacon-2026/speaker/WUWQQ3/"}, {"code": "E8A3VF", "name": "Viral B. Shah", "avatar": null, "biography": "Viral Shah is the co-founder and CEO of JuliaHub, Inc, and the co-creator of the Julia Programming Language", "public_name": "Viral B. Shah", "guid": "ce877776-555e-535b-8afe-a12be5c2b5b6", "url": "https://pretalx.com/juliacon-2026/speaker/E8A3VF/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/LCY7JK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/LCY7JK/", "attachments": []}, {"guid": "2eb05be9-ee81-5cc6-a2ef-f99e5930e137", "code": "PAH3UZ", "id": 93447, "logo": "https://pretalx.com/media/juliacon-2026/submissions/PAH3UZ/image_xSVQfrq.webp", "date": "2026-08-14T10:00:00+02:00", "start": "10:00", "end": "2026-08-14T10:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93447-securing-the-supply-chain-vulnerability-scanning-for-julia", "url": "https://pretalx.com/juliacon-2026/talk/PAH3UZ/", "title": "Securing the Supply Chain: Vulnerability Scanning for Julia", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Professional Julia use requires industrial security. This challenge is unique because risks often hide in binary dependencies (JLLs) that standard tools ignore. This talk, following the launch of the J**ulia Security Working Group**, shows how **Trivy** was adapted to scan the entire Julia dependency graph. We explore the implementation of this workflow within **JuliaHub** to provide automated security auditing and **SBOM generation** for any Julia project, ensuring safety for all.", "description": "This session demonstrates the technical integration of **Trivy** to provide vulnerability scanning for the Julia ecosystem. We outline how the **JuliaHub platform** automatically analyzes source code and dependencies to identify known risks, closing the **\"vulnerability gap\"** found in binary JLLs.\nThe session covers the core concepts of vulnerability scanning and demonstrates how these platforms integrate directly into development workflows to catch threats before they reach production. We conclude with a **live demonstration** of a workflow that brings these industrial-grade protections to every Julia project.", "recording_license": "", "do_not_record": false, "persons": [{"code": "JY8MSY", "name": "Mridul Ranjan Upadhyay", "avatar": "https://pretalx.com/media/avatars/KUECZN_S64A3DR.webp", "biography": "**Mridul Ranjan Upadhyay** serves as a Technical Program Manager at JuliaHub, orchestrating **strategic initiatives** and **technological innovation** at the intersection of research and industry. A forward-thinking leader and **multiple patent holder**, he specializes in transforming complex, high-level concepts into **scalable products.** Mridul is passionate about **professionalizing development lifecycles** and driving the evolution of **emerging technologies** within high-growth organizations.", "public_name": "Mridul Ranjan Upadhyay", "guid": "ad23414e-b459-596f-a85d-00b523919f27", "url": "https://pretalx.com/juliacon-2026/speaker/JY8MSY/"}, {"code": "YSNXTV", "name": "Venkatesh Dayanand", "avatar": null, "biography": null, "public_name": "Venkatesh Dayanand", "guid": "18e75068-c6bc-54d3-9aad-c9f452ba84ab", "url": "https://pretalx.com/juliacon-2026/speaker/YSNXTV/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/PAH3UZ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/PAH3UZ/", "attachments": []}, {"guid": "736e9f44-c3d4-5a9b-a08e-4de241042941", "code": "7BTWVN", "id": 91312, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7BTWVN/image_dnyJH6e.webp", "date": "2026-08-14T10:30:00+02:00", "start": "10:30", "end": "2026-08-14T11:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-91312-solving-the-no-language-problem-with-julia", "url": "https://pretalx.com/juliacon-2026/talk/7BTWVN/", "title": "Solving the No Language Problem with Julia", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "When Julia was first introduced, its creators aimed to directly address the \"two language\" problem where coders were forced to switch between a programming language that is performant and one that can be easily understood. In this talk, we'll explore how they accidentally created the perfect language for the emerging \"no language\" problem where code is increasingly not being written by humans at all!", "description": "The practice of programming computers has seen more upheaval in the last year than any time since the introduction of the first \"high level\" languages like Fortran and Lisp. Many of the concerns, however, remain the same: How can we trust what the computer generates? Should a programmer have to understand everything their computer does? How can we be certain the outcome of running a program is deterministic? The battle lines in this dispute have been drawn, but in this talk we will explore how the emerging conflict is built upon a fundamental misunderstanding.\n\nIf, as the famous quote goes, \"programs must be written for people to read, and only incidentally for machines to execute\", then the real problem is not that we're asking LLMs to generate code we aren't reading, but rather that programmers have been writing fundamentally unreadable code for a generation! Languages such as Python and Javascript have been developed for a decade or more with a such a focus on making machines execute that they've failed to develop the necessary features to enhance people's ability to read and understand code.\n\nTogether we will explore the ways in which Julia, with its Lisp heritage, elegant type system, and focus on multiple dispatch, is the perfect language for the coming era of machines increasingly writing code on their own. This talk is not just a sales pitch for using Julia in your next Vibe-coding session, though. We will also look at how existing tools in both the Julia language itself and a handful of available packages can be combined to not only allay the concerns of those who worry about handing the reins over to the machines, but also to make coding Julia hand-in-hand with the machines more powerful than any of the alternatives.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TV9TVY", "name": "Joshua Ballanco", "avatar": "https://pretalx.com/media/avatars/TV9TVY_asWY2wi.webp", "biography": "Dr. Joshua Ballanco has built operating systems with Apple, local news sites\nwith AOL, and served as the Chief Scientist for a world-wide distributed team of\nprogramming and design consultants. He even managed to complete his Ph.D. in\nComputational Evolutionary Dynamics along the way. He currently works remotely\nfrom his home in Greenville, SC where he lives with his beautiful wife and two kids.", "public_name": "Joshua Ballanco", "guid": "fcc15bb5-8f5a-52b4-9204-a38a4187c539", "url": "https://pretalx.com/juliacon-2026/speaker/TV9TVY/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7BTWVN/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7BTWVN/", "attachments": []}, {"guid": "140862af-7253-5fe1-b49d-44ab0ec4da59", "code": "XXLRUE", "id": 92697, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XXLRUE/image_zNkLnNr.webp", "date": "2026-08-14T11:15:00+02:00", "start": "11:15", "end": "2026-08-14T11:30:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92697-from-graphical-block-diagram-to-juliac-executable", "url": "https://pretalx.com/juliacon-2026/talk/XXLRUE/", "title": "From graphical block diagram to juliac executable", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "We present an update on the synchronous programming capabilities in the Dyad modeling language. A synchronous program (discrete-time dynamical system), can now be implemented in a graphical block-diagram editor together with an acausal model of a continuous-time system, simulated, and code generated to a juliac/trim executable or C code. Under the hood, Dyad compiles to ModelingToolkit, which in turn lowers the synchronous program to the new domain-specific language SynchJulia.jl, which in turn generates C code or executable julia code compiled with JuliaC.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "9YRTNA", "name": "Fredrik Bagge Carlson", "avatar": null, "biography": "Control-systems enthusiast at JuliaHub", "public_name": "Fredrik Bagge Carlson", "guid": "c3af1ea3-62f9-5198-be8d-3398055e6578", "url": "https://pretalx.com/juliacon-2026/speaker/9YRTNA/"}, {"code": "9YQMAK", "name": "Tim Besard", "avatar": "https://pretalx.com/media/avatars/9YQMAK_BNCDK1H.webp", "biography": "Tim Besard is a software engineer at JuliaHub, where he leads GPU support and development for the Julia programming language. He holds a Ph.D. in computer science engineering from Ghent University, Belgium, and has been a key contributor to Julia's GPU ecosystem since 2014. Tim maintains several foundational GPU packages including CUDA.jl, GPUArrays.jl, GPUCompiler.jl, and LLVM.jl, which together form the backbone of GPU computing in Julia.", "public_name": "Tim Besard", "guid": "63c32f70-0ed1-5b19-9487-52b2891214e7", "url": "https://pretalx.com/juliacon-2026/speaker/9YQMAK/"}, {"code": "BFBBVP", "name": "Benjamin Chung", "avatar": null, "biography": null, "public_name": "Benjamin Chung", "guid": "d4143a1e-791c-59f9-b7c9-33dd10dda867", "url": "https://pretalx.com/juliacon-2026/speaker/BFBBVP/"}, {"code": "FDYQWE", "name": "Kiran Pamnany", "avatar": null, "biography": null, "public_name": "Kiran Pamnany", "guid": "2a99ffe9-3a7b-5fa0-84cb-3fb8bf03578c", "url": "https://pretalx.com/juliacon-2026/speaker/FDYQWE/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XXLRUE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XXLRUE/", "attachments": []}, {"guid": "8936a938-1993-524a-a110-c34c38e29851", "code": "3NCWH3", "id": 93300, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3NCWH3/image_zJA7vpZ.webp", "date": "2026-08-14T11:30:00+02:00", "start": "11:30", "end": "2026-08-14T12:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93300-makie-jl-highlights-raytracing-compute-graphs-and-complex-recipes", "url": "https://pretalx.com/juliacon-2026/talk/3NCWH3/", "title": "Makie.jl Highlights: Raytracing, Compute Graphs and Complex Recipes", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "We want to show off the most exciting developments in Makie over the past year: a new GPU-enabled ray tracing system for beautiful renderings written entirely in Julia, the compute pipeline refactor that replaced Observables in many internals and fixed the age-old multiple updates conundrum, and the upcoming complex recipe system that could change how most users write plotting functions in Makie.", "description": "Makie is Julia's most comprehensive native plotting ecosystem, with nearly all functionality implemented in Julia itself. In this talk we want to present the highlights of last year's development and take a take a look at what the future might hold for the project.\n\nWith RayMakie and Hikari, Makie has gained a new photorealistic GPU-accelerated ray tracing backend. Compared to the existing RPRMakie backend which was based on the Radeon ProRender framework and never quite lived up to its potential, RayMakie and its dependencies Hikari and Raycore are written in Julia. This gives a much greater degree of flexibility, the opportunity to implement custom tracing passes, and it allows to target platforms that were unreliable or unusable with ProRender. Built as a Julia port of pbrt-v4, Hikari supports physically-based materials, volumetric media, and environment lighting, all running on different types of GPUs via KernelAbstractions.jl. RayMakie opens up a new world of visualizations for Makie users, where light can be used in a more technical way, like visualizing simulated clouds to verify algorithms, or in more artistic ways, rendering 3D data in a way that sparks curiosity. We'll present some beautiful renderings and give interested users ideas how to get started.\n\nSecond, we'll cover v0.24's switch from Observables to compute graphs from ComputePipeline.jl. This fundamental refactor enabled synchronized multi-attribute updates and eliminated redundant intermediate computations. Many users are not yet aware how they can utilize the new features or how they might benefit from a rewrite of old Observables based code. We'll go over some typical patterns and also clarify in what situations Observables are still the way to go.\n\nThird, we want to introduce complex recipes, an upcoming feature that fills in longstanding gaps in Makie's API and should make it easier for most users to write themeable and composable plotting functions. The existing `@recipe` macro was designed as a low-level building block and never intended to handle multi-axis scenarios with automatic legends, colorbars, or even UI elements, which is nevertheless what people have tried to implement with it. The age-old question \"how do I simply add an axis title to my recipe\" might finally get some answers.\n\nWe'll finish off with a brief outlook on the future of the Makie project and where we're headed next.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HT8L8F", "name": "Julius Krumbiegel", "avatar": "https://pretalx.com/media/avatars/37WXQK_CwRgAuj.webp", "biography": "Senior Product Engineer at Pumas AI\nCo-author and co-maintainer of Makie.jl, maintainer of AlgebraOfGraphics.jl. \nCreator of various packages such as Chain.jl or SummaryTables.jl.", "public_name": "Julius Krumbiegel", "guid": "1fc68473-b23b-5406-a0e3-37e01267d0c8", "url": "https://pretalx.com/juliacon-2026/speaker/HT8L8F/"}, {"code": "JHFESW", "name": "Simon Danisch", "avatar": "https://pretalx.com/media/avatars/JSJDXE_fZrtTfz.webp", "biography": "Simon Danisch is the creator of Makie.jl, Bonito.jl, GPUArrays.jl, and BonitoBook.jl. With a background in cognitive science and computer vision, he has spent the last decade building out Julia's visualization, interactive UI, and GPU computing ecosystem.", "public_name": "Simon Danisch", "guid": "9fe14921-7cb7-55ed-abe4-d22427bd2b37", "url": "https://pretalx.com/juliacon-2026/speaker/JHFESW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3NCWH3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3NCWH3/", "attachments": []}, {"guid": "a58266df-8f8f-5799-bbad-280833a24657", "code": "RSTAHL", "id": 92874, "logo": "https://pretalx.com/media/juliacon-2026/submissions/RSTAHL/image_TfFE5JZ.webp", "date": "2026-08-14T12:00:00+02:00", "start": "12:00", "end": "2026-08-14T12:15:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92874-dyadagentbench-an-agent-evaluation-framework-for-dyad-agent", "url": "https://pretalx.com/juliacon-2026/talk/RSTAHL/", "title": "DyadAgentBench: An Agent Evaluation Framework for Dyad Agent", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "DyadAgent is an AI coding assistant for generating and debugging Dyad code across modelling and simulation workflows, enabling engineers to express complex model requirements in natural language. Evaluating the performance of such an agent requires verification of generated simulation results against standards of physical correctness and numerical accuracy. DyadAgentBench is an evaluation infrastructure designed to measure the agent's modelling and simulation capabilities in a systematic and reproducible manner. In this talk, we present the infrastructure and evaluation framework developed for DyadAgent, covering how agent performance is assessed and how the resulting insights are used to benchmark and guide iterative improvements.", "description": "We will demonstrate the infrastructure that enables the evaluation of the agent's physical modelling capabilities and the correctness of its numerical simulations. In this talk, we will also demonstrate how we track incremental updates to the agent and measure their cumulative impact on the agent's capabilities.", "recording_license": "", "do_not_record": false, "persons": [{"code": "F7XTSA", "name": "Ashutosh Bharambe", "avatar": null, "biography": "Software Engineer in Dyad AI team at JuliaHub.", "public_name": "Ashutosh Bharambe", "guid": "08608d5b-e0ae-5b40-81c5-17a9f6727b0d", "url": "https://pretalx.com/juliacon-2026/speaker/F7XTSA/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/RSTAHL/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/RSTAHL/", "attachments": []}, {"guid": "047028e4-f872-5232-8cd2-5be53d67fca0", "code": "AXUZ9V", "id": 92625, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AXUZ9V/image_IlGgZ2K.webp", "date": "2026-08-14T12:15:00+02:00", "start": "12:15", "end": "2026-08-14T12:30:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92625-f16-trim-to-stabilize-workflow", "url": "https://pretalx.com/juliacon-2026/talk/AXUZ9V/", "title": "F16 Trim-to-Stabilize Workflow", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "We aim to illustrate the initialization features of Dyad by trimming the Nonlinear F-16 model dynamics using NLSQ for desired altitude and cruise speed. We will then use the analysis features of Dyad by designing an LQR for longitudinal stabilization under trim and demonstrate a dashboard that enables real time tuning and response behavior visualization.\n\nThe plant model is written in Dyad, a new declarative acausal modeling language. The F-16 model encodes full six-degree-of-freedom rigid-body dynamics: translational and rotational equations of motion with coupled aerodynamic force and moment coefficients, ISA atmosphere density, and Euler angle kinematics. Dyad's `RealInput`/`RealOutput` connectors and `analysis_point` annotations expose the model's control and measurement interfaces for downstream analysis without modifying the physics..\n\nController design uses DyadControlSystems' `LQGAnalysis`, which automatically linearizes the closed-loop Dyad model at the trim operating point and solves the dual Riccati equations. The 12-measurement, 5-control, 8-controlled-output problem yields a 12-state observer-based feedback controller. An interactive GLMakie GUI with a plugin architecture (Gang of Four, Nyquist, step response, pole-zero map) allows real-time tuning of LQR weights and Kalman filter covariances. A GLMakie animation engine renders the 3D trajectory alongside user-selected state and control variable time histories.\n\nThe key contribution is demonstrating that Dyad's declarative syntax unifies what are traditionally separate modeling, analysis, and design stages into a single reproducible project. The same `.dyad` files that define the physics also declare the trim analysis, the LQG synthesis problem, and the simulation scenarios. Julia's composability: ModelingToolkit for symbolic-numeric transformations, DyadControlSystems for control theory and GLMakie for visualization eliminates the toolchain fragmentation typical of aerospace control workflows.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "9YHUWL", "name": "Rajeev Voleti", "avatar": "https://pretalx.com/media/avatars/GCQ8Q3_qw9dXjr.webp", "biography": "Rajeev holds a Ph.D. in Aerospace Engineering with expertise in dynamical systems, controls, and numerical optimization. At JuliaHub, he works on advanced modeling and simulation workflows using Dyad, ModelingToolkit and the broader Julia ecosystem, focusing on large-scale dynamical systems and optimal control.", "public_name": "Rajeev Voleti", "guid": "3158b35e-5488-5636-9c52-59640cb52514", "url": "https://pretalx.com/juliacon-2026/speaker/9YHUWL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AXUZ9V/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AXUZ9V/", "attachments": []}, {"guid": "6f0ddc44-e4fb-5f2f-b836-4e22bb6a066b", "code": "BXSHUH", "id": 92911, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BXSHUH/image_QysFAp1.webp", "date": "2026-08-14T12:30:00+02:00", "start": "12:30", "end": "2026-08-14T13:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92911-data-center-system-modeling-with-dyad", "url": "https://pretalx.com/juliacon-2026/talk/BXSHUH/", "title": "Data Center System Modeling with Dyad", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "The rapid spread of AI into all aspects of society has led to a corresponding surge in data centers to support the exploding computing demand.  Data centers are complex interconnected physical systems with thermal power generation, electrical power conversion, and cooling systems for the compute chips.  The compute load that the data center can effectively deliver is a function of the complex response of these systems including the associated controls for the load dispatch strategy and cascaded controls of the various subsystems.  System modeling with representation of the physical systems and key controls is a critical tool for understanding the physical response and operation of data centers.  \n\nThis talk presents two different uses cases for system modeling in data centers.  The first case focuses on data center operation.  High level transient models in Dyad, Julia, and ModelingToolkit of the data center load, power generation, and electrical system are shown focusing primarily on power demand and supply and high-level control and dispatch.  These models are meant to capture the critical interactions between the total power demand from the compute side of the data center and the required power generation provided by the turbines and generators.  Different operational strategies for turbine scheduling will be demonstrated to assess their impact on system performance and robustness over different load profiles.  These models can address questions regarding the optimal dispatch strategy for the turbines and the impact of different load management strategies on system performance.  Models including the effects of battery energy storage systems are developed to assess the impact of battery sizing and control strategies on the data center operation.  The impacts of various failures can also be simulated with these models. These system models are suitable for simulations over multiple time scales.  Shorter simulations are shown to focus on load planning and the resulting transient power dynamics.  Long time horizon simulations (hours, weeks, months) support operational and economic optimization of data centers with SciML techniques.  \n\nThe second use case focuses on multi-physics models for data center cooling.  Transient models for data center cooling are demonstrated that capture the thermal interactions between the CPU and GPU and the resulting cooling system.  Built from reusable components in Dyad, these models are full physical models that capture the lumped thermal dynamics of the chips and cooling system at the server and rack level.  They can provide temperature predictions at the lumped chip level to support a higher level of fidelity in the system simulations and for load planning.  These models are still suitable for long time horizon simulations as they are lumped but discretized.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "7ECCXX", "name": "John Batteh", "avatar": "https://pretalx.com/media/avatars/3PJBLZ_h9Q7juU.webp", "biography": "I am currently Senior Lead \u2013 Modeling and Simulation at JuliaHub.  With over 25 years of modeling and simulation experience, I enjoy working with customers to develop software solutions to solve complex multi-domain system simulation problems.  Prior to joining JuliaHub, I worked at Ford Motor Company, several engineering consulting companies, and most recently Modelon.", "public_name": "John Batteh", "guid": "83292990-f347-5309-b8b6-a6a62563531c", "url": "https://pretalx.com/juliacon-2026/speaker/7ECCXX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BXSHUH/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BXSHUH/", "attachments": [{"title": "flyer-BXSHUH", "url": "/media/juliacon-2026/submissions/BXSHUH/resources/BXSHUH_UrYwscu.png", "type": "related"}]}, {"guid": "4655f6e8-147e-53bf-9140-c5f25e751aba", "code": "VQWV7R", "id": 92912, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VQWV7R/image_rowMizO.webp", "date": "2026-08-14T14:30:00+02:00", "start": "14:30", "end": "2026-08-14T15:00:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92912-practical-perspectives-on-the-use-of-ai-agents-in-engineering-system-simulation", "url": "https://pretalx.com/juliacon-2026/talk/VQWV7R/", "title": "Practical Perspectives on the Use of AI Agents in Engineering System Simulation", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "There has been a rapid spread of AI agents into all aspects of society.  The exponential increase in capabilities of these agents has led to new ways of working across nearly every profession.  While the adoption of LLMs and agentic workflows has been more common in computer science and software development, the integration of these technologies into engineering system simulation tools is at its infancy.  As an engineer with over 25 years of experience in model-based systems engineering across different engineering domains, I have had no previous experience with agentic workflows in my daily work prior to the last few months.  Speaking with engineering simulation colleagues in different fields, many of them are in the same situation given that their traditional tools did not offer these capabilities but are now ready and interested to explore possibilities of these emerging technologies.\n\nThis talk will offer practical perspectives on agentic workflows focused on engineering system simulation use cases.  Using the Dyad AI agent, these use cases will be explored using Dyad, Julia, and ModelingToolkit.  The focus of this talk is to provide practical perspectives on agentic workflows in model creation, system model assembly, debugging, testing, and simulation and analysis.  The examples will also explore different methods for providing resources to the agent to support the tasks required.  Examples in different engineering domains will be presented. Various workflows are critically evaluated to assess effectiveness and accuracy.  The focus of this talk is to provide practical perspectives demonstrating use cases that work well, those that are still developing or not yet mature (though certainly might be in future versions of the underlying LLMs), and effective prompting techniques based on personal experience.  This talk will be presented via slides documenting the workflows along with live demonstrations within the time constraints of the talk.\n\nThe capabilities of engineering simulation tools are rapidly changing and are fundamentally redefining the human and machine interface.  Engineers need to quickly adapt to utilize new technologies effectively and responsibly.  Though it is certainly impossible to gain a deep understanding of agentic workflows within the duration of a single talk, the hope is that this talk demystifies the use of agentic workflows in system simulation within the Julia ecosystem, inspires critical thinking within the context of an engineer\u2019s unique workflows and simulation needs, and provides practical perspectives that can lead to more effective usage as engineers start to adopt these new technologies.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "7ECCXX", "name": "John Batteh", "avatar": "https://pretalx.com/media/avatars/3PJBLZ_h9Q7juU.webp", "biography": "I am currently Senior Lead \u2013 Modeling and Simulation at JuliaHub.  With over 25 years of modeling and simulation experience, I enjoy working with customers to develop software solutions to solve complex multi-domain system simulation problems.  Prior to joining JuliaHub, I worked at Ford Motor Company, several engineering consulting companies, and most recently Modelon.", "public_name": "John Batteh", "guid": "83292990-f347-5309-b8b6-a6a62563531c", "url": "https://pretalx.com/juliacon-2026/speaker/7ECCXX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VQWV7R/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VQWV7R/", "attachments": []}, {"guid": "fec9a102-bb60-56fd-b04b-8d823b837f4b", "code": "9QTK9V", "id": 92796, "logo": "https://pretalx.com/media/juliacon-2026/submissions/9QTK9V/image_WknoL0y.webp", "date": "2026-08-14T15:00:00+02:00", "start": "15:00", "end": "2026-08-14T15:30:00+02:00", "duration": "00:30", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92796-the-julia-ecosystem-security-advisory-database", "url": "https://pretalx.com/juliacon-2026/talk/9QTK9V/", "title": "The Julia ecosystem security advisory database", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Tracking active security advisories (like CVEs) is a critical requirement for many orgs to use and deploy code... but it can't work without the advisories themselves! The new SecurityAdvisories.jl database enables exactly that for Julia packages and their upstream artifacts (like JLLs). Building such a system in a manner that is both sustainable and manageable for thousands of packages is not trivial; I'll be discussing key factors in how it works and how package maintainers and users alike can make use of it.", "description": "The security advisory landscape is messy, complicated, and difficult to understand. Yet maintaining an ecosystem database in accordance with industry best practice is fundamental to powering security scanners.  This talk will dive into the design, creation, data, automations, and work that powers SecurityAdvisories.jl at a level that is approachable to all Julia programmers.\n\nLike a CVE, the new JLSEC advisory is a mechanism to assign a unique identifier to a vulnerability in a Julia package \u2014\u00a0or relay information about an upstream vulnerability in one of its artifacts (commonly a JLL).  There are many challenges with such a database, and this talk will discuss them.\n\nKey points to be covered will include:\n* The format and best ways to author a JLSEC advisory\n* How JLSEC advisories relate to CVEs and GitHub advisories\n* How artifacts and JLLs are linked with upstream projects and their published CVEs (and the myriad challenges therein)\n* How to get involved", "recording_license": "", "do_not_record": false, "persons": [{"code": "DSVLPL", "name": "Matt Bauman", "avatar": null, "biography": "Matt has been a part of the Julia community for over a decade and is the Director of Sales Engineering at JuliaHub.", "public_name": "Matt Bauman", "guid": "0f0da6a9-d55c-556c-9ae9-e6c72775461a", "url": "https://pretalx.com/juliacon-2026/speaker/DSVLPL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/9QTK9V/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/9QTK9V/", "attachments": []}, {"guid": "650b2307-4c46-547c-8f5c-1fb670b62a1f", "code": "DSWTZG", "id": 91832, "logo": "https://pretalx.com/media/juliacon-2026/submissions/DSWTZG/image_Smop86y.webp", "date": "2026-08-14T15:45:00+02:00", "start": "15:45", "end": "2026-08-14T16:00:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-91832-running-tests-in-parallel-with-paralleltestrunner-jl", "url": "https://pretalx.com/juliacon-2026/talk/DSWTZG/", "title": "Running tests in parallel with ParallelTestRunner.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "In this talk we will introduce [`ParallelTestRunner.jl`](https://github.com/JuliaTesting/ParallelTestRunner.jl), a package for running in parallel the tests of Julia packages, using a very simple infrastructure, well integrated with Julia's `Test`'s standard library. We will showcase `ParallelTestRunner.jl`'s features, which include filtering of test files, and provide practical dos and don'ts when using this package.", "description": "Some packages have extensive and particularly compute-intensive test suites, which run for hours in continuous integration (CI) pipelines, with significant resources usage, and time spent for developers waiting for the results of the tests. This is for example the case for several GPU-related packages (CUDA.jl, AMDGPU.jl, Metal.jl, Enzyme.jl, etc...), and many of them rolled their own independent solutions to parallelise the run of the tests, to address this problem and speed up the development cycle.  This eventually lead to the creation of [`ParallelTestRunner.jl`](https://github.com/JuliaTesting/ParallelTestRunner.jl), a reusable package for running tests of Julia packages in parallel that we'll present in this talk.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ST7KZT", "name": "Mos\u00e8 Giordano", "avatar": "https://pretalx.com/media/avatars/ST7KZT_3IdkVo1.webp", "biography": "Research Software Developer at UCL during the day, binary builder during the night.", "public_name": "Mos\u00e8 Giordano", "guid": "70751b6a-0472-5cfa-81b8-4fd48c25a745", "url": "https://pretalx.com/juliacon-2026/speaker/ST7KZT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/DSWTZG/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/DSWTZG/", "attachments": []}, {"guid": "78a2d067-daf6-5e21-964e-496a35516daf", "code": "VP8XK9", "id": 92671, "logo": "https://pretalx.com/media/juliacon-2026/submissions/VP8XK9/image_i883LxR.webp", "date": "2026-08-14T16:00:00+02:00", "start": "16:00", "end": "2026-08-14T16:15:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92671-implementing-ai-workloads-on-ray-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/VP8XK9/", "title": "Implementing AI Workloads on Ray in Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "This talk discusses our efforts to implement artificial intelligence (AI) workloads on a [**Ray**]([url](https://www.ray.io/#why-ray)) commodity cluster using the Julia programming language. Similar to Apache Spark, Ray is a cluster computing environment for data analytics and AI workloads, mainly in Python. First, we present the configurations and setup steps for a Ray cluster. Next, we discuss the implementation of three distributed clustering algorithms in Ray: **_partition-based_** (a variant of distributed KMeans), **_hierarchical_** (the PACk algorithm), and **_graph_** (filtered graphs with a distributed hierarchical bubble tree). Specifically, we emphasise the integration of Julia and Python within Ray. Finally, we contrast the Ray environment to Apache Spark and highlight the lessons learned (limitations and advantages) throughout this experiment.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "RWL9SQ", "name": "Jos\u00e9 Quenum", "avatar": null, "biography": "Jos\u00e9 Quenum is a Researcher at the Namibia University of Science and Technology (NUST). His interests include Distributed Systems, Artificial Intelligence and Big Data.", "public_name": "Jos\u00e9 Quenum", "guid": "3e42bcb3-8028-590b-b33f-01f51436d3d8", "url": "https://pretalx.com/juliacon-2026/speaker/RWL9SQ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/VP8XK9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/VP8XK9/", "attachments": []}, {"guid": "8e2a1a5e-db30-5aa7-a589-cc481a4f7145", "code": "QUVQMK", "id": 93315, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QUVQMK/image_v6m9bwu.webp", "date": "2026-08-14T16:15:00+02:00", "start": "16:15", "end": "2026-08-14T16:30:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93315-the-making-of-advanced-pluto-vscode-extension", "url": "https://pretalx.com/juliacon-2026/talk/QUVQMK/", "title": "The making of Advanced Pluto - VSCode Extension", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Pluto is a fun-to-use teaching tool. But in order to allow for this seamless experience, it needs to be so much more! It's a **reactive execution engine**, that works over a **robust** remote websocket, **analyses** code, communicates **logs**, **status** and rich MIME **results**! Sounds familiar? This is more than what a notebook does. So why should we limit ourselves in the HTML world when, for example AI Agents can't really -natively- see? In this talk we present the VSCode Extension we made for Pluto, where we wrap Pluto's backend functionalities with a native VSCode Notebook UI.", "description": "We present two fresh takes on the Pluto Project:\n1. a [VSCode extension](https://marketplace.visualstudio.com/items?itemName=juliapluto-pankgeorg.advanced-vscode-extension) with ~700 installs already, built on top of native VSCode Notebook APIs (meaning all VSCode tools, like AI autocomplete, Julia Language Server and LSP already work\n2. A TypeScript library ([Pluto Rainbow](https://www.npmjs.com/package/@plutojl/rainbow)) that natively connects to a Pluto Server, allowing developers to create experiences that leverage Pluto's native features, as first-class citizens in a javascript runtime (browsers or nodejs), on top of which the VSCode extension was also built.\n\nWe will present a set of fresh features that the combination of VSCode Notebook and other native APIs make possible, such as the Pluto Advanced Terminal, Pluto Advanced MCP (for ai agent interaction) and reactivity (sliders and similar UI widgets), all inside VSCode, in a professional and familiar User Interface.\n\nRepository: https://github.com/JuliaPluto/advanced-vscode-extension", "recording_license": "", "do_not_record": false, "persons": [{"code": "CDRVXA", "name": "Panagiotis Georgakopoulos", "avatar": "https://pretalx.com/media/avatars/NDRZYU_39AxTQb.webp", "biography": "Proudly developing Dyad with JuliaHub and improving the julia ecosystem in the meantime, removing one `sleep(1)` at a time. Pluto maintainer. Past lives include software engineer, a business analyst, a consultant, a data entry intern, a waiter and a sailor.", "public_name": "Panagiotis Georgakopoulos", "guid": "f8f896f9-e95e-5023-9935-fa9d6bb7dcea", "url": "https://pretalx.com/juliacon-2026/speaker/CDRVXA/"}, {"code": "NMZBQ8", "name": "Dmitrij Ro\u017ed\u011bstvensk\u00fd", "avatar": null, "biography": "Dmitrij Ro\u017ed\u011bstvensk\u00fd", "public_name": "Dmitrij Ro\u017ed\u011bstvensk\u00fd", "guid": "91fcba90-b6ca-5e9a-9331-167f617d5cac", "url": "https://pretalx.com/juliacon-2026/speaker/NMZBQ8/"}], "links": [{"title": "Pluto \u00b7 Advanced VSCode Extension", "url": "https://github.com/JuliaPluto/advanced-vscode-extension?", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QUVQMK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QUVQMK/", "attachments": []}, {"guid": "bef4f23d-bc93-52c4-bbda-36d29d9896d5", "code": "PMRJ7G", "id": 89367, "logo": "https://pretalx.com/media/juliacon-2026/submissions/PMRJ7G/image_crZrhXr.webp", "date": "2026-08-14T16:30:00+02:00", "start": "16:30", "end": "2026-08-14T16:45:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-89367-testpicker-bringing-modernity-to-julia-testing-in-the-terminal", "url": "https://pretalx.com/juliacon-2026/talk/PMRJ7G/", "title": "TestPicker, bringing modernity to Julia testing in the terminal", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "The Julia standard testing experience can feel quite frustrating.\nNo interface to run a specific test, difference of project environment, keeping the right context etc...\n[`TestPicker`](https://github.com/theogf/TestPicker.jl) provides a simple REPL mode to run any specific testfile or testset with the same conditions as `Pkg.test()` without introducing a new testing framework.\nIt provides on top some nice bonuses like inspection of the results or quick reruns of the same tests.", "description": "The talk will be presenting the [`TestPicker.jl`](https://github.com/theogf/TestPicker.jl) tool and its different features including:\n- Identifying all test files of a package\n- Run any subset using an interactive picker (fzf)\n- Run any `@testset` block\n- Include any relevant context in evaluation\n- Analyze the results with the ability to parse the test results and stacktraces with view on the source code.\nI will also mention the [`TestPickerMCPServer.jl`](https://github.com/theogf/TestPickerMCPServer.jl) package which plugs the test interface of `TestPicker` to AI agents.", "recording_license": "", "do_not_record": false, "persons": [{"code": "EWDVK7", "name": "Th\u00e9o Galy-Fajou", "avatar": "https://pretalx.com/media/avatars/EWDVK7_US6rgxk.webp", "biography": "Bayesian researcher and Julia developer for quite some time now. I am part of the Julia Gaussian Process team and developed all kind of serious tools for statistical analysis and more stupid stuff like WatchJuliaBurn.jl or [DeepFry.jl](https://github.com/JuliaWTF/DeepFry.jl)\nI am currently working for PlantingSpace\n\nhttps://github.com/theogf", "public_name": "Th\u00e9o Galy-Fajou", "guid": "f10d2060-5c35-5bff-a2ab-2f41f5e590d3", "url": "https://pretalx.com/juliacon-2026/speaker/EWDVK7/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/PMRJ7G/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/PMRJ7G/", "attachments": [{"title": "flyer-PMRJ7G", "url": "/media/juliacon-2026/submissions/PMRJ7G/resources/PMRJ7G_ch97eLl.png", "type": "related"}]}, {"guid": "dc9db2a8-5e14-5828-89f4-a02f0c94c539", "code": "XNCC8A", "id": 92405, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XNCC8A/image_SRxuZmW.webp", "date": "2026-08-14T16:45:00+02:00", "start": "16:45", "end": "2026-08-14T17:00:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92405-jumbo-julia-distribution", "url": "https://pretalx.com/juliacon-2026/talk/XNCC8A/", "title": "Jumbo Julia distribution", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Have you ever tried sharing Julia code that computes the Lorenz attractor using DifferentialEquations and visualises it in Makie? I haven't\u2014because the TTFX is unbearable. Users often wait 5+ minutes for compilation during project instantiation, creating an unacceptable first-time experience. What if we could ship precompiled dependencies just like Julia's standard libraries? This is what Jumbo Julia does.\n\nIn this talk, I'll explain how Julia distributions work and what's included in Jumbo Julia, including the tradeoffs imposed by package compatibility constraints that can force older versions. I'll demonstrate common workflows and project instantiations to illustrate both capabilities and limitations. Then I'll show you how to create custom distributions for your own package sets. I'll conclude by speculating on how Julia distributions could solve the PkgImage distribution problem in the short term within Pkg itself.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "KXUWU3", "name": "Janis Erdmanis", "avatar": "https://pretalx.com/media/avatars/BYKXMD_OfhQybm.webp", "biography": "I am a full-stack Julia developer with a Ph.D. in physics from TU Delft, and I enjoy simplifying complex concepts and making the impossible possible. I have thorough experience in Julia, HTTP, QML, cryptographic protocols, and system architectures. Find more about me on [janiserdmanis.org](https://janiserdmanis.org).", "public_name": "Janis Erdmanis", "guid": "fd90434a-7e06-51bf-9c07-5d8cb531a7ab", "url": "https://pretalx.com/juliacon-2026/speaker/KXUWU3/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XNCC8A/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XNCC8A/", "attachments": []}, {"guid": "54a7bba3-b590-53be-a883-193e6d270050", "code": "3GSWV9", "id": 92461, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3GSWV9/image_gscuZox.webp", "date": "2026-08-14T17:00:00+02:00", "start": "17:00", "end": "2026-08-14T17:15:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-92461-julia-gender-inclusive-initiatives-to-create-a-more-welcoming-community", "url": "https://pretalx.com/juliacon-2026/talk/3GSWV9/", "title": "Julia Gender Inclusive: Initiatives to create a more welcoming community", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Julia Gender Inclusive is an initiative dedicated to strengthening gender diversity and inclusion within the Julia community. We are a group of people whose gender is underrepresented in the community and aim to provide a supportive space for all gender minorities in the Julia community. Over the last year, we have worked toward increasing participation in our community by creating and organizing more virtual events, such as regular coffee meetings and hackathons. In this talk, we will share our recent outcomes, lessons learned, and concrete ways the broader Julia community can support and engage with our efforts.", "description": "The objective of our organization is to create space for discussion and community building among people who feel their gender is underrepresented within the Julia community, as well as allies who want to support us. We aim to create a safe and fruitful discussion about gender diversity, increase awareness of our current initiatives, receive input on new actions we can take as Julia Gender Inclusive, and reach out to others who want to get involved.", "recording_license": "", "do_not_record": false, "persons": [{"code": "PW9NQY", "name": "Julia Gender Inclusive", "avatar": null, "biography": "Julia Gender Inclusive is an organization that promotes discussions and spaces for equity and inclusion in the community.", "public_name": "Julia Gender Inclusive", "guid": "1f64c45a-8ddd-52a7-8688-edc86adf8882", "url": "https://pretalx.com/juliacon-2026/speaker/PW9NQY/"}, {"code": "U3WPAA", "name": "Let\u00edcia Madureira", "avatar": "https://pretalx.com/media/avatars/U3WPAA_0m9Kve1.webp", "biography": "Leticia Madureira is a PhD Candidate in Computational Quantum Chemistry at Carnegie Mellon University.", "public_name": "Let\u00edcia Madureira", "guid": "4a5f5b45-89b0-59bc-b591-ee07eb2673a0", "url": "https://pretalx.com/juliacon-2026/speaker/U3WPAA/"}, {"code": "9VEHDZ", "name": "Firoozeh Dastur", "avatar": null, "biography": "---", "public_name": "Firoozeh Dastur", "guid": "f5277a46-7455-5c5f-8031-a20ad58ff962", "url": "https://pretalx.com/juliacon-2026/speaker/9VEHDZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3GSWV9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3GSWV9/", "attachments": []}, {"guid": "32b5b106-ab2f-54bb-8409-44e144000764", "code": "HQDK3J", "id": 93044, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HQDK3J/image_asPua3T.webp", "date": "2026-08-14T17:30:00+02:00", "start": "17:30", "end": "2026-08-14T18:30:00+02:00", "duration": "01:00", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93044-state-of-julia-keynote", "url": "https://pretalx.com/juliacon-2026/talk/HQDK3J/", "title": "State of Julia keynote", "subtitle": "", "track": "General", "type": "Keynote", "language": "en", "abstract": "It is time again to take a look at what new exciting things have happened with Julia; the language, the package ecosystem and the community. Join us as we explore these advancements and celebrate the progress of Julia in in the past year.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "STZAPU", "name": "Jeff Bezanson", "avatar": "https://pretalx.com/media/avatars/JWDSCE_L1lwVeY.webp", "biography": "Co-creator of the Julia language and co-founder of JuliaHub, Inc.", "public_name": "Jeff Bezanson", "guid": "0f4f7e19-c31f-5938-b983-e502161711f2", "url": "https://pretalx.com/juliacon-2026/speaker/STZAPU/"}, {"code": "KSS7SR", "name": "Keno Fischer", "avatar": null, "biography": null, "public_name": "Keno Fischer", "guid": "e94ca854-f560-5368-93b5-7a60c2c6782e", "url": "https://pretalx.com/juliacon-2026/speaker/KSS7SR/"}, {"code": "DNXX8F", "name": "Tim Holy", "avatar": "https://pretalx.com/media/avatars/J9BSUH_jUUc1I6.webp", "biography": "Timothy E. Holy is the Alan A. and Edith L. Wolff Professor of Neuroscience and Biomedical Engineering at Washington University in St. Louis. His lab combines technological innovation with analysis of the rules governing neuronal function and computation. His work on Julia includes contributions to the type system, the array and broadcasting infrastructure, the standard library, and developer tools like the profiler, debugger, Revise, and many others.", "public_name": "Tim Holy", "guid": "baa486d6-5ebc-537a-85d8-349d97deddfc", "url": "https://pretalx.com/juliacon-2026/speaker/DNXX8F/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HQDK3J/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HQDK3J/", "attachments": []}, {"guid": "c1666e7c-a5f6-5c3c-b2d0-dd0c05ca9292", "code": "7VVKFA", "id": 93047, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7VVKFA/image_5nxcQyo.webp", "date": "2026-08-14T18:30:00+02:00", "start": "18:30", "end": "2026-08-14T18:45:00+02:00", "duration": "00:15", "room": "Tent \u2014 RW1", "slug": "juliacon-2026-93047-closing-ceremony", "url": "https://pretalx.com/juliacon-2026/talk/7VVKFA/", "title": "Closing Ceremony", "subtitle": "", "track": "General", "type": "Ceremony", "language": "en", "abstract": "Thank you all for joining us in Mainz! Safe travels, and see you all next year!", "description": "", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7VVKFA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7VVKFA/", "attachments": []}], "Muschel \u2014 N1": [{"guid": "6fcf9924-614f-5e4e-a9f0-c0c2a73135be", "code": "FLU7MM", "id": 92706, "logo": "https://pretalx.com/media/juliacon-2026/submissions/FLU7MM/image_rGnm1uE.webp", "date": "2026-08-14T10:00:00+02:00", "start": "10:00", "end": "2026-08-14T10:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92706-dyad-analyses-designing-engineering-workflows-with-julia", "url": "https://pretalx.com/juliacon-2026/talk/FLU7MM/", "title": "Dyad Analyses: Designing Engineering Workflows with Julia", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "The Dyad platform allows engineers to leverage the power of Julia and SciML via a graphical system modeling environment.  The models created by engineers are translated into Julia and harness Julia's just-in-time compilation along with ModelingToolkit's symbolic manipulation capabilities to provide world class simulation performance.  But what happens when you want to integrate these models into engineering workflows or wish to leverage the symbolic representations in different ways?  In this talk, we'll describe Dyad analyses and how they provide a gateway to the expansive Julia ecosystem.", "description": "Models of engineering systems can be used for many purposes.  The most basic type of analysis is a \"what if\" scenario where you want to determine either the steady state behavior of the system or how the system responds in time to disturbances.  While these steady-state and transient analyses are quite common, they are just the tip of the iceberg when it comes to engineering workflows.\n\nFor example, when engineering products it is often useful to transform a given system model into an FMU for use in software-in-the-loop (SiL) or hardware-in-the-loop (HiL) applications.  But unlike the steady-state and transient analyses, generation of an FMU requires a different set of steps.  But the key is that it can be based on the same model.  By introspecting the model it is possible to reconstitute it as an FMU.\n\nAnother common application is to perform some kind of model optimization or model calibration.  Having access to the symbolic representation of the system allows us to exploit automatic differentiation for efficient computation of gradients.  Again, we wish to pass a model as \"input\" and then perform some transformation or computation which generates engineering results or artifacts for us.  In this case, the result will be a design that has been optimized according to a provided objective and constraints.\n\nThe list of these potential analyses is nearly endless.  Not only that, every company in every industry has their own specific engineering processes so it is impossible to provide a fixed set of analyses that will satisfy everybody.  This is exactly why we need an open ended framework for formulating these analyses.\n\nIn this talk, we will talk about how we leverage the power and immense ecosystem of the Julia programming language to provide a structured way of constructing engineering workflows.  Within this analyses framework, it is possible to create your own workflows described via Dyad models, parameters and even nested workflows and use Julia to map those inputs into engineering artifacts (time series data, reports, diagrams, etc).  All of this works equally well on the desktop as well as in continuous integration (CI) pipelines.\n\nOnce authored, these workflows can be extended and customized using the Dyad graphical user interface.  This provides turnkey access to engineers across the organization without having to be experts in Julia.  Those engineers provide the necessary inputs via the GUI and once the analysis is complete they can review the engineering artifacts in the same GUI.\n\nFinally, it isn't just humans who benefit from these analyses because they are also fully recognized and understood by the Dyad agent.  This means that when you create your own analyses, they become part of the toolbox that our Dyad agent can use in helping you answer your most pressing engineering questions.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9RPXXF", "name": "Michael Tiller", "avatar": "https://pretalx.com/media/avatars/CEQ7XY_Gg1AciJ.webp", "biography": "I am currently the Senior Directory of Product Management for JuliaSim at JuliaHub.  I have built my whole career on my passion for modeling, simulation and software and before coming to work for JuliaHub I had the privilege of working on engineering software at companies like Ford, LMS, Dassault Syst\u00e8mes and Ricardo.", "public_name": "Michael Tiller", "guid": "808c6ad2-58af-51ac-8bd7-435f8db363af", "url": "https://pretalx.com/juliacon-2026/speaker/9RPXXF/"}, {"code": "C7BG78", "name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "avatar": null, "biography": "Software Eng. at JuliaHub & PhD student at University of Bucharest.", "public_name": "Sebastian Miclu\u021ba-C\u00e2mpeanu", "guid": "3e31b822-d61c-5033-b95f-3f20ed7eccbc", "url": "https://pretalx.com/juliacon-2026/speaker/C7BG78/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/FLU7MM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/FLU7MM/", "attachments": []}, {"guid": "a2cc9ac5-8715-534c-aeba-ed11f57bb02a", "code": "E33UYZ", "id": 92140, "logo": "https://pretalx.com/media/juliacon-2026/submissions/E33UYZ/image_1K3AfBE.webp", "date": "2026-08-14T10:15:00+02:00", "start": "10:15", "end": "2026-08-14T10:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92140-designing-the-amazonia-1b-space-mission-with-the-julia-ecosystem", "url": "https://pretalx.com/juliacon-2026/talk/E33UYZ/", "title": "Designing the Amazonia 1B Space Mission with the Julia Ecosystem", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "The Brazilian National Institute for Space Research (INPE) is developing Amazonia 1B, an Earth observation satellite with an enhanced-resolution camera for environmental monitoring. Although its bus is nearly identical to Amazonia 1, launched in 2021, the new payload requires a different orbit, demanding the redesign of several mission aspects. The mission design phase was conducted using the Julia ecosystem, with core algorithms encapsulated in the public packages [SatelliteToolbox.jl](https://github.com/JuliaSpace/SatelliteToolbox.jl) and [SatelliteAnalysis.jl](https://github.com/JuliaSpace/SatelliteAnalysis.jl). This presentation covers how Julia was leveraged for orbit selection, eclipse duration and beta angle computation, and ground station access and gap estimation. The results were incorporated into the Amazonia-1B Critical Design Review (CDR), which occurred in November 2025, and validated by the review board without issues, demonstrating the maturity and reliability of the Julia ecosystem for operational space mission design.", "description": "The Brazilian National Institute for Space Research (INPE) is currently developing a new Earth observation satellite named Amazonia 1B. The spacecraft bus is nearly identical to that of the previous mission, Amazonia 1, which was successfully launched in February 2021. However, Amazonia 1B utilizes a distinct payload, a camera with significantly enhanced spatial resolution, aimed at improving environmental monitoring capabilities for Brazil. Consequently, the satellite operates in a different orbit, necessitating the redesign of several critical aspects of the mission.\n\nThe mission design phase of Amazonia 1B was carried out with the support of a comprehensive set of packages within the Julia programming ecosystem, enabling the execution of numerous essential engineering tasks. The core algorithms developed and employed throughout this process are encapsulated within the publicly available packages [SatelliteToolbox.jl](https://github.com/JuliaSpace/SatelliteToolbox.jl) and [SatelliteAnalysis.jl](https://github.com/JuliaSpace/SatelliteAnalysis.jl), both of which provide a robust framework for satellite mission analysis.\n\nThe mission design of a new satellite requires a systematic approach encompassing several interdependent steps. In this presentation, we will delve into the intricacies of orbit design, a foundational aspect of the satellite's overall configuration. The selection of an appropriate orbit constitutes the initial and most fundamental step in the process, as it directly influences the functionality and performance of various satellite subsystems, including power generation, thermal control, and communication links. Within this context, we will demonstrate how the Julia ecosystem was leveraged to facilitate the efficient selection of an optimal orbit, compute the satellite's eclipse duration and beta angle profile, and estimate the access windows and revisit gaps for the designated ground stations.\n\nThe results derived from these analyses were subsequently incorporated into the Amazonia 1B Critical Design Review (CDR), which occurred in November 2025. All findings underwent thorough validation by the review board, which did not identify any issues with the methodology or the results. This outcome demonstrates the maturity and reliability of the Julia ecosystem, confirming its suitability for application in the design phase of operational space missions.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZZE3ZN", "name": "Ronan Arraes Jardim Chagas", "avatar": "https://pretalx.com/media/avatars/8AFQPZ_7x1i0JM.webp", "biography": "Since 2013, Ronan Arraes Jardim Chagas has been with the Space Systems Division of the Brazilian National Institute for Space Research (INPE). As his most significant accomplishment, he was the Mission Architect and the responsible technician of the attitude and orbit control subsystem (AOCS) of the Brazilian Satellite Amazonia 1, successfully launched in February 2021.\n\nHe has been working with Control Systems and Signal Processing for 17 years. During this time, he was involved in many projects related to those areas. He successfully embedded Kalman filters (Extended and Unscented) in many autonomous systems and developed state-of-art signal processing algorithms to perform estimation in distributed sensor networks.\n\nHe conducts several research projects at INPE. Those projects include artificial intelligence and advanced control techniques applied to the AOCS, space mission design optimization, advanced signal processing, and orbit analysis.\n\nHe is also a Julia language enthusiast. He has used it daily since 2013 to perform many activities related to his work. As his most significant project with this language, he developed a complete AOCS simulator to test and verify this subsystem. The simulation achieved outstanding performance and accuracy, given the orbital data collected from the satellite Amazonia 1.\n\nHe is the creator and maintainer of some important packages of the Julia language ecosystem: ReferenceFrameRotations.jl, SatelliteToolbox.jl, SatelliteAnalysis.jl, PrettyTables.jl, and others.", "public_name": "Ronan Arraes Jardim Chagas", "guid": "b5d7bf2c-16c0-5e02-9b23-d2209711a3ab", "url": "https://pretalx.com/juliacon-2026/speaker/ZZE3ZN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/E33UYZ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/E33UYZ/", "attachments": [{"title": "flyer-E33UYZ", "url": "/media/juliacon-2026/submissions/E33UYZ/resources/E33UYZ_QaHVDYm.png", "type": "related"}]}, {"guid": "5ca0239d-7f57-551d-abb4-a3e08c79168e", "code": "ZJ3D8Q", "id": 92454, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZJ3D8Q/image_hkNNqmZ.webp", "date": "2026-08-14T10:30:00+02:00", "start": "10:30", "end": "2026-08-14T10:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92454-embedding-julia-on-petoi-bittle-and-raspberry-pi", "url": "https://pretalx.com/juliacon-2026/talk/ZJ3D8Q/", "title": "Embedding Julia on Petoi Bittle and Raspberry PI", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "This talks will demonstrate Julia in an embedding setting with Petoi Bittle robots. We show how to run a state estimation using adaptive kalman filter implemented with RxInfer.jl and communicate with Petoi Bittle Dog robot using PetoiBittle.jl, all of this autonomously on Raspberry PI.", "description": "Integrating software and hardware can be a huge pain. Incompatibilities, different communication protocols create a bottleneck for researchers.\n\nIn this talk we will show listeners how Julia can be integrated with actual hardware. We show how we embed Julia on Raspberry PI and, as an example, we will be using state-of-the-art state estimation software RxInfer.jl. For communication we use the open-source library PetoiBittle.jl and as a hardware we use an open source programmable robotics platform called Petoi and their open-source high-performance Robot Dog called Bittle.\n\nThis work has been conducted together with Wouter Kouw, assistant professor from BIASlab https://biaslab.github.io/author/wouter-kouw/\n\nLinks \n- https://rxinfer.com/ - RxInfer website\n- https://www.petoi.com/ - Petoi robots website\n- https://github.com/ReactiveBayes/RxInfer.jl - RxInfer official GitHub repository\n- https://github.com/bvdmitri/PetoiBittle.jl - Petoi Bittle communication library", "recording_license": "", "do_not_record": false, "persons": [{"code": "BM9FUU", "name": "Dmitry Bagaev", "avatar": "https://pretalx.com/media/avatars/F87RD7_qGgTgPZ.webp", "biography": "Senior software engineer and PhD scientist with a strong mathematical foundation and expertise in software development, machine learning and data science. Brings a unique blend of academic rigor and hands-on industry experience, with a proven track record of leading technical teams, architecting complex systems, and translating cutting-edge research into practical applications.", "public_name": "Dmitry Bagaev", "guid": "6edebcc1-0356-50cc-8ccf-9016fe9e76da", "url": "https://pretalx.com/juliacon-2026/speaker/BM9FUU/"}], "links": [{"title": "RxInfer official GitHub repository", "url": "https://github.com/ReactiveBayes/RxInfer.jl", "type": "related"}, {"title": "Petoi Bittle communication library", "url": "https://github.com/bvdmitri/PetoiBittle.jl", "type": "related"}, {"title": "RxInfer website", "url": "https://rxinfer.com/", "type": "related"}, {"title": "Petoi robots website", "url": "https://www.petoi.com/", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZJ3D8Q/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZJ3D8Q/", "attachments": [{"title": "flyer-ZJ3D8Q", "url": "/media/juliacon-2026/submissions/ZJ3D8Q/resources/ZJ3D8Q_ZDS6jBd.png", "type": "related"}]}, {"guid": "2baf222f-08dc-58a1-ab56-9b24e7447946", "code": "T79F7F", "id": 92898, "logo": "https://pretalx.com/media/juliacon-2026/submissions/T79F7F/image_Z2NfHpC.webp", "date": "2026-08-14T10:45:00+02:00", "start": "10:45", "end": "2026-08-14T11:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92898-developing-a-custom-fem-solver-for-the-heat-problem-in-the-laser-processing-of-metals", "url": "https://pretalx.com/juliacon-2026/talk/T79F7F/", "title": "Developing a custom FEM solver for the heat problem in the laser processing of metals", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "Many modern manufacturing techniques rely heavily on lasers and more often than not, the behavior of heat in these systems plays a key role in determining whether a finished product is of acceptable quality.  In this talk, I present how it's possible to construct a bespoke finite-element method solver for the heat equation, starting from first principles and building on the work of _Ferrite.jl_ and _DifferentialEquations.jl_. The solver is then validated against experimental results and I show how access to the inner workings facilitates the extension of the code base to tackle related problems such as computing surface hardness after laser processing.", "description": "In doing research on laser-based manufacturing techniques, particularly more recent ones such as directed energy deposition, when the cost of materials and time meets with the large parameter space of the problem, simulation becomes a necessity. \n\nWhile commercial solutions do exist, they typically abstract away the inner workings of the physics taking place. In consequence, building a custom solver can be a great way to understand both the physics and the computer science involved.\n\nStarting from first principles, I show how I implemented the transient heat equation into the _Ferrite.jl_ framework and how _DifferentialEquations.jl_ can be used to efficiently solve the ordinary differential equations required for temperature, while taking into consideration the non-linear boundary conditions including radiation. The solver is then validated against experimental results obtained in the laboratory. \n\nFinally, I show how exposing the inner workings of the solver makes the implementation of additional functionality, such as computing the hardness of the processed material much easier.", "recording_license": "", "do_not_record": false, "persons": [{"code": "99WPSY", "name": "Petru-Vlad TOMA", "avatar": null, "biography": "- PhD student at the Faculty of Physics of the University of Bucharest.\n- Research assistant at the Center of Advanced Laser Technologies (CETAL) of the National Institute for Laser Plasma and Radiation (Romania).", "public_name": "Petru-Vlad TOMA", "guid": "dc725c87-5d69-53ad-931c-806d585d6fa1", "url": "https://pretalx.com/juliacon-2026/speaker/99WPSY/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/T79F7F/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/T79F7F/", "attachments": []}, {"guid": "cddfbea6-f169-543c-a303-f5dc8035008f", "code": "PG9DLH", "id": 92907, "logo": "https://pretalx.com/media/juliacon-2026/submissions/PG9DLH/image_UJcQ8L2.webp", "date": "2026-08-14T11:00:00+02:00", "start": "11:00", "end": "2026-08-14T11:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92907-laminar-workflow-turbulent-performance-xcalibre-jl-a-modern-cfd-framework-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/PG9DLH/", "title": "Laminar Workflow, Turbulent Performance: XCALibre.jl - A Modern CFD Framework in Julia", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "Computational Fluid Dynamics (CFD) has traditionally suffered from the \"Two-Language Problem\": researchers develop new physical models in high-level environments like Python or MATLAB, only to face an extensive rewrite in C++ or Fortran for production-scale runs. This \"viscous\" development cycle slows down innovation across the aerospace, automotive, and energy industries. In this talk, we present XCALibre.jl: a new Julia package designed to eliminate this friction. XCALibre.jl provides a \"Laminar\" workflow, allowing for rapid prototyping of complex Multiphysics, and a \"Turbulent\" runtime performance with native GPU acceleration. XCALibre.jl handles industry-relevant geometries and complex physical solvers, proving that in the Julia ecosystem, developer productivity and fast simulation runtime are not mutually exclusive.", "description": "XCALibre.jl achieves \"Turbulent\" performance by utilising Julia\u2019s unique language features and composable ecosystem. The proposed structure for this talk is as follows (approximately 5 minutes per section):\n\n\u2022\t**Motivation**: Why XCALibre.jl? We discuss the gap this framework fills and how it complements existing Julia CFD packages like Trixi.jl, WaterLily.jl, and Oceananigans.jl, highlighting how XCALibre.jl contributes to this ecosystem.\n\u2022\t**Development History**: Released just over a year ago, XCALibre.jl has matured with surprising speed. We provide a brief timeline of its evolution, highlighting a key success story: much of the core development was driven by undergraduate and master\u2019s students, a testament to the \"Laminar\" ease of prototyping in Julia.\n\u2022\t**Technical Architecture**: We dive into the dependencies that underpin XCALibre.jl, specifically KernelAbstractions.jl and the broader GPU ecosystem. We explore how we use (and perhaps \"abuse\") Julia\u2019s type system, multiple dispatch, macros, and generated functions to achieve high-performance and to build our Domain Specific Language (DSL) for defining new physics.\n\u2022\t**Features & Benchmarks**: A showcase of current capabilities, performance benchmarks against legacy solvers, and CFD visualisations.\n\u2022\t**Vision & Future Plans**: An honest look at current limitations, ongoing work, and our roadmap for industrial-scale simulation.\n\n**Target Audience**: This talk is for engineers, physicists, and HPC enthusiasts. Attendees will learn how Julia can modernize legacy engineering workflows without sacrificing efficiency. We hope to inspire developers in other scientific domains by sharing our experience and to foster new collaborations within the Julia CFD community.", "recording_license": "", "do_not_record": false, "persons": [{"code": "MSQGSJ", "name": "Humberto Medina", "avatar": null, "biography": "Associate Professor in Aerodynamics and loves coding in Julia and advancing CFD simulation", "public_name": "Humberto Medina", "guid": "a25bf6a8-c651-526b-9e63-b932bd65d2f1", "url": "https://pretalx.com/juliacon-2026/speaker/MSQGSJ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/PG9DLH/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/PG9DLH/", "attachments": [{"title": "flyer-PG9DLH", "url": "/media/juliacon-2026/submissions/PG9DLH/resources/PG9DLH_74pN9cN.png", "type": "related"}]}, {"guid": "383bcbcd-34b3-5954-b60c-5cdbd0320e1e", "code": "CHQSVY", "id": 92676, "logo": "https://pretalx.com/media/juliacon-2026/submissions/CHQSVY/image_Na5aBTd.webp", "date": "2026-08-14T11:15:00+02:00", "start": "11:15", "end": "2026-08-14T11:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92676-mlthermoproperties-jl-hybrid-models-for-thermodynamic-property-prediction-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/CHQSVY/", "title": "MLThermoProperties.jl: Hybrid Models for Thermodynamic Property Prediction in Julia", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "We present MLThermoProperties.jl, a Julia package that provides a variety of state-of-the-art thermodynamic models that combine modern machine learning methods with physical knowledge. These hybrid models obey hard physical constraints while being more accurate and applicable to a wider scope of substances than established models. MLThermoProperties.jl is built upon the Clapeyron.jl package, leveraging its rich thermodynamic solver ecosystem. The MLThermoProperties.jl models significantly improve molecular property prediction in various applications in science and engineering, e.g., chemical process engineering. Exemplary applications will be demonstrated in the talk by coupling MLThermoProperties.jl with Julia's rich ecosystem for scientific modelling and simulation.", "description": "Knowledge of thermodynamic properties of fluids is crucial in many fields of engineering and science, e.g., for developing new chemical and biotechnological processes, optimizing heat pumps, or designing carbon capture and storage technologies. However, experimental data on thermodynamic properties are notoriously scarce due to the high cost and effort of measurements, making reliable prediction models indispensable. In recent years, machine learning (ML) has emerged as a particularly promising approach to thermodynamic modeling [1].\n\nIn our research group, many state-of-the-art thermodynamic ML models are developed under the umbrella of MLPROP, a collection of open-source ML models for molecular property prediction. These models, despite being neural networks at their core, are thermodynamically consistent, i.e., they obey hard physical constraints. This consistency is either achieved by an appropriate architecture or by exploiting existing thermodynamic models to form new hybrid models. Based only on the SMILES code of a substance \u2013 a textual representation of the molecular structure \u2013 the MLPROP models predict multiple thermodynamic properties, outperforming established models in both accuracy and scope [2]. Among the covered properties are phase equilibria of pure substances and mixtures [2], which are central to chemical process design, as well as transport properties such as diffusion coefficients [3] that govern molecular mass transfer. The models utilize different ML architectures, including the chemical language model ChemBERTa [4] and molecular graph neural networks.\n\nIn Julia, a rich ecosystem for thermodynamics and chemical engineering already exists. Clapeyron.jl [5], a mature and comprehensive thermodynamic package, is a central part of this ecosystem. It combines a large number of thermodynamic models (including equations of state (EoS) and Gibbs excess energy models) with advanced and efficient solvers. Leveraging Julia\u2019s excellent extensibility, several packages extend Clapeyron.jl, e.g., EntropyScaling.jl for modeling transport properties, or Langmuir.jl for modeling adsorption. \n\nThis talk introduces MLThermoProperties.jl, which provides Julia implementations of the thermodynamic models from MLPROP, based on Clapeyron.jl \u2013 enabling the prediction of thermodynamic properties for any substance in Julia. MLThermoProperties.jl not only provides implementations of existing models, but also serves as a central anchor point for the development of new models. Due to the excellent extensibility of Julia packages, MLThermoProperties.jl substantially broadens the\ncapabilities for modeling chemical processes, based on state-of-the-art molecular property prediction. The individual models are presented and their integration into the existing thermodynamic ecosystem is explained. Additionally, illustrative applications of these models in chemical process simulations are showcased using packages from the wider scientific-modeling ecosystem in Julia, particularly from the SciML organization, e.g., ModelingToolkit.jl and DifferentialEquations.jl.\n\n**References**\n[1] H. Hasse, S. Schmitt, and F. Jirasek: Artificial Intelligence in Thermodynamics: Hybrid Modeling of Thermophysical Properties of Fluids, Current Opinion in Chemical Engineering 51 (2026) 101236, DOI: https://doi.org/10.1016/j.coche.2026.101236.\n[2] T. Specht, M. Nagda, S. Fellenz, S. Mandt, H. Hasse, and F. Jirasek: HANNA: Hard-Constraint Neural Network for Consistent Activity Coefficient Prediction, Chemical Science (2024), DOI: https://doi.org/10.1039/D4SC05115G.\n[3] J. Wagner, Z. Romero, K. M\u00fcnnemann, S. Schmitt, T. Specht, H. Hasse, and F. Jirasek: Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids, to be published (2026).\n[4] W. Ahmad, E. Simon, S. Chithrananda, G. Grand, and B. Ramsundar: ChemBERTa-2: Towards Chemical Foundation Models, arXiv, DOI: https://doi.org/10.48550/arXiv.2209.01712.\n[5] P. J. Walker, H.-W. Yew, and A. Riedemann: Clapeyron.jl: An Extensible, Open-Source Fluid Thermodynamics Toolkit, Industrial & Engineering Chemistry Research 61 (2022) 7130\u20137153, DOI: https://doi.org/10.1021/acs.iecr.2c00326.", "recording_license": "", "do_not_record": false, "persons": [{"code": "W3X3MW", "name": "Sebastian Schmitt", "avatar": null, "biography": "I'm a postdoctoral researcher at Laboratory of Engineering Thermodynamics (LTD) of the University of Kaiserslautern (RPTU), Germany. My work focuses on hybrid thermodynamic models that combine physical knowledge with machine learning.\n\nSee [here](https://mv.rptu.de/fgs/ltd/lehrstuhl/mitarbeiter/sebastian-schmitt) for more.", "public_name": "Sebastian Schmitt", "guid": "e14b160f-32f6-5cd2-ae48-a4d646faed54", "url": "https://pretalx.com/juliacon-2026/speaker/W3X3MW/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/CHQSVY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/CHQSVY/", "attachments": []}, {"guid": "f0bfaa20-1a9c-5660-8776-1cf29b14c51a", "code": "WYZSFH", "id": 92071, "logo": "https://pretalx.com/media/juliacon-2026/submissions/WYZSFH/image_2ffShCa.webp", "date": "2026-08-14T11:30:00+02:00", "start": "11:30", "end": "2026-08-14T11:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92071-railtoolkit-building-an-open-ecosystem-from-trainruns-jl", "url": "https://pretalx.com/juliacon-2026/talk/WYZSFH/", "title": "RailToolKit: Building an Open Ecosystem from TrainRuns.jl", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "RailToolKit is an emerging ecosystem for open railway research. TrainRuns.jl is our showcase package for running time calculations. Today, railway studies often rely on siloed spreadsheets, proprietary tools, and non-reproducible workflows. We have built TrainRuns.jl as the first component. Now we are designing interfaces to enable data exchange and reproducible workflows across packages. Share your workflows and use cases to help shape this ecosystem!", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "KYS8QF", "name": "Martin Scheidt", "avatar": "https://pretalx.com/media/avatars/HWDKBR_9wpEYmC.webp", "biography": "Martin Scheidt recently joined Darmstadt University of Applied Sciences as a professor. He studied transport engineering at TU Dresden and TU Braunschweig, where he subsequently obtained his doctorate at the Institute for Railway Engineering and Transport Safety. His research focuses on railway operations planning, in particular timetable design and railway infrastructure modelling.", "public_name": "Martin Scheidt", "guid": "ad410e48-ed4f-5b61-bbb7-e58eca865658", "url": "https://pretalx.com/juliacon-2026/speaker/KYS8QF/"}, {"code": "FQC3LZ", "name": "Gregor Wehrle", "avatar": null, "biography": null, "public_name": "Gregor Wehrle", "guid": "78f93b54-b15c-55eb-865a-b5fb380bceee", "url": "https://pretalx.com/juliacon-2026/speaker/FQC3LZ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/WYZSFH/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/WYZSFH/", "attachments": []}, {"guid": "d556c4cf-77dd-5958-9606-2ddabe802f96", "code": "Z38XCK", "id": 92710, "logo": "https://pretalx.com/media/juliacon-2026/submissions/Z38XCK/image_ARL3oXy.webp", "date": "2026-08-14T11:45:00+02:00", "start": "11:45", "end": "2026-08-14T12:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92710-real-time-gnss-positioning-with-juliagnss-from-sdr-signals-to-your-location", "url": "https://pretalx.com/juliacon-2026/talk/Z38XCK/", "title": "Real-Time GNSS Positioning with JuliaGNSS: From SDR Signals to Your Location", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "JuliaGNSS is an open-source software stack for processing Global Navigation Satellite System signals entirely in Julia. With all core packages now at version 1.0, the ecosystem has reached production readiness. This talk demonstrates real-time GNSS positioning by connecting JuliaGNSS to a Software Defined Radio, acquiring satellite signals, and computing position and time live. I show how Julia's performance and composability enable a complete GNSS receiver that rivals traditional C/C++ implementations.", "description": "This talk demonstrates that Julia can power a real-time GNSS receiver. Using recorded SDR captures, I walk through the complete signal processing chain\u2014from raw radio samples to a position fix\u2014showing acquisition, tracking, decoding, and positioning as they happen in real time.             \n                                                                                                                    \nThe talk centers on a demonstration of the full receiver pipeline:                                                                                \n\n  1. SDR signal capture \u2013 Raw I/Q samples from GPS satellites received by commodity hardware                                          \n  2. Satellite acquisition \u2013 The receiver detecting visible GPS satellites            \n  3. Signal tracking \u2013 Correlation loops locking onto and following satellite signals                      \n  4. Position computation \u2013 Calculating position and time from the tracked signals                         \n\nAttendees will see the complete journey from radio waves to coordinates, processed entirely in Julia.                                             \n\nTalk Structure (12 minutes)                                                                                                                       \n  - Introduction (6 min) \u2013 Brief context: what GNSS signals are and why real-time processing is challenging, walking through the receiver processing real SDR data: acquisition, tracking, and position fix\n  - Demonstration (4 min) \u2013 Demo (Either from live signals or recorded signals)\n  - Ecosystem & Next Steps (2 min) \u2013 The 1.0 milestone, package overview, and how to get started with JuliaGNSS\n\nLive Demo\n\nIf the venue permits line-of-sight to GPS satellites (e.g., near a window), I will run a fully live demonstration\u2014connecting an SDR on stage and computing our position at the conference in real time. GPS signals are weak and do not penetrate buildings reliably, so recorded captures serve as the primary demonstration with live acquisition as a bonus if conditions allow.                                                                  \n                                                                                                                                       \nGNSS receivers are traditionally implemented in C/C++ or as dedicated hardware. A working real-time receiver in Julia demonstrates that the language is ready for demanding signal processing applications. Seeing satellites being acquired and tracked provides concrete evidence of Julia's performance in action.\n\nThis talk expands Julia's presence in SDR and embedded signal processing\u2014domains where C/C++ still dominates. The JuliaGNSS ecosystem (GNSSSignals.jl, Acquisition.jl, Tracking.jl, GNSSDecoder.jl, PositionVelocityTime.jl, and GNSSReceiver.jl) is open source and available for researchers, educators, and hobbyists to use and extend.", "recording_license": "", "do_not_record": false, "persons": [{"code": "AMZWAL", "name": "S\u00f6ren Sch\u00f6nbrod", "avatar": null, "biography": "Soeren Schoenbrod received his M.Sc. in electrical engineering from RWTH Aachen University in 2015. His research interests include multi antenna GNSS receivers, attitude and calibration estimation, robust interference and spoofing detection and mitigation.", "public_name": "S\u00f6ren Sch\u00f6nbrod", "guid": "2be3cbd1-065c-52d7-82a4-099ef915f826", "url": "https://pretalx.com/juliacon-2026/speaker/AMZWAL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/Z38XCK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/Z38XCK/", "attachments": [{"title": "flyer-Z38XCK", "url": "/media/juliacon-2026/submissions/Z38XCK/resources/Z38XCK_edunGYp.png", "type": "related"}]}, {"guid": "5bcc604a-92b4-5d02-bab1-60ddc8ddd82a", "code": "YWMTMA", "id": 92896, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YWMTMA/image_dweUWeC.webp", "date": "2026-08-14T12:00:00+02:00", "start": "12:00", "end": "2026-08-14T12:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92896-reliability-analysis-of-underground-hydrogen-storage-under-limited-data", "url": "https://pretalx.com/juliacon-2026/talk/YWMTMA/", "title": "Reliability Analysis of Underground Hydrogen Storage Under Limited Data", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "It is estimated that by 2030 Germany will produce around 20 _TWh_ of green hydrogen per year, highlighting the need for suitable storage. One possible solution is storing the hydrogen in porous underground media. In this work we analyse the reliability of such a potential storage site.\n\nReliability analyses typically require tens to hundreds of thousands of model evaluations for accurate results, especially when imprecise probabilities are involved due to limited data availability and input variables are modelled as intervals or probability boxes. The true model in this study has an associated runtime of around three days rendering a direct reliability analysis virtually impossible.\n\nWe use Julia to train an accurate surrogate model on which we are then able to perform our analyses. Propagating the imprecise input quantities through the surrogate model we obtain bounds on the probability of failure of the system. All algorithms used in this study are implemented in the _UncertaintyQuantification_ package.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"code": "73K8UV", "name": "Jasper Behrensdorf", "avatar": "https://pretalx.com/media/avatars/9L3JQF_bhNvqj3.webp", "biography": "Postdoctoral researcher at the *Institute for Risk and Reliability* at the *Leibniz University Hannover*, Germany.", "public_name": "Jasper Behrensdorf", "guid": "e1adeb40-c405-5c2f-8611-11e75a03bb2e", "url": "https://pretalx.com/juliacon-2026/speaker/73K8UV/"}, {"code": "MJKWXU", "name": "Gergely Schmidt", "avatar": null, "biography": null, "public_name": "Gergely Schmidt", "guid": "d848efd4-6b53-5181-beba-b627eb112060", "url": "https://pretalx.com/juliacon-2026/speaker/MJKWXU/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YWMTMA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YWMTMA/", "attachments": []}, {"guid": "b78f4643-4840-5655-a81c-5fcd6fa565e4", "code": "QZGFZP", "id": 92578, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QZGFZP/image_KIXW6ZK.webp", "date": "2026-08-14T12:15:00+02:00", "start": "12:15", "end": "2026-08-14T12:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92578-simulation-and-modelling-of-persistent-high-altitude-solar-aircraft-with-julia", "url": "https://pretalx.com/juliacon-2026/talk/QZGFZP/", "title": "Simulation and Modelling of Persistent High Altitude Solar Aircraft with Julia", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "The talk details how Julia is used for the simulation and modelling of PHASA-35, an unmanned persistent stratospheric solar-powered aircraft developed by Prismatic Ltd, a subsidiary of BAE Systems plc. PHASA-35 can stay airborne year-round to provide communications services, wildfire detection, or ISR.\nJulia is used both in standalone simulation tools as well as in FMUs, which are built using JuliaC to perform system-level Software-in-the-Loop simulations.", "description": "The following aspects will be covered in the talk:\n- A brief overview of PHASA-35 and its capabilities.\n- Use of Julia in standalone PHASA-35 performance calculation tools.\n- Building of FMUs using JuliaC, which are used to perform whole-aircraft simulations.\n- Speeding up of simulations with JuliaGPU packages.\n- Continuous Integration with Julia.\n- General comments on the development experience with Julia.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZE3ALT", "name": "Nathanael West", "avatar": "https://pretalx.com/media/avatars/TC99ED_g88fkqD.webp", "biography": "Nat obtained an MEng in Aerospace, Aerothermal, and Mechanical Engineering from the University of Cambridge. He has since worked on a range of aircraft that vary widely in size and operational altitude (fixed-wing drones, airships, and PHASA-35). He is passionate about software development best practices and is a pedant when it comes to commit message guidelines. Nat holds a sailplane pilot\u2019s license, which he sometimes puts to use in his free time.", "public_name": "Nathanael West", "guid": "41490a43-6044-5650-a207-68a1fe55a3f8", "url": "https://pretalx.com/juliacon-2026/speaker/ZE3ALT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QZGFZP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QZGFZP/", "attachments": [{"title": "flyer-QZGFZP", "url": "/media/juliacon-2026/submissions/QZGFZP/resources/QZGFZP_21zJSJU.png", "type": "related"}]}, {"guid": "47e0603c-ea9c-54b9-b868-e2b1f9467875", "code": "7YNPXC", "id": 92902, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7YNPXC/image_kV2j2P0.webp", "date": "2026-08-14T12:30:00+02:00", "start": "12:30", "end": "2026-08-14T12:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92902-statfem-euclid-jl-data-assimilation-and-constitutive-model-discovery", "url": "https://pretalx.com/juliacon-2026/talk/7YNPXC/", "title": "statFEM-EUCLID.jl: Data assimilation and constitutive model discovery", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "A common task in solid mechanics is to select and calibrate a constitutive model for a specific material. Constitutive model discovery seeks to automate this task. The framework statFEM-EUCLID.jl provides an unsupervised approach for constitutive model discovery from sparse and noisy measurements and global reaction forces. Using UMBridge.jl, the framework treats the finite element solution as a black box, such that any finite element solver can, in principle, be linked to it.", "description": "Starting point is a specific problem setting, e.g. a plate with a hole under tension. For this setting, a set of displacement and/or strain observations Y and global reaction forces must be available. The discovery loop starts with an initial guess for a constitutive model and its parameters, and goes as follows:\n\n1) Sample traction forces from a distribution that reflects the uncertainties in reaction forces\n2) Query the FEM black box through UMBridge and propagate uncertain traction onto forecasted displacements u_f using non-intrusive polynomial chaos expansion\n3) Check if the distance between measurements Y and forecasted displacements is sufficiently small \n3a) If yes, we have converged and the current material model is the best fit\n3b) If not, continue\n4) Bayesian updating of forecasted displacements u_f from given observations Y yields assimilated displacements u_a \n5) Taking the mean of u_a, discover a new material model using the virtual field method VFM or EUCLID\n\nIn the talk, the basic ideas of the statFEM-EUCLID framework, design decisions of the package and the flexibility in using different FEM solvers through UMBridge will be addressed.", "recording_license": "", "do_not_record": false, "persons": [{"code": "WPSYLQ", "name": "Jan Philipp Thiele", "avatar": null, "biography": "Jan Philipp Thiele is a Research Software Engineer in the digital science support lab of TU Braunschweig.", "public_name": "Jan Philipp Thiele", "guid": "82a7ef31-27b5-5023-ba7d-56ee33a4c6d4", "url": "https://pretalx.com/juliacon-2026/speaker/WPSYLQ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7YNPXC/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7YNPXC/", "attachments": [{"title": "flyer-7YNPXC", "url": "/media/juliacon-2026/submissions/7YNPXC/resources/7YNPXC_jXVV0XY.png", "type": "related"}]}, {"guid": "46348c86-933e-5c08-b778-15183ceec8f1", "code": "XD88T7", "id": 93201, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XD88T7/image_9EpfaB5.webp", "date": "2026-08-14T12:45:00+02:00", "start": "12:45", "end": "2026-08-14T13:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93201-trishellfiniteelement-jl-a-mindlin-triangular-shell-finite-element-formulation-for-use-with-ferrite-jl", "url": "https://pretalx.com/juliacon-2026/talk/XD88T7/", "title": "TriShellFiniteElement.jl: A Mindlin triangular shell finite element formulation for use with Ferrite.jl", "subtitle": "", "track": "Engineering with Julia", "type": "Short talk", "language": "en", "abstract": "A triangular shell finite element formulation is implemented in for use with the popular open-source finite element software package Ferrite.jl.  Both elastic and geometric stiffness matrices are available, allowing for the calculation of elastic deformations, stresses, and buckling in thin-walled structures. The shell element formulation utilizes linear shape functions to interpolate for membrane deformations, and considers additional quadratic shape functions to predict bending deformation while avoiding shear locking. The triangular shell element is shown to perform accurately when compared to Abaqus shell element and analytical solutions in a series of thin and thick plate examples which consider elastic deformation and elastic buckling.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "QNA3UC", "name": "Cris Moen", "avatar": "https://pretalx.com/media/avatars/QNA3UC_FYos8UV.webp", "biography": "Cris Moen is the CEO of RunToSolve LLC, an engineering technology company that supports industries with product R&D, software, simulation, and workflow automation solutions.", "public_name": "Cris Moen", "guid": "fb463b3d-d917-5a55-991a-2b70704e6ed5", "url": "https://pretalx.com/juliacon-2026/speaker/QNA3UC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XD88T7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XD88T7/", "attachments": []}, {"guid": "a907b4af-61be-54be-b11f-b6b9ba951227", "code": "CCVYAA", "id": 92729, "logo": "https://pretalx.com/media/juliacon-2026/submissions/CCVYAA/image_dHeXfzi.webp", "date": "2026-08-14T14:45:00+02:00", "start": "14:45", "end": "2026-08-14T15:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92729-jutarget-a-julia-powered-pipeline-built-with-a-hybrid-machine-learning-method-for-m-tuberculosis-drug-resistance-prediction", "url": "https://pretalx.com/juliacon-2026/talk/CCVYAA/", "title": "juTarget: A Julia-powered Pipeline built with a Hybrid Machine Learning method for M. tuberculosis Drug Resistance Prediction", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "juTarget is a browser based Linux application for M. tuberculosis tNGS data analysis that applies a hybrid ML method for predicting drug resistance. Industry standard toolkit has been used to identify the variants compared against the WHO catalogue. Furthermore, the resistance profile for novel variants is predicted by Random Forest using a feature set of 30 biophysical properties of amino acids. Molecular Drug Susceptibility Report is generated which can be used as a clinical decision-making tool.", "description": "**The Structure of the Talk is as follows:**\n\n**Background:** (2 mins; Will brief clinical challenges, limitations of the existing tools)\nThe emergence and persistence of drug resistant Mycobacterium tuberculosis has attracted researchers to come up with modern rapid diagnostic methods like tNGS. Nevertheless, the data analysis to extract the valuable information requires either cumbersome or costlier methodologies and so, we have developed juTarget, a dockerized browser based Linux application.\n\n**juTarget Pipeline:** (3 min; Will detail application architecture)\nWe integrated the industry standard tools into Julia such as: minimap2 for long read alignment, samtools for BAM manipulation, bcftools for variant calling. A customized Julia-native module is then applied to standardize chromosome names and apply quality filters. SnpEff was then used to annotate the variants to identify the deleterious mutations.\n\n**The Hybrid ML Method:** (4 min; Will describe our novel two-tier classification method)\nThe heart of this application is a hybrid machine learning method for classifying drug resistance.\n\n- **Catalogue-based Identification:** SnpEff-annotated variants are first compared with the extensively curated database of known resistance mutations, the WHO catalogue, prioritizing the clinical and biological relevance.\n\n- **Predictive Classification:** For novel uncatalogued variants, a Random Forest model classifies the mutations based on a feature set of 30 biophysical features of the amino acids.\n\n**Validation and Conclusion:** (3 min; Will conclude presenting the validation results)\nThe application has been validated by the experts with the clinical samples at the National Institute for Research in Tuberculosis (NIRT), Chennai, The accuracy of the method was confirmed with high concordance between the drug resistance profiles generated by juTarget and camspred, a locally developed pipeline, on a shared dataset. This project demonstrates Julia\u2019s applicability for developing complex, reproducible bioinformatics pipelines integrating the industry standard tools and custom scripts with ML models, providing a scientifically validated platform for Mycobacterium tuberculosis tNGS data analysis.\n\n**Technical Details:** The application runs on a local server and it is dockerized for portability. It is developed using Genei.jl framework in a user-friendly GUI ecosystem and the user does not have to be an expert Linux user. Parallel processing has been implemented considering that computer\u2019s RAM capacity. Status bars indicating the process status has been incorporated for easy monitoring. The results are stored locally which can be retrieved through the application interface at any time. The code is available on https://github.com/drbenedictpaul/jutarget.", "recording_license": "", "do_not_record": false, "persons": [{"code": "CWNJWS", "name": "Dr Benedict Christopher Paul", "avatar": "https://pretalx.com/media/avatars/GZ3ABS_Ubnnmyo.webp", "biography": "Dr. Paul is an Assistant Professor of Biotechnology and a Biomedical Data Scientist specializing in the integration of computational biology with experimental research. He holds a PhD in Computational Biology from VIT University, Vellore, with a research focus on breast cancer and aromatase inhibitors. With a unique academic foundation spanning Medical Laboratory Technology (CMC Vellore) and Biotechnology, his work bridges the gap between clinical context and molecular analysis. Dr. Paul is a strong advocate for the Julia programming language in scientific application development, having developed jSeqTB, a machine-learning integrated GUI application for Next-Generation Sequencing (NGS) data analysis of Mycobacterium tuberculosis. His lab focuses on developing high-performance tools like juProt and exploring TNGS-based diagnostics, aiming to address complex biomedical challenges through data science.", "public_name": "Dr Benedict Christopher Paul", "guid": "f8354f55-121c-5014-b691-96512a6819d0", "url": "https://pretalx.com/juliacon-2026/speaker/CWNJWS/"}], "links": [{"title": "juTarget_Demo", "url": "https://drive.google.com/file/d/1UjqqqXIVN_2A5yGuyEHRH8NErApBWAlv/view?usp=drive_link", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/CCVYAA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/CCVYAA/", "attachments": [{"title": "flyer-CCVYAA", "url": "/media/juliacon-2026/submissions/CCVYAA/resources/CCVYAA_omCRPR3.png", "type": "related"}, {"title": "juTarget_ViewResults_WHO", "url": "/media/juliacon-2026/submissions/CCVYAA/resources/ViewResults1_ZetCacw.png", "type": "related"}, {"title": "juTarget_ViewResults_AI", "url": "/media/juliacon-2026/submissions/CCVYAA/resources/ViewResults2_N3DbPEI.png", "type": "related"}]}, {"guid": "ee745e68-38dd-5f13-8435-7934d1097059", "code": "KGR8N9", "id": 93034, "logo": "https://pretalx.com/media/juliacon-2026/submissions/KGR8N9/image_fjtDBJU.webp", "date": "2026-08-14T15:00:00+02:00", "start": "15:00", "end": "2026-08-14T15:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93034-what-s-new-with-medyan-jl-a-coarse-grained-cytoskeleton-simulator", "url": "https://pretalx.com/juliacon-2026/talk/KGR8N9/", "title": "What\u2019s new with MEDYAN.jl: A Coarse-Grained Cytoskeleton Simulator", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "MEDYAN.jl is a framework for modeling the cytoskeletal filaments and associated proteins that shape and move cells. New declarative rules for dynamic mechanical and chemical bonds, accelerated by new spatial data structures, enable simulations as large as a whole T-cell or as detailed as the individual reactions in a motor protein step. These improvements help bridge the gap between single molecules and cell behavior on timescales of minutes and length scales of micrometers.", "description": "I will describe the new collision detection system for accelerating force and distance calculations between point, line segment, and triangle objects, including data structures and algorithms in the reusable SimplexCellLists.jl package.\n\nI will also describe the new system for handling the dynamic mechanical and chemical links that can form and break between entities during a simulation. Users can write declarative rules for any custom force law, reaction rate, and reaction effect. These rules leverage Julia to generate efficient multi-threaded force kernels. The chemical reaction rules are accelerated with spatial data structures from SimplexCellLists.jl. Link topology is tracked using generational-index graphs via UniqueIDs.jl, enabling efficient creation and deletion of transient connections. This modular and extensible system is important for describing the variety of protein interactions and chemistry in the cell.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TL7PTK", "name": "Nathan Zimmerberg", "avatar": "https://pretalx.com/media/avatars/Y3LCKR_DUfuuqC.webp", "biography": "PhD student in biophysics, University of Maryland College Park", "public_name": "Nathan Zimmerberg", "guid": "f55993b8-5654-5ebd-ae01-c141482d9385", "url": "https://pretalx.com/juliacon-2026/speaker/TL7PTK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/KGR8N9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/KGR8N9/", "attachments": [{"title": "flyer-KGR8N9", "url": "/media/juliacon-2026/submissions/KGR8N9/resources/KGR8N9_QGtbhi5.png", "type": "related"}]}, {"guid": "5e2c6dbc-13ef-599b-8037-d07793994a93", "code": "GJRCSK", "id": 92082, "logo": "https://pretalx.com/media/juliacon-2026/submissions/GJRCSK/image_pRYG9k2.webp", "date": "2026-08-14T15:15:00+02:00", "start": "15:15", "end": "2026-08-14T15:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92082-building-a-coulomb-explosion-simulation-on-top-of-differentialequations-jl", "url": "https://pretalx.com/juliacon-2026/talk/GJRCSK/", "title": "Building a Coulomb explosion simulation on top of DifferentialEquations.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Coulomb Explosion Imaging is a booming method to image small molecules. Its principle is relatively straightforward: remove as many electrons as possible as fast as possible from a molecule to induce its explosion into atomic fragments.\n\nSimulating this process is crucial to interpret the experimental data. In this talk I will briefly describe the concept of an x-ray induced Coulomb explosion, introduce a semi-classical model to simulate it and present how I implemented it.", "description": "One of the dreams of the molecular imaging community is to watch a chemical reaction while it is happening. To this end, various methods have been developed, trying to reach enough resolution in space and time to resolve molecular dynamics.\n\nOne such promising method is x-ray-induced Coulomb explosion imaging. Short x-ray pulses are used to remove many electrons from a single molecule, resulting in its ultrafast fragmentation into the composing atoms. The velocities of the fragments are then measured in coincidence, providing a probe of the molecule.\n\nThe interpretation of the post-explosion velocity data relies on simulations, to map features of the measured data to features of the molecule before its destruction. Therefore, simulations play a crucial role in this method and are expected to continue doing so as more advanced analysis techniques are being developed, including supervised deep learning of the molecular structure to reconstruct it from the experimental data.\n\nI implemented a semi-classical model for such simulation in Julia, building it on top of the DifferentialEquations.jl ecosystem. I will present the model and key properties of its Julia implementation, including\n\n- Coupling of continuous (positions and velocities) and discrete (electronic states) degrees of freedom of the atoms through JumpProcesses.jl\n- Flexible inputs and callbacks\n- Easy to update code, thanks to its short length (especially compared to the reference C implementation)\n\nWhile introducing the physics is necessary for context, the talk will focus as much as possible on the Julia implementation, also mentioning the challenges encountered, the benefits of relying on a mature ecosystem, and the possible future use for the code developed.", "recording_license": "", "do_not_record": false, "persons": [{"code": "GZQBUC", "name": "Beno\u00eet Richard", "avatar": "https://pretalx.com/media/avatars/KMMWMD_wAt8C88.webp", "biography": "Doctor in theoretical physics, working mostly in simulation and data analysis with experimentalists blowing up tiny things in large facilities.\n\nWorked on IntervalArithmetic.jl for my master thesis and stuck around.\n\nMade the questionable life choice of writing a LaTeX engine in julia.\n\nFirst julia version used: v0.4", "public_name": "Beno\u00eet Richard", "guid": "9dfc8e9d-fe73-5314-ab3d-19c491871fed", "url": "https://pretalx.com/juliacon-2026/speaker/GZQBUC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/GJRCSK/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/GJRCSK/", "attachments": []}, {"guid": "973d30fa-b4d9-5ea8-909c-0010160858ed", "code": "FDVTJJ", "id": 92879, "logo": "https://pretalx.com/media/juliacon-2026/submissions/FDVTJJ/image_Un8pbsC.webp", "date": "2026-08-14T15:45:00+02:00", "start": "15:45", "end": "2026-08-14T16:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92879-randomsequentialadsorption-jl-modeling-adsorbate-packing-in-area-selective-atomic-layer-deposition", "url": "https://pretalx.com/juliacon-2026/talk/FDVTJJ/", "title": "RandomSequentialAdsorption.jl - Modeling Adsorbate Packing in Area-Selective Atomic Layer Deposition", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "A precise material deposition is nowadays a key component of any microchip production. A prominent deposition technique offering the required level of control is area-selective atomic layer deposition. To improve this technique the chemical reactions of the adsorbates at the substrate surface are modeled. Here, the presented random sequential adsorption approach targets to model the adsorption and packing of the first adsorbate layer. Implementation of the key assumptions as well as first results are part of this contribution.", "description": "## The scientific background\nThe continuous shrinking of building blocks in modern microchip production has led to steady improvements in material deposition processes. As atomic layer deposition (ALD) offers nearly atomic control over the thickness of the deposited material it has become a common tool within the lithography process. Within ALD, so-called precursors and co-reactants are alternately offered to a surface where they react and result in the desired material deposition. Current research is focusing on improving ALD by constraining the material deposition to a certain area, the so-called growth surface, while preventing deposition on other parts of the surface, the so-called non-growth surface. Consequently, this deposition technique is called area-selective ALD (AS-ALD). The most promising strategy to AS-ALD is to use small molecules, which selectively adsorb only on the non-growth surface and block all other incoming molecules. As material deposition is inhibited by these molecules on the non-growth surface, they are usually termed small molecule inhibitors (SMI). To understand the chemistry of SMIs different modelling approaches spanning density functional theory, molecular dynamics, kinetic Monte-Carlo or random sequential adsorption are used.\n\n## The modeling approach\nThe presented random sequential adsorption (RSA) approach targets to derive realistic SMI packing layers on the non-growth surface. This approach assumes that SMIs are only weakly interacting with each other and their behavior therefore best described by a random adsorption. The adsorption takes place on a grid representing all adsorption sites of the modeled surface. In addition to adsorption events, the present implementation also includes diffusion, rotation and conformer changes of the adsorbates. Here, the implementation follows a simplified kinetic Monte-Carlo as time evolution and chemically motivated rate constants are ignored while the common cycle of generating a list of possible events, selecting the event to execute and updating the event list is maintained. Convenience functions to run and evaluate thousands of RSA runs are provided to easily judge the packing and inhibition efficiency of studied SMIs.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HAKD78", "name": "Fabian Pieck", "avatar": null, "biography": "I'm a staff scientist in the theoretical chemistry group at Leipzig University. My research is focusing on modeling chemical reactions within the (area-selective) atomic layer deposition.", "public_name": "Fabian Pieck", "guid": "23c8cb18-8873-5231-b650-9a80cc96375c", "url": "https://pretalx.com/juliacon-2026/speaker/HAKD78/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/FDVTJJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/FDVTJJ/", "attachments": []}, {"guid": "c411f3e1-c880-5490-b743-963c204662df", "code": "KXDFUJ", "id": 88736, "logo": "https://pretalx.com/media/juliacon-2026/submissions/KXDFUJ/mi_intro_fig_3s9ZCeM__dxGrTxd.webp", "date": "2026-08-14T16:00:00+02:00", "start": "16:00", "end": "2026-08-14T16:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-88736-modeling-optical-setups-with-beamletoptics-jl", "url": "https://pretalx.com/juliacon-2026/talk/KXDFUJ/", "title": "Modeling optical setups with BeamletOptics.jl", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "**BeamletOptics.jl** (BMO) is a Gaussian beamlet tracing package built on top of a geometrical ray tracing solver. It can be used to prototype laboratory optical setups featuring laser sources. BMO features a signed distance function (SDF) based geometry representation which allows for the accurate modeling of surface normals. It offers a variety of optical models for common components, like mirrors, lenses, beamsplitters and detectors. In addition, the API allows for the easy implementation of custom optical elements. This talk will focus on the current state of the package, feature several showcases and outline future development goals.", "description": "Designing optical setups featuring laser sources is a common task in many optical laboratories. The use of digital models for this purpose can allow for easier prototyping before committing to an experimental setup. Commercial and open-source software for this purpose already exists, for instance in Julia the OpticSim.jl and ABCDMatrixOptics.jl packages are referred to. These packages implement classic geometrical optics and matrix optics, respectively. An alternative modeling approach is the complex ray tracing or Gaussian beamlet tracing approach first proposed by J. Arnaud (1968) and later A. Greynolds (1985).\n\nThis formalism uses a set of geometrical rays to represent the 0th order Gaussian mode (TEM00). By using a classical ray tracing approach, this model of the Gaussian beam can be efficiently propagated through optical systems. More recent improvements of this method have also introduced the ability to model polarization effects and astigmatism. The TEM00 Gaussian mode can also be used to coherently decompose and propagate arbitrary electrical fields.\n\nThe goal of this package is to implement the mentioned approach with a focus on 3D volume-based optics modelling and easy manipulation of optics position and orientation. Therefore, we have opted for a solids-based geometry representation rather than the more established surface-based modelling approach. Our package features a hybrid sequential-non-sequential tracing solver and SDF-based representation of e.g. spherical and aspherical lenses. \n\nThis talk will focus on an introduction into the application programming interface (API) of BMO by explaining the structure of the underlying solver (Intersect-Interact-Repeat-Loop), the options for the representation of optical geometries and the optical models for mirrors, lenses, beamsplitters and more that are provided as part of this package. In addition, the extension capabilities for the implementation of custom optical models will be featured. We will talk about the current and future development roadmap for this package and showcase examples for practical uses cases from our research group.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HSZYQ7", "name": "Hugo Uittenbosch", "avatar": null, "biography": "PhD student at the German Aerospace Center (Institute of Technical Physics)", "public_name": "Hugo Uittenbosch", "guid": "57d498ad-c7ce-5eba-b101-4ed06529353c", "url": "https://pretalx.com/juliacon-2026/speaker/HSZYQ7/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/KXDFUJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/KXDFUJ/", "attachments": [{"title": "flyer-KXDFUJ", "url": "/media/juliacon-2026/submissions/KXDFUJ/resources/KXDFUJ_rVj7HBs.png", "type": "related"}]}, {"guid": "dad322f3-b01e-5255-adfb-a85ed2f4bcba", "code": "HYQWVR", "id": 93440, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HYQWVR/image_XhOYqfq.webp", "date": "2026-08-14T16:30:00+02:00", "start": "16:30", "end": "2026-08-14T16:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-93440-modelling-repulsion-beyond-determinants-sampling-pfaffian-point-processes", "url": "https://pretalx.com/juliacon-2026/talk/HYQWVR/", "title": "Modelling repulsion beyond determinants \u2014 Sampling Pfaffian Point Processes", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Pfaffian point processes (PfPPs) most famously arise in the eigenvalue distributions of random orthogonal or symplectic matrices, but also appear in the description of other stochastic processes, such as annihilating and coalescing random walks, random involutions, or symmetric corner growth. We introduce novel sampling algorithms for discrete and continuous PfPPs, as well as a method for constructing skew-symmetric kernels based on skew-orthogonal polynomials derived from arbitrary weights.", "description": "While many sampling algorithms have been developed for determinantal point processes, much less is known about sampling from PfPPs. We introduce an exact sampling algorithm for discrete PfPPs based on a skew-symmetric variant of the Cholesky decomposition, as well as a variety of methods for sampling from continuous Pfaffian kernels based on Markov chain Monte Carlo.\n\nPfaffian kernels are typically constructed from skew-orthogonal polynomials (SOPs), so we also introduce a new numerical method for constructing SOPs from arbitrary weight functions based on symplectic Arnoldi iteration.\n\nWe present a Julia toolbox for Pfaffian point processes with the methods we developed. All of this wouldn't have been possible without Julia's rich ecosystem for numerical linear algebra, computational statistics and automatic differentiation. As such, we were able to reuse existing functionality from and extend packages such as SkewLinearAlgebra.jl, KrylovKit.jl, DynamicHMC.jl, and Mooncake.jl\n\nWe investigate accuracy, as well as performance, of these methods on concrete examples and give an outlook on how these methods could be further improved upon.", "recording_license": "", "do_not_record": false, "persons": [{"code": "MUW7BP", "name": "Simeon Schaub", "avatar": "https://pretalx.com/media/avatars/LWE98T_R3EkBeu.webp", "biography": "PhD student at ENPC (Paris, France)\n\nInterested in computational mathematics, programming language design, automatic differentiation and compilers.\n\nGitHub: https://github.com/simeonschaub", "public_name": "Simeon Schaub", "guid": "e817590a-5090-58c1-932c-014496660a09", "url": "https://pretalx.com/juliacon-2026/speaker/MUW7BP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HYQWVR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HYQWVR/", "attachments": []}, {"guid": "4ba99208-5197-557d-a0d5-c8f7f86ccea3", "code": "7PQKQJ", "id": 92871, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7PQKQJ/image_DJyqsDw.webp", "date": "2026-08-14T16:45:00+02:00", "start": "16:45", "end": "2026-08-14T17:15:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N1", "slug": "juliacon-2026-92871-neuroblox-jl-new-features-and-applications", "url": "https://pretalx.com/juliacon-2026/talk/7PQKQJ/", "title": "Neuroblox.jl -- New features and applications", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Neuroblox.jl is designed for computational neuroscience, pharmaceutical, and psychiatric applications. Our tools range from control circuit system identification to brain circuit simulations bridging scales from spiking neurons to fMRI-derived circuits, parameter-fitting models to neuroimaging data, interactions between the brain and other physiological systems, experimental optimization, and scientific machine learning. \n\nIn this talk we will give an update on the new features we added in the last year, including a new DSL for interacting with Neuroblox, a rich library of pharmacologically relevant receptors, and new GUI features. We'll also discuss our progress on running Neuroblox on various accelerators, and algorithmic improvements we've made to our solving infrastructure.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "JE3MPL", "name": "Mason Protter", "avatar": "https://pretalx.com/media/avatars/VQKWL3_gBlYY5A.webp", "biography": "I'm a long time Julia user and passionate programmer. My background is in physics, but I'm currently working with Neuroblox.jl to develop computational neuroscience software in Julia. I like tinkering with interesting problems, and helping people learn to use Julia.", "public_name": "Mason Protter", "guid": "84e1c375-5738-5a13-9135-5f6d9d20e084", "url": "https://pretalx.com/juliacon-2026/speaker/JE3MPL/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7PQKQJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7PQKQJ/", "attachments": [{"title": "flyer-7PQKQJ", "url": "/media/juliacon-2026/submissions/7PQKQJ/resources/7PQKQJ_ScaRIyS.png", "type": "related"}]}], "Muschel \u2014 N2": [{"guid": "9edf8dba-fddf-57bb-b718-cbbb791cdef3", "code": "HMEGDF", "id": 88670, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HMEGDF/image_t2zWUcS.webp", "date": "2026-08-14T10:00:00+02:00", "start": "10:00", "end": "2026-08-14T10:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-88670-decoding-radio-time-signals-with-radioclock-jl", "url": "https://pretalx.com/juliacon-2026/talk/HMEGDF/", "title": "Decoding radio time signals with RadioClock.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "This talk is about [`RadioClock.jl`](https://github.com/giordano/RadioClock.jl), a Julia package to encode and decode time signals such as the [DCF77](https://en.wikipedia.org/wiki/DCF77).", "description": "About 60 km away from the venue of JuliaCon 26, there is an antenna broadcasting the radio signal [DCF77](https://en.wikipedia.org/wiki/DCF77), which is used to synchronize clocks, watches and other time-keeping devices all across Europe.  In this talk we well present [`RadioClock.jl`](https://github.com/giordano/RadioClock.jl), a Julia package to encode and decode time signals such as the DCF77 one.  We will also demonstrate (with a live demo!) how to build an Arduino-based system to receive the DCF77 radio signal, and decode it with Julia, with a submillisecond accuracy,", "recording_license": "", "do_not_record": false, "persons": [{"code": "ST7KZT", "name": "Mos\u00e8 Giordano", "avatar": "https://pretalx.com/media/avatars/ST7KZT_3IdkVo1.webp", "biography": "Research Software Developer at UCL during the day, binary builder during the night.", "public_name": "Mos\u00e8 Giordano", "guid": "70751b6a-0472-5cfa-81b8-4fd48c25a745", "url": "https://pretalx.com/juliacon-2026/speaker/ST7KZT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HMEGDF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HMEGDF/", "attachments": []}, {"guid": "a60887f1-4934-5b0c-939e-7c52b547a429", "code": "NAPBCA", "id": 89648, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NAPBCA/image_1o7hNEK.webp", "date": "2026-08-14T10:15:00+02:00", "start": "10:15", "end": "2026-08-14T10:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-89648-modelling-cost-sustainability-trade-offs-in-maritime-logistics-eedi-driven-multi-objective-optimization", "url": "https://pretalx.com/juliacon-2026/talk/NAPBCA/", "title": "Modelling Cost-Sustainability Trade-offs in Maritime Logistics: EEDI-Driven Multi-Objective Optimization", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Our paper develops a nonlinear bi-objective optimization model to analyze cost-emission\ntrade-offs in maritime fleet operations. The model minimizes total fleet cost and total fleet\nemissions through interactions between fuel share choices, digitization adoption, regulatory\nframeworks, and operational decisions. We implement the model using the Julia program-\nming language with the JuMP modeling framework, employing the \u03b5-constraint method\nto generate a discrete approximation of the Pareto frontier. Results demonstrate that cost-\neffective maritime decarbonization emerges from coordinated fuel transition, universal adop-\ntion of digitization technologies, and regulatory-driven fleet reallocation, rather than from\nisolated interventions. Sensitivity analysis across different digitization adoption modes re-\nveals that unconstrained digitization serves as a low-cost enabler of emissions reduction.", "description": "The maritime shipping industry is responsible for  approximately 3% of global GHG emissions, and 90% of world trade. The International Maritime Organization (IMO) has established an ambitious strategy targeting a 20% reduction in greenhouse gas (GHG) emissions by 2030, 70% by 2040, and full decarbonisation by 2050, relative to 2008 emission levels . However, by 2023 only a 3.6% reduction had been achieved. This gap motivates the need for  optimization frameworks jointly optimizing fuel choices , digitization adoption, regulatory compliance while maintaining cost and emissions feasibility. We present a Julia-based mixed-integer non-linear programming (MINLP) model which optimizes fuel mix, digitization adoption binary variables, EEDI/EEXI constraints and regional assignment variables. The model is formulated as a  bi-objective optimization model, minimizing both total systems costs and fleet emissions under given regulatory,demand,fuel and operational constraints.Optimization modelling has been carried out using Julia packages such as  JuMP.jl, with Ipopt.jl for non-linear optimization, HiGHS.jl for mixed-integer components and MathOptInterface.jl as the solver interface .Dataframes has been used for data handling, and visualization of Pareto frontiers was carried out using Plots.jl has been used for plotting.A custom implementation of the augmented \u03b5-constraint (AUGMECON) method is used to generate discrete approximations of cost\u2013emissions Pareto frontiers. The talk focuses on Julia-implementation challenges in solving large scale MINLPs, and reproducible modeling workflows. Our work is primarily based on demonstrating the  applications of the Julia programming language in the context of decarbonization in the transportation sector, highlighting the use of mathematical computing to solve real-world challenges.", "recording_license": "", "do_not_record": false, "persons": [{"code": "GHLUQC", "name": "Jia Bhanushali", "avatar": "https://pretalx.com/media/avatars/GHLUQC_u54C5xc.webp", "biography": "I am a second-year undergraduate student at the Indian Institute of Management Ranchi, with a strong inclination towards Operations Research, logistics, and supply chain management, focusing on optimization modelling and applied mathematical computational methods.", "public_name": "Jia Bhanushali", "guid": "185f0fe1-7646-58b9-a072-df335d737a3a", "url": "https://pretalx.com/juliacon-2026/speaker/GHLUQC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NAPBCA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NAPBCA/", "attachments": []}, {"guid": "628b7f38-0f42-51ba-97ea-f49f2b3d1523", "code": "N7TFF3", "id": 93745, "logo": "https://pretalx.com/media/juliacon-2026/submissions/N7TFF3/image_R27teNf.webp", "date": "2026-08-14T10:30:00+02:00", "start": "10:30", "end": "2026-08-14T10:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93745-visualizations-for-modeling-and-simulation-with-makie", "url": "https://pretalx.com/juliacon-2026/talk/N7TFF3/", "title": "Visualizations for modeling and simulation with Makie", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Building and understanding complex models is easier when you can see them come to life.  At JuliaHub, we have been creating interactive dashboards that show simulation results, allow live, interactive analysis, and some which even show real world data, for quite some time now.  In this talk, I will go through some of the paradigms we've developed and how those have played out, as well as showing some cool examples for inspiration.", "description": "Visualization is the final product of most engineering workflows.  Julia's ecosystem enables users to create fast, interactive and beautiful visualizations, now easier than ever with coding agents.  The aim of this talk is to show folks some strategies and ways to create and think about both animations and \"live\" dashboards in the engineering context.  Much of the focus will be on system simulation, ModelingToolkit,jl and Dyad, but there will be some examples outside of that as well.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RQHPG", "name": "Anshul Singhvi", "avatar": "https://pretalx.com/media/avatars/7RQHPG_OxNT4Gf.webp", "biography": "Product engineer for Dyad, the new modeling and simulation language from JuliaHub.  Also heavily involved in geospatial (via JuliaGeo and GeometryOps.jl) and Makie.jl, as well as the Documenter.jl ecosystem.", "public_name": "Anshul Singhvi", "guid": "5b6d2c3a-d127-5c27-8672-a0d779e40d95", "url": "https://pretalx.com/juliacon-2026/speaker/7RQHPG/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/N7TFF3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/N7TFF3/", "attachments": []}, {"guid": "ac0fa364-6ee9-5619-895d-66c2a0c00351", "code": "DPR3VJ", "id": 93065, "logo": "https://pretalx.com/media/juliacon-2026/submissions/DPR3VJ/image_PMZvMQT.webp", "date": "2026-08-14T10:45:00+02:00", "start": "10:45", "end": "2026-08-14T11:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93065-formal-linear-combinations-in-julia-with-linearcombinations-jl", "url": "https://pretalx.com/juliacon-2026/talk/DPR3VJ/", "title": "Formal linear combinations in Julia with LinearCombinations.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Formal linear combinations are ubiquitous in Mathematics. The package [LinearCombinations.jl](https://github.com/matthias314/LinearCombinations.jl) provides an easy and efficient way to deal with them, as well as with linear and multilinear maps.", "description": "Formal linear combinations (aka \"vector spaces with a given basis\" or \"free modules\") appear in many places in (Pure) Mathematics. For example, a polynomial is a formal linear combination of monomials. The package [LinearCombinations.jl](https://github.com/matthias314/LinearCombinations.jl) defines a type `Linear{T,R}` for formal linear combinations of terms of type `T` (arbitrary)  with coefficients of type `R` (any commutative ring with unit). The type `DenseLinear{T,R}` automatically translates between linear combinations and coordinate vectors with respect to some chosen basis. Linear and multilinear functions can easily be defined with the macros `@linear` and `@multilinear`. Tensors are also supported, as is the Koszul sign rule in the graded setting. The overall aim of the package is to provide functions that are efficient and easy to use.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BY8DLV", "name": "Matthias Franz", "avatar": null, "biography": "Professor of Mathematics at the University of Western Ontario, with interest in Topology and Computer Algebra.", "public_name": "Matthias Franz", "guid": "cfdcc24a-fd67-5893-87a9-d417c3af60fc", "url": "https://pretalx.com/juliacon-2026/speaker/BY8DLV/"}], "links": [{"title": "GitHub repository", "url": "https://github.com/matthias314/LinearCombinations.jl", "type": "related"}, {"title": "Documentation", "url": "https://matthias314.github.io/LinearCombinations.jl/", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/DPR3VJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/DPR3VJ/", "attachments": []}, {"guid": "8138f6a8-5dae-5141-bc0b-a58a67121cc6", "code": "HYN7DE", "id": 92635, "logo": "https://pretalx.com/media/juliacon-2026/submissions/HYN7DE/image_tYivuCq.webp", "date": "2026-08-14T11:15:00+02:00", "start": "11:15", "end": "2026-08-14T11:45:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92635-introducing-contexts-jl-context-and-role-oriented-programming-for-self-adaptive-systems", "url": "https://pretalx.com/juliacon-2026/talk/HYN7DE/", "title": "Introducing Contexts.jl: Context- and Role-Oriented Programming for Self-Adaptive Systems", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Technical systems become increasingly complex, optimizing their processes by adapting to external conditions. Such self-adaptive systems (SAS) need software that dynamically adapts its behavior.\nContext-Oriented and Role-Oriented Programming simplify defining dynamic behavior. Hence, implementing these concepts in Julia is beneficial for SAS development. The library Contexts.jl provides infrastructure for using these paradigms in Julia. This talk introduces its structure and key features.", "description": "Technical systems, such as power grids and vehicles, grow increasingly complex as they optimize their processes by adapting to both environmental conditions and their internal states. These so-called self-adaptive systems (SAS) rely on software capable of dynamically adjusting their behavior to meet these demands.\n\nContext-Oriented Programming (COP) and Role-Oriented Programming (ROP) are programming paradigms designed to simplify the definition of such dynamic behavior. The Julia programming language, with its just-in-time compilation and multiple dispatch, is particularly well-suited for implementing and invoking dynamic behaviors. Furthermore, Julia\u2019s simulation ecosystem, including tools like ModelingToolkit.jl, provides an ideal environment for simulating self-adaptive cyber-physical systems. This makes the implementation of COP and ROP in Julia a valuable contribution to the development of SAS. The Contexts.jl library offers a framework for leveraging these paradigms in Julia. This talk will introduce its structure and key features.\n\nThe presentation will begin with an introduction to the concepts of COP and ROP, highlighting their role in the development of self-adaptive systems. The second part will focus on the implementation of COP in Contexts.jl, detailing its architecture and the use of Julia\u2019s multiple dispatch and metaprogramming capabilities. A brief example will demonstrate how Contexts.jl allows specifying context-dependent behavior and contextual control mechanisms. The final section will introduce ROP, its implementation as an embedded domain-specific language, and a practical example showcasing its application.\n\nThis talk will provide attendees with a comprehensive understanding of how COP and ROP paradigms, implemented in Julia, can advance the development and simulation of self-adaptive systems.", "recording_license": "", "do_not_record": false, "persons": [{"code": "EB3AZB", "name": "Christian Gutsche", "avatar": null, "biography": "I am a PhD student at TU Dresden. After studying physics, I began a PhD in the Boysen-TU Dresden-Research Training Group and the Chair of Software Technology at TU Dresden in 2023. My research focuses on extending Equation-based Modeling (EBM) languages to improve simulations of cyber-physical systems.", "public_name": "Christian Gutsche", "guid": "86e997f7-abc1-5ce7-b0f4-f7b273ffd5b6", "url": "https://pretalx.com/juliacon-2026/speaker/EB3AZB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/HYN7DE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/HYN7DE/", "attachments": []}, {"guid": "c7a6bb51-1be1-5632-9bc4-17f1f23f5bb9", "code": "XT7QD8", "id": 92452, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XT7QD8/image_PWbLlty.webp", "date": "2026-08-14T11:45:00+02:00", "start": "11:45", "end": "2026-08-14T12:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92452-reasoning-with-many-valued-spatial-and-temporal-logics-with-sole", "url": "https://pretalx.com/juliacon-2026/talk/XT7QD8/", "title": "Reasoning with Many-Valued, Spatial and Temporal Logics with SOLE", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Reasoning with temporal and spatial data is crucial in many real-world applications; however, this data is often characterized by uncertainty and unclear boundaries. In this talk, we will see how we can extend spatial and temporal modal logics offered by SOLE through a new submodule, namely ManyValuedLogics, offering support for fuzzy and many-valued logics. Moreover, we will explore a new package called SoleReasoners.jl, offering a reasoning tool for many-valued spatial and temporal logics.", "description": "Many real-world applications make use of temporal and spatial data, and reasoning is among the most important tasks, allowing to solve problems spanning from scheduling, to monitoring, up to predicting future scenarios. However, this data is often characterized by uncertainty and unclear boundaries (e.g., due to sensoring and discretization), challenges usually tackled with the use of fuzzy logics.\n\nSoleLogics.jl offers a new submodule, namely ManyValuedLogics, allowing for the treatment of continuous fuzzy logics and finite many-valued logics, definable over their algebraic counterpart up to FLew-algebras. In this talk, we will see through practical examples how we can make use of this new machinery, together with multi-modal logics already offered by SoleLogics.jl to treat spatial and temporal information (e.g., Linear Temporal Logic, Compass Logic, Halpern and Shoham\u2019s Interval Temporal Logic, Lutz and Wolter\u2019s Logic of Topological Relations), to model real world scenarios with more accuracy.\n\nIcing on the cake, SoleReasoners.jl is a new package in the SOLE framework offering a reasoning tool, based on analytic tableau technique, to solve satisfiability and validity for many-valued spatial and temporal logics (the first specialized implementation at this level of generality that we know of!), and we will see how we can make use of it when dealing with logics carrying both a many-valued and a multi-modal component.", "recording_license": "", "do_not_record": false, "persons": [{"code": "F8PAGK", "name": "Alberto Paparella", "avatar": "https://pretalx.com/media/avatars/UDB889_Ui4U2wy.webp", "biography": "Hello everyone! My name is Alberto Paparella, and I am currently a PhD student in Mathematics at the University of Ferrara. My main interests are Mathematical Logic, specifically Many-Valued and Modal Logics, and Machine Learning. In the last few years, I have been working with the Applied Computational Logic and Artificial Intelligence Laboratory on the SOLE framework for Symbolic Learning in Julia, where my main contributions have been a sub-module for the SoleLogics.jl core package to work with Many-Valued Logics and a package for satisfiability and authomated theorem proving for Many-Valued Multi-Modal Logic based on analytic tableau technique, namely SoleReasoners.jl.", "public_name": "Alberto Paparella", "guid": "33d2d10b-d698-5b17-be0d-1cf0d7b78943", "url": "https://pretalx.com/juliacon-2026/speaker/F8PAGK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XT7QD8/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XT7QD8/", "attachments": []}, {"guid": "e3991115-8d65-5f33-8ce0-d5bbe0adc1a3", "code": "F8L7TA", "id": 93445, "logo": "https://pretalx.com/media/juliacon-2026/submissions/F8L7TA/image_WP5wyqI.webp", "date": "2026-08-14T12:00:00+02:00", "start": "12:00", "end": "2026-08-14T12:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93445-big-simulation-models-suddenly-feel-very-small-with-fmi-jl", "url": "https://pretalx.com/juliacon-2026/talk/F8L7TA/", "title": "Big simulation models suddenly feel very small - with FMI.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Using the _Functional Mock-Up Interface_ (FMI), we can handle and exchange big simulation models.  It seems only logical to integrate this standard into our favorite programming language. Our open-source journey of _FMI.jl_ started almost exactly 5 years ago with this goal in mind: Blur the boundaries between Julia and FMI. In this talk, we want to give a broad overview over what is possible with FMUs in Julia today \u2013 with live programs that fit a single slide each.", "description": "One of the biggest obstacles in the transition from demo application to the task that we actually want to solve is often the dimension of the simulation model. The bigger (and more complex) the system of equations becomes, the less handy it is. To tackle this issue, various modeling tools have been developed for different domains over time, and the multitude of tools has created a new problem: The exchange of models between the tools is not trivial \u2013 but definitely necessary!\n\nThe _Functional Mock-Up Interface_ (FMI) was developed with the aim of eliminating this problem in the field of engineering industry. And because this worked out quite well, other domains beyond engineering adapted the standard. The idea was quite simple: Define an interface, that allows for the creation of simulation models that can be imported and exported by a variety of simulation tools. Models that implement this standard are known as _Functional Mock-Up Units_ (FMUs). \n\nSo, anyone who works with large simulation models from industry has probably had contact with FMI at one time or another. And it seems only logical to integrate this standard into our favorite programming language. Our open-source journey of _FMI.jl_ started almost exactly 5 years ago with this goal in mind: Blur the boundaries between Julia and FMI. Since then, the core library opened up to many new and interesting application domains. In this talk, we want to give a broad overview over what is possible with FMUs in Julia today \u2013 with live programs that fit a single slide each.\n\nWith the combined power of Julia and FMI, dealing with big simulation models becomes as easy as playing around with small demo systems.", "recording_license": "", "do_not_record": false, "persons": [{"code": "XBWQVT", "name": "Tobias Thummerer", "avatar": "https://pretalx.com/media/avatars/PWQVZG_ZCQd4A4.webp", "biography": "... is an advanced doctoral student at University of Augsburg. He completed both his Bachelor's and Master's degree in \u201cEngineering and Computer Sciences\u201d at this same university. His research focuses on Scientific Machine Learning, and he is specifically engaged with the area of Hybrid Modeling, the combination of simulation models \u2013 often from engineering \u2013 and novel machine learning approaches. His research includes the development of new methods in this field, but explicitly also the improvement of new and established approaches so that they can be applied in a demanding real-world environment.", "public_name": "Tobias Thummerer", "guid": "be2f37c5-8630-504d-a268-e5acbd030fd0", "url": "https://pretalx.com/juliacon-2026/speaker/XBWQVT/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/F8L7TA/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/F8L7TA/", "attachments": [{"title": "flyer-F8L7TA", "url": "/media/juliacon-2026/submissions/F8L7TA/resources/F8L7TA_cewwsHM.png", "type": "related"}]}, {"guid": "89941283-a9b7-554d-aa9f-6db512225f5e", "code": "7AEJTQ", "id": 89346, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7AEJTQ/image_NF9i8hL.webp", "date": "2026-08-14T12:15:00+02:00", "start": "12:15", "end": "2026-08-14T12:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-89346-structuredilluminationmicroscopy-jl", "url": "https://pretalx.com/juliacon-2026/talk/7AEJTQ/", "title": "StructuredIlluminationMicroscopy.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Structured Illumination Microscopy is a method in which a fluorescent sample is illuminated with a number of patterns containing high spatial frequencies. This encodes super-resolution information into the  classical light microscopy image which is the successively recovered by Fourier-space based reconstruction methods. \nThis talk will introduce into the topic and then highlight some of the concepts behind `StructuredIlluminationMicroscopy.jl`, which supports some of the fastest algorithms for reconstructing images measured by structured illumination. The package features Fourier-space reconstruction approaches including upsampling, reconstruction and  noise-reduction steps. It exploits `rFFT`s and `SeparableFunctions.jl`, wherever possible, minimizes the memory footprint by working on pre-allocated arrays and fully supports GPU acceleration via `CUDA.jl`.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "CNV3SE", "name": "Rainer Heintzmann", "avatar": "https://pretalx.com/media/avatars/CNV3SE_lYiMnOM.webp", "biography": "I am heading a department at the Leibniz Institute of Photonic Technology, where our research focuses on imaging cellular function at high resolution. We develop new light microscopy techniques to measure multidimensional information in small biological objects such as cells, cellular organelles or other small structures of interest.\n\nComputer-based reconstruction methods, in particular in Julia, are a core focus and support many of our developments.", "public_name": "Rainer Heintzmann", "guid": "838d872b-70f8-5f6c-9acd-36f5a0289cf6", "url": "https://pretalx.com/juliacon-2026/speaker/CNV3SE/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7AEJTQ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7AEJTQ/", "attachments": []}, {"guid": "6802c380-3e01-5d25-b765-fca8f57a6734", "code": "XYMFMZ", "id": 92542, "logo": "https://pretalx.com/media/juliacon-2026/submissions/XYMFMZ/image_nkxb5vn.webp", "date": "2026-08-14T12:45:00+02:00", "start": "12:45", "end": "2026-08-14T13:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92542-this-is-a-metaline-announcing-the-release-of-gometa", "url": "https://pretalx.com/juliacon-2026/talk/XYMFMZ/", "title": "#~ This is a metaline announcing the release of `GoMeta`", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "With this talk, `GoMeta` will be released. This package finally implements a vastly matured offspring of a [my] half-baked idea proposed at *JuliaCon2025*.\n\nThe core concept remains the same: Add interpretable meaning to comments within a Julia file by means of brief, simple, expressive and extendable metadata. The crux of the idea lies in its potential to serve a wider variety of different packages and even facilitating interactions between them.\n\nHowever, in order to truly fulfill what had been promised, in particular to allow for the desired expressiveness and extensibility, language-like features had to be incorporated in the proposed schema.\n\nMoreover, metadata needs to be absorbed, i.e.: parsed and interpreted, from *somewhere* before it can be applied *somewhere*. Thus far, both tasks had been executed on a `Block`-level [a section of consecutive lines]. `BLS` now provides a distinctly finer granularity by introducing `Component`s such as `Block`, `Line` and `Segment`. This not only necessitated a complete overhaul of `GoMeta`'s implementation but also of the grammar being used.", "description": "The primary aim of this talk is to motivate embedding formalized meaning into a Julia file's comments **by example.** The same principles are easily extended to other programming languages or project data in general. These illustrations go hand in hand with the introduction of `GoMeta`.\n\n`GoMeta` handles the metadata. However, before it can begin its work, `BLS` reads the file[s], extracts features of interest and records them in a tree-like structure based on the content at hand. The basic building block of the aforementioned structure consists of the `Component`. `Block`s, `Line`s and `Segment`s are all `Component`s at different levels in the hierarchy. The records include where a `Component` starts and ends, its *flavor* [to avoid the term `Type`] such as *code*, *metadata* or *text*, its place in the tree structure and **much, much more**.\n\nNext, `GoMeta` absorbs, i.e.: parses and interprets, the metadata stored in the corresponding `Component`s. The result can then be passed to a separate program or plug-in. At this point the question may arise: What would such a program or plug-in possibly use this for? What can actually be gained from embedding formalized metadata within comments?\n\nTo address this question, before continuing with the technicalities of `GoMeta`, lets consider **four, seemingly unrelated use cases** which together ought to resonate with the vast majority of JuliaCon's attendees:\n\n1. Over time a large number of Julia files can accumulate. Wouldn't it be wonderful to speedily retrieve any desired, long forgotten poignant example, powerful code snippet, insightful explanatory statement or laboriously tweaked plot related to some task at hand? Adding **meaning** to content can change the game. On top of that, an instrument solving this problem might prove even more invaluable when collaborating in a group sharing a repository of documents and it is also not restricted to Julia files only.\n\n2. One might want to [inter]link a certain fragment of one file with another fragment, possibly contained in some other file. E.g.: linking an intricate piece of code to an enlightening explanation or a clarifying toy example. Occasionally it could also be handy to swap entire sections of text / code of one file with alternate versions, depending on the circumstances.\n\n3. Would you like to harness the power of `Documenter`, enjoying simplified control while sticking to pure Julia files rather than having to deal with additional markdown documents? More generally, a Julia file can serve as a source for all kinds of outputs.\n\n4. Being able to control how individual blocks of code / text are being executed, respectively rendered, can be beneficial. E.g.: certain passages of a file might be intended for personal usage only. Marking them as such would allow automated removal before sharing. **`GoMeta`'s own source code, available on `GitHub`, has been processed this way**. Another example is the ability to restrict execution to code blocks pertaining to a particular group only.\n\nSimilar to `Literate` & Co., segments of code and text can live happily together, weaved into the same files. These serve as source files which can subsequently be converted into a variety of formats such as notebooks, standard markdown or even documenter markdown files. A suitable system for metadata **allows exerting precise control over these format's wide ranging capabilities from within the Julia source file** and provides the means to generalize `Literate`'s core idea and more. The concept extends well beyond Julia and **can be applied not only to other programming languages but to all sorts of text documents** such as lectures notes or any number of scripts concerning a project, research or otherwise.\n\nSo what does the proposed schema for metadata look like?\n\nAny line starting with `#~` is considered to be a `metaline` i.e.: it will be processed as metadata. **Contiguous metalines** constitute a coherent *block of metadata* separating it from others. By **positioning such a block just above a block of code or text** [no vertical spacing in between] the former gets attached to the latter \u2013 similar to how docstrings get associated with Julia structures following them. For the purpose of `inheritance`, a **hierarchy of blocks of metadata** can be defined by appending additional `~`s to the head of a metablock's first metaline or, alternatively, adding there a digit representing the desired depth within the *metahierarchy*.\n\nAll this is readily exemplified by a self-explanatory **toy** example. It is contained in the `GoMeta` folder together with **instructions** and **additional tools** revealing some insights into the package's inner workings.\n\nThe first three lines in the code below form a `Block` of metadata at level 1. The **optional** `{}` following `hide` on line 1 allows the user to **specify conditions** under which `hide` **may** get applied. In this case, a `Block`, say `aBlock`, inheriting from it will be *hidden*, if it has been attributed `:label4` **or** is a *code* `Block` **provided** there is no opposing statement closer to `aBlock` overruling it. Analogously, `:label1` gets assigned to anything which inherits from this first meta `Block`, if it is *text* **and** *containsMeta* **or** is itself *meta*, **provided** there is no other statement closer by, overruling this.\n\n```\n#~ hide{ :label4 , isCode } ## This is a comment within a `Block` of meta.\n## The above `Line` of meta initiated this `Block` of meta.\n#~ :label1{ (isText && containsMeta), isMeta } ## This meta `Block` ends here.\n\n# This `Line` of text starts a new `Block` of text.\n# This `Block` is NOT attached to metadata - it does NOT INHERIT metadata.\n# However, this `Line` will get discarded due to this: #~ discard\n\n#~2 :label5 show{ !:label5} ## This is line 9. It is a one-line meta `Block`.\nusing Plots ## This `Line` starts a new `Block` of code.\n## This code `Block` is ATTACHED to metadata - it inherits metadata from above.\n## Therefore, this code `Block` receives label5.\n## `show` from line 9 above does NOT get applied to this `Block`\n##      as this can only happen\n##      if label5 has NOT been applied. Note `{!:label5}` following `show`.\n## Instead, this code `Block` inherits `hide` from line 1 above\n##      since this is a code `Block` satisfying the condition `{isCode}` following `hide`.\n\nprintln(\"!!! NOTE !!! Only Code and Text `Block`s may contain empty lines.\")\nprintln(\"\\t Whereas an empty line after a meta `Line` starts a new `Block`.\")\n\n#~3 :label4 discard{:label3} :label3\n# This `Block` of text receives label4\n#       plus label5 from further above\n#       plus label1 since it `isText` AND `containsMeta` [see last statement of this `Block`].\n# `discard{:label3}` has NO effect here\n#       since label3 has NOT been applied yet,\n#       it gets added only after `discard{:label3}` has been issued.\n# Instead, it gets \"hidden\" due to its label4 and `hide{:label4}` on line 1.\n# This `Line`, however, will NOT get hidden. #~ show\n\n#~2 :label5 ## This one-line meta `Block` is at level 2.\n##      Thus, this meta `Block` inherits only from the first meta `Block` at the top. \nmd\"\"\"\nThis is a `Block` of markdown text. #~ hide\n\"\"\"\n```\n\nThe alert reader may have noticed that this example alludes to the possibility of a finer grained grammar. For instance, a `Segment` of metadata within a `Line` is applied to the `Segments` preceding it. This also applies within `Block`s which are not attached. However, a more thorough discussion of the matter is beyond the scope of this proposal.\n\nMore importantly though, `GoMeta` does not execute the instructions inscribed in the metadata. Instead, it provides additional functions and other packages with the means to do so. The source code for `GoMeta` available on `GitHub` has been stripped of all tests and non-essential print statements as well as personal notes and metadata by applying one single, simple function using the output of `GoMeta`. Conversely, these *discarded* entities can easily and selectively be re-integrated if so desired.\n\nIt is worth pointing out, that the vocabulary of instructions used in the example above has been restricted to a bare minimum for the purpose of illustrating the concept. Due to the **modular design of `GoMeta` it is easily altered, extended and enables future contributors to integrate their own types of metadata**. Moreover, `()` are used for **argument passing**. But again, a more thorough discussion of the matter is beyond the scope of this proposal.\n\nIn the near future, AI will automate much of the labeling in the background. Crucially, if this black box is confined within a formalized framework of metadata, intermediary steps can be systematically recorded, making the process not only better traceable but allowing for subsequent adjustments, if necessary \u2013 potentially enabling the agent to improve over time.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZH8CWP", "name": "Jerae Sieburgh", "avatar": null, "biography": "Profile stuff will follow :-)", "public_name": "Jerae Sieburgh", "guid": "e03b5b69-9f05-50f6-b9c7-b460edb73821", "url": "https://pretalx.com/juliacon-2026/speaker/ZH8CWP/"}], "links": [{"title": "Source Code", "url": "https://www.dropbox.com/scl/fo/xmll604cj5obwzuyxiixs/ALEQJUdHLezRqKds04znRWU?rlkey=6ykxlyh98ze1dsux79jykkfzd&st=ev90kz6c&dl=0", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/XYMFMZ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/XYMFMZ/", "attachments": []}, {"guid": "d7d9399c-bf00-5bd1-95a1-55f5c252a008", "code": "TXMEAT", "id": 92629, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TXMEAT/image_uLoAjwS.webp", "date": "2026-08-14T16:00:00+02:00", "start": "16:00", "end": "2026-08-14T16:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92629-let-s-run-julia-everywhere-from-mobile-to-web", "url": "https://pretalx.com/juliacon-2026/talk/TXMEAT/", "title": "Let's run Julia everywhere from mobile to web", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "We implemented a Rust-based virtual machine that accepts and executes a subset of Julia syntax. \nThis enables Julia code to run in environments where the official runtime is difficult to deploy. By compiling the VM to WebAssembly, Julia can run web apps for educational purposes, and static linking with Swift or Flutter allows mobile applications. This short talk demonstrates these capabilities through live demos.", "description": "As shown on the [Supported platforms Julia](https://julialang.org/downloads/support/) page, Julia guarantees \u201cTier 1\u201d support for common developer platforms such as Windows, macOS, and Linux. This satisfies most users. However, as shown on the [Why We Created Julia](https://julialang.org/blog/2012/02/why-we-created-julia/) page, `We are greedy: we want more`.\n\nWe would like to run Julia in offline mobile environments such as airplanes, inside web browsers without long startup delays (TTFX), and even on older ARMv7 (32-bit) Raspberry Pi devices. Due to technical constraints, the official Julia runtime is difficult to use in these settings. In particular, platforms like iOS and iPadOS, where JIT compilation is restricted and app review is strict, are especially challenging.\n\nTo address this, we implemented a virtual machine in Rust that accepts and executes a subset of Julia syntax:\n\nhttps://github.com/AtelierArith/julia-vm-oss\n\nBecause Rust is designed for systems programming, it supports many platforms that Julia does not directly target. Since the VM does not rely on JIT compilation, it can be deployed as a native iOS and iPadOS application:\n\nhttps://apps.apple.com/us/app/subsetjuliavm/id6757257182\n\nRust can also be compiled to WebAssembly, allowing the VM to be integrated into browser-based applications:\n\nhttps://terasakisatoshi.github.io/subset_julia/\n\nActually, the VM itself is largely AI-generated using Claude Code, Codex, and Cursor. Human guidance was used to reference the official Julia implementation and to keep parts written in Julia whenever possible.\n\nWe also explored transpiling Julia code into Rust. Initial experiments show that it is possible to generate programs such as Mandelbrot set visualizations and distribute them as standalone binaries. In principle, these binaries can run on any platform supported by Rust. \n\nCompared with approaches that implicitly depend on `libjulia`, this provides a more portable alternative.\n\nThis short talk demonstrates these ideas through live demos.", "recording_license": "", "do_not_record": false, "persons": [{"code": "CWCTFA", "name": "terasakisatoshi", "avatar": "https://pretalx.com/media/avatars/KGTQWK_TKwYxAq.webp", "biography": "I develop some Julia packages on GitHub. \n\nSee https://github.com/AtelierArith", "public_name": "terasakisatoshi", "guid": "9bc7bc50-516f-5ecc-a021-f29334433dce", "url": "https://pretalx.com/juliacon-2026/speaker/CWCTFA/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TXMEAT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TXMEAT/", "attachments": [{"title": "flyer-TXMEAT", "url": "/media/juliacon-2026/submissions/TXMEAT/resources/TXMEAT_Bp4qtzW.png", "type": "related"}]}, {"guid": "cd796d9b-2823-5dc4-aad8-59acacda1175", "code": "L7CKA7", "id": 92824, "logo": "https://pretalx.com/media/juliacon-2026/submissions/L7CKA7/image_HTzIEXf.webp", "date": "2026-08-14T16:15:00+02:00", "start": "16:15", "end": "2026-08-14T16:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-92824-every-bit-counts", "url": "https://pretalx.com/juliacon-2026/talk/L7CKA7/", "title": "Every Bit Counts", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Julia supports defining new integer types. However, they are currently limited to byte size. Until this restriction is lifted, we can emulate arbitrary bit-sized integers with larger byte-sized ones which should behave identical to future native bit-sized integers. This is what EmulatedBitIntegers.jl does as a generalization of BitIntegers.jl to non-byte-sized integers.\n\nThis emulation produces unused bits. Often, structs can be used to combine such types with other emulated integers, making use of the unused bits of one emulated integer to store the content of another emulated integer. This is done with PackedStructs.jl which allows annotating structs to have their fields packed on bit-level to not waste a single bit, because: Every Bit Counts!", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "UGCEZ9", "name": "Patrick H\u00e4cker", "avatar": null, "biography": "- Studies of Electrical Engineering and Information Science at University Stuttgart\n- PhD in statistical signal processing at University Stuttgart\n- Working for Bosch in different roles on perception systems for road and rail vehicles", "public_name": "Patrick H\u00e4cker", "guid": "d48bc7be-b514-52b8-ac3f-91753799ff14", "url": "https://pretalx.com/juliacon-2026/speaker/UGCEZ9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/L7CKA7/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/L7CKA7/", "attachments": []}, {"guid": "2def90f0-1f06-5b7d-b326-adac0e09a5bf", "code": "URPF3H", "id": 93268, "logo": "https://pretalx.com/media/juliacon-2026/submissions/URPF3H/image_7IoZ3VT.webp", "date": "2026-08-14T16:45:00+02:00", "start": "16:45", "end": "2026-08-14T17:15:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N2", "slug": "juliacon-2026-93268-hyperhessians-jl-forward-mode-ad-specialized-for-second-order-derivatives", "url": "https://pretalx.com/juliacon-2026/talk/URPF3H/", "title": "HyperHessians.jl -- Forward mode AD specialized for second order derivatives", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Information about the sensitivity (the derivative) of a function is of great use in, among many other things, non-linear optimization and root-finding algorithms.\nIn particular, second-order derivative information (curvature) can be used to accelerate such solvers, for example, the Newton method for optimization and [Halley's method](https://en.wikipedia.org/wiki/Halley%27s_method) for root finding.\n*Automatic Differentiation* (AD), where these sensitivities are effectively available \"for free\" (in terms of developer time investment), is therefore attractive since it can significantly reduce the time of an implementation. In addition, the performance cost of the AD may either be close to a hand-optimized implementation or not be significant compared to other parts of the full problem, making AD attractive even from a performance standpoint.\n\nIn Julia, there are many packages for AD, each with different trade-offs. They might use forward mode AD or reverse mode AD, they might be implemented using operator overloading or by using code inspection, or they might focus on a certain application like machine learning, etc.\nHyperHessians.jl is a Julia package for forward mode AD that specializes in taking second-order derivatives (Hessians). It does this by using [*HyperDual* numbers](https://www.mdpi.com/2227-7390/13/24/3909), which is an extension of [Dual numbers](https://en.wikipedia.org/wiki/Dual_number). By adopting hyperdual numbers, we can show performance gains over traditional nested dual numbers for second-order derivatives, which is employed by, e.g., ForwardDiff.jl. In addition, HyperHessians.jl supports computing Hessian-vector products (Hvp) and quadratic forms (v'Hvp) at a much lower cost than computing the full Hessian, which is not available with straightforward usage of ForwardDiff.\n\nIn this presentation, I will go through some of the theory behind hyperdual numbers, how this theory is implemented in the HyperHessians.jl package, some of the implementation considerations, and present some benchmarks (both micro and real-world benchmarks) that show HyperHessians.jl has value as yet another AD package in the Julia AD ecosystem.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "8N3ZHH", "name": "Kristoffer Carlsson", "avatar": null, "biography": "Software engineer at JuliaHub working on the language, releases and tooling.", "public_name": "Kristoffer Carlsson", "guid": "594ec697-a1bb-5816-9ffd-c92c93250002", "url": "https://pretalx.com/juliacon-2026/speaker/8N3ZHH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/URPF3H/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/URPF3H/", "attachments": []}], "Muschel \u2014 N3": [{"guid": "1e3d73b6-b25c-57a4-bd98-5b43dcfcbf18", "code": "H9MULV", "id": 92878, "logo": "https://pretalx.com/media/juliacon-2026/submissions/H9MULV/image_5rHV6li.webp", "date": "2026-08-14T10:00:00+02:00", "start": "10:00", "end": "2026-08-14T10:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92878-optimising-quantum-control-systems-application-to-nv-centres", "url": "https://pretalx.com/juliacon-2026/talk/H9MULV/", "title": "Optimising Quantum Control Systems: Application to NV Centres", "subtitle": "", "track": "Quantum Mini", "type": "Short talk", "language": "en", "abstract": "We report on recent progress in the numerical optimisation of quantum control systems using [OptimalControl.jl](https://github.com/control-toolbox/OptimalControl.jl). Although the package is designed to optimise general control systems governed by ordinary differential equations, it naturally accommodates quantum problems described by finite-dimensional Schr\u00f6dinger equations evolving on Lie groups \u2014 specifically, bilinear dynamical systems whose state trajectories lie on unitary groups and are expressed compactly in terms of tensor products of complex matrices.\n\nA well-established Julia ecosystem for quantum optimal control already exists, with packages such as `QuantumControl.jl`, `Krotov.jl`, and `GRAPE.jl` providing mature, quantum-tailored implementations of the GRAPE and Krotov algorithms. These methods are effective for a broad class of problems and can accommodate extensions such as free final time or path constraints on controls and states, typically via penalisation of the cost functional. However, penalisation-based approaches offer no rigorous guarantee of constraint satisfaction and can introduce significant ill-conditioning. Our motivation is complementary: to leverage state-of-the-art nonlinear programming solvers that treat such constraints directly, as genuine algebraic equalities and inequalities arising from the transcription of the continuous-time optimal control problem \u2014 including additional optimisation variables such as free final time or parameters of the system.\n\n`OptimalControl.jl` offers a high-level, expressive modelling interface that allows users to specify dynamics, objectives, and constraints in a form close to mathematical notation, with no compromise on performance. Problems are transcribed via direct methods into large-scale sparse nonlinear programmes, which are solved using interior-point methods on both CPU and GPU, exploiting automatic differentiation through `ExaModels.jl` and `MadNLP.jl`. Crucially, the framework also supports the combination of direct and indirect methods: direct transcription is used first to identify the qualitative structure of the optimal solution, after which indirect shooting methods \u2014 based on the Pontryagin Maximum Principle \u2014 can be applied to refine the solution to arbitrary numerical precision.\n\nWe present preliminary results on the optimisation of small quantum systems modelling nitrogen-vacancy (NV) centres in diamond. These systems, comprising an electron spin coupled to one or more nuclear spins via hyperfine interaction, are naturally described with bilinear dynamics driven by bounded microwave controls. The combination of hard amplitude constraints, partial controllability (nuclear spins are driven only indirectly through the electron spin), and the need for various costs functionals for gate synthesis makes NV centres a compelling benchmark for our approach. We discuss the formulation of these problems within `OptimalControl.jl`, and compare the results and computational performance against existing quantum-specific methods.", "description": "Nitrogen-vacancy (NV) centres, and more generally colour centres in diamond, are promising physical platforms for quantum sensing and quantum information processing, owing to the long coherence times of the associated spin degrees of freedom, even at room temperature, and to the ability to initialise and read out spin states optically. The results presented in this talk are grounded in real experimental activity at the Institut Carnot de Bourgogne (ICB, Universit\u00e9 Bourgogne Europe), where colour centres in diamond are actively investigated. The minimal but physically meaningful model we consider consists of two coupled spin-1/2 particles \u2014 one electron spin and one nuclear spin \u2014 with microwave control acting exclusively on the electron spin. \n\nAfter applying the rotating wave approximation, the system reduces to a bilinear control system evolving on SU(4), with a two-dimensional control input constrained to a disc, $u_1^2(t) + u_2^2(t) \\leq u^2_{\\max}$. A controllability analysis carried out via Lie bracket computations and rank conditions on the Lie algebra generated by the drift and control vector fields reveals that, depending on the hyperfine coupling parameters, not every gate in SU(4) is reachable \u2014 a direct consequence of the indirect nature of the nuclear spin control. When the desired target gate lies outside the attainable set, the optimisation is reformulated as fidelity maximisation towards the closest reachable gate. For the evolution times of interest, the dynamics are sufficiently smooth that no specialised exponential integrators are required, and we test several standard numerical integration schemes within the direct transcription framework. The high-level problem modelling, including the specification of dynamics directly in terms of matrix Lie group structure and Kronecker products of complex matrices, is enabled by a new extension to LinearAlgebra within ExaModels.jl developed by the Exanauts team, which retains full compatibility with automatic differentiation.\n\nThe resulting nonlinear programmes are solved by interior-point methods from the MadSuite (MadNLP.jl), both on CPU and on GPU via CUDSS.jl. The direct transcription solution additionally serves as a warm start for an indirect shooting method obtained by applying the Pontryagin Maximum Principle to the bilinear system on SU(4), allowing the qualitative structure of the optimal control to be captured first and then refined to arbitrary numerical precision. The talk will cover the formulation within OptimalControl.jl, a comparison of direct and indirect approaches, and GPU performance benchmarks.\n\nThe joint project CONV (Control of NV-centres) between Universit\u00e9 C\u00f4te d'Azur Math lab and Institut Carnot of Universit\u00e9 Bourgogne Europe receives financial support from the CNRS through the MITI interdisciplinary program. J.-B. Caillau is also supported by a FACCTS grant of the France-Chicago center.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZDRATR", "name": "Jean-Baptiste Caillau", "avatar": null, "biography": "Professor of applied math at Universit\u00e9 C\u00f4te d\u2019Azur, CNRS, Inria, LJAD\n\nScientific interests - Optimisation and control: geometry, algorithms, applications\n\nhttps://caillau.perso.math.cnrs.fr", "public_name": "Jean-Baptiste Caillau", "guid": "f67dfc52-7bea-53cd-8b9b-e0e1f42dc483", "url": "https://pretalx.com/juliacon-2026/speaker/ZDRATR/"}, {"code": "Y9AHQY", "name": "David Tinoco", "avatar": null, "biography": null, "public_name": "David Tinoco", "guid": "f20c34c6-d696-5f75-841c-6edb33fac4ec", "url": "https://pretalx.com/juliacon-2026/speaker/Y9AHQY/"}], "links": [{"title": "Tinoco, D.; Babin, C.; Beschastnyi, I.; Caillau, J.-B.; Sugny, D. Control of an NV center as a two-qubit system (2026). HAL preprint no. 05404999", "url": "https://hal.science/hal-05404999", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/H9MULV/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/H9MULV/", "attachments": [{"title": "flyer-H9MULV", "url": "/media/juliacon-2026/submissions/H9MULV/resources/H9MULV_G0mMMLV.png", "type": "related"}]}, {"guid": "27f952f2-74c7-5c5c-b4db-4f1e992d3536", "code": "7PP38R", "id": 92937, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7PP38R/image_BvRVfS5.webp", "date": "2026-08-14T10:15:00+02:00", "start": "10:15", "end": "2026-08-14T10:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92937-quantum-many-body-simulations-with-paulistrings-jl", "url": "https://pretalx.com/juliacon-2026/talk/7PP38R/", "title": "Quantum many-body simulations with PauliStrings.jl", "subtitle": "", "track": "Quantum Mini", "type": "Short talk", "language": "en", "abstract": "I will present PauliStrings.jl, a package for quantum many-body simulations, which performs fast operations on the Pauli group by encoding Pauli strings in binary. When combined with various truncation methods, this representation provides performance advantages for solving certain kinds of problems. \nPauliStrings.jl also allows for symbolic calculations, is a natural platform to take advantage of symmetries and is a fun and pedagogical tool to visualize quantum algebra.", "description": "PauliStrings.jl provides a competitive platform for studying quantum many-body dynamics by representing operators as sets of Pauli strings encoded in binary.\nWe have shown that this encoding can be advantageous for numerical simulation of quantum dynamics. The advantage arises from two key features: (i) The Pauli string algebra is encoded in low-level logic operations on integers, making it very efficient to numerically store and multiply strings together. (ii) Operators can be systematically truncated to some precision by discarding strings with negligibly small weight. This allows one to keep the number of strings manageable at the cost of some incurred error.\nWe will present examples of this for Heisenberg time evolution and Krylov subspace expansion through the recursion method. One of the important strengths of Pauli strings is that they provide a natural framework to take advantage of noise to make simulations tractable. In addition, though tensor network methods quickly break down with increasing long-range entanglement, some systems with this type of entanglement can still be decomposed into a small number of strings, making Pauli strings more efficient for these kinds of systems. Furthermore, Pauli string methods are not as limited in spatial dimension and geometry, and arbitrary geometries are easy to implement.\nIn this talk I will focus on new features of PauliStrings.jl : symbolic calculations and exploitation of translation symmetries.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NQZKBC", "name": "Nicolas Loizeau", "avatar": "https://pretalx.com/media/avatars/ZX3HPW_kVEn4DD.webp", "biography": "Postdoc researcher at Niels Bohr Institute, Copenhagen", "public_name": "Nicolas Loizeau", "guid": "8a38139d-9743-5dc3-8e3c-3d0735eb9512", "url": "https://pretalx.com/juliacon-2026/speaker/NQZKBC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7PP38R/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7PP38R/", "attachments": [{"title": "flyer-7PP38R", "url": "/media/juliacon-2026/submissions/7PP38R/resources/7PP38R_hc2MgL5.png", "type": "related"}]}, {"guid": "bcd08d4d-0b39-5004-87da-8115336e1075", "code": "NHJH7G", "id": 93479, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NHJH7G/image_inrJ0aZ.webp", "date": "2026-08-14T10:30:00+02:00", "start": "10:30", "end": "2026-08-14T11:00:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-93479-piccolo-jl-1-x-a-unified-agent-enabled-quantum-control-package", "url": "https://pretalx.com/juliacon-2026/talk/NHJH7G/", "title": "Piccolo.jl 1.x: a unified, agent-enabled quantum control package", "subtitle": "", "track": "Quantum Mini", "type": "Long talk", "language": "en", "abstract": "The name means small, so we made it smaller. Piccolo.jl 1.0 consolidates five quantum optimal control packages into one -- one language, one package, just `using Piccolo`. A unified Julia codebase that AI coding agents can thrive in -- accelerating feature development, performance work, and letting users go from system parameters to optimized pulses naturally. We demonstrate real-world impact through robust control theory (arXiv:2602.10349) and experimental studies of universal dynamics in Rydberg arrays (arXiv:2508.19075).", "description": "[Piccolo.jl](https://github.com/harmoniqs/Piccolo.jl) is an open-source framework for quantum optimal control via direct trajectory optimization, developed by [Harmoniqs](https://harmoniqs.ai).\n\n**One package to rule them all.** Previously, Piccolo was a meta-package re-exporting QuantumCollocation.jl, PiccoloQuantumObjects.jl, PiccoloPlots.jl, NamedTrajectories.jl, and TrajectoryIndexingUtils.jl. Users had to navigate five repos, five sets of docs, and version compatibility across all of them. For 1.0, we pulled core functionality into Piccolo.jl itself, keeping only truly independent libraries ([NamedTrajectories.jl](https://github.com/harmoniqs/NamedTrajectories.jl), [DirectTrajOpt.jl](https://github.com/harmoniqs/DirectTrajOpt.jl)) as separate packages. The result: `using Piccolo` gives you everything from Hamiltonians to plotting.\n\n**Agent-enabled by design.** Julia's single-language stack -- where the high-level API and the performance-critical internals are the same language -- turns out to be a superpower for AI-assisted development. An LLM reading Piccolo source code doesn't need to context-switch between Python glue and C++/Fortran kernels. We leaned into this by shipping structured context files and building reusable agent skills for common quantum control workflows: problem setup, physics references, testing, and demo generation. On the development side, this accelerates feature implementation and performance optimization across the stack. On the user side, coding agents can go from a gate specification to an optimized pulse with minimal human steering -- making quantum optimal control more accessible to experimentalists who think in terms of physics, not software.\n\n**Real-world impact.** Piccolo's direct optimal control framework underpins two recent results. First, [Kamen et al.](https://arxiv.org/abs/2602.10349) position robustness as a first-class objective within direct, constrained optimal control, introducing a critical discretization correction to toggling-frame robustness estimators and demonstrating precise, physics-informed robust pulse design. Second, [Hu et al.](https://arxiv.org/abs/2508.19075) use the framework to experimentally demonstrate universal dynamics on Rydberg-atom arrays, synthesizing three-body interactions and topological dynamics under global control constraints.\n\nIn this talk, we demo the unified package, show how agent skills accelerate research workflows, and discuss what we have learned about designing Julia packages for the age of AI-assisted scientific computing.\n\n- [Package](https://github.com/harmoniqs/Piccolo.jl)\n- [Documentation](https://docs.harmoniqs.co/Piccolo/dev/)", "recording_license": "", "do_not_record": false, "persons": [{"code": "FV7D7C", "name": "Aaron Trowbridge", "avatar": null, "biography": "Currently co-founder and CEO at Harmoniqs, a startup building Julia-based quantum optimal control and calibration software. Previously, a research associate in the Robotics Institute at Carnegie Mellon University. An avid reader, climber, runner, and Bialetti coffee drinker.", "public_name": "Aaron Trowbridge", "guid": "8298c138-9e35-55b4-888c-3b526de617af", "url": "https://pretalx.com/juliacon-2026/speaker/FV7D7C/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NHJH7G/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NHJH7G/", "attachments": []}, {"guid": "59c7e971-1da0-5dbf-9f1d-20d966c164e1", "code": "MJP9RJ", "id": 92557, "logo": "https://pretalx.com/media/juliacon-2026/submissions/MJP9RJ/image_3wwnJHY.webp", "date": "2026-08-14T11:15:00+02:00", "start": "11:15", "end": "2026-08-14T11:45:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92557-qruise-toolset-differentiable-quantum-simulation-toolbox", "url": "https://pretalx.com/juliacon-2026/talk/MJP9RJ/", "title": "qruise-toolset: differentiable quantum simulation toolbox", "subtitle": "", "track": "Quantum Mini", "type": "Long talk", "language": "en", "abstract": "We introduce `qruise-toolset`, a differentiable quantum simulation toolbox with a Python interface and a Julia simulation backend.  The toolbox enables researchers and companies to build faithful digital twin models of their hardware for fast calibration and prototyping via closed-loop quantum optimal control strategies at the pulse level. Moreover, the realistic behaviour of the control stack and the pulse delivery via the signal chain is an indispensable part of the toolbox, allowing the user to explore the limitations of the control stack components.", "description": "`qruise-toolset` is a fully differentiable simulation toolbox for quantum simulation and the quantum optimal control problem. It enables fast prototyping and optimisation of the hardware of interest by building a digital twin of the system. The automatic differentiation framework in `qruise-toolset` is provided via LLVM intermediate representation of the quantum simulation problem, which is then ingested by `Enzyme.jl`. This allows the user to benefit from the performance Julia JIT compilation offers and still stick to the convenience that the Python programming language offers. This approach uplifts the requirement of using automatic differentiation packages such as PyTorch, TensorFlow or JAX that are mostly suited for deep learning neural network architectures.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NRYN7M", "name": "Yousof Mardoukhi", "avatar": null, "biography": "Yousof is a scientific software developer at Qruise GmbH.", "public_name": "Yousof Mardoukhi", "guid": "180fb205-c266-54fc-b0eb-5ecd580da904", "url": "https://pretalx.com/juliacon-2026/speaker/NRYN7M/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/MJP9RJ/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/MJP9RJ/", "attachments": []}, {"guid": "075f6862-7914-57ec-8d49-fc95fe4e88fa", "code": "M3HL7P", "id": 92868, "logo": "https://pretalx.com/media/juliacon-2026/submissions/M3HL7P/image_l5S43Dt.webp", "date": "2026-08-14T11:45:00+02:00", "start": "11:45", "end": "2026-08-14T12:15:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92868-quantum-hamlets-distributed-compilation-of-large-algorithmic-graph-states", "url": "https://pretalx.com/juliacon-2026/talk/M3HL7P/", "title": "Quantum Hamlets: Distributed Compilation of Large Algorithmic Graph States", "subtitle": "", "track": "Quantum Mini", "type": "Long talk", "language": "en", "abstract": "We investigate the problem of partitioning graph states for distributed quantum computing. Graph states are a way of representing certain quantum entangled states as graphs. Due to the nature of entanglement, it's far better to partition graphs to minimize the size of the maximum matchings between partitions rather than the number of edges, as traditional algorithms do. We provide an algorithm for this in our Julia software package for graph state partitioning evaluation, QuantumHamlets.jl.", "description": "For people without quantum information science (QIS) background, and mainly interested in graph algorithms, we heuristically address the problem of balanced graph k partitioning with the objective of minimizing the sizes of the maximum matchings between partitions, rather than the number of edges cut. By this metric of minimizing matching sizes, we outperform nearly all existing algorithms for edge minimization k partition on most input graphs.\n\nFor those with QIS background:\nWe investigate the problem of compiling the generation of graph states to arbitrarily many distributed homogeneous quantum processing units (QPUs). To do so, we provide a protocol we term vertex cover grafting (VCG) for graph state generation, and design a heuristic algorithm we term BURY for the balanced partitioning of the graph state between the QPUs in order to reduce the number of required long-range Bell pairs utilized by VCG.\nIn this effort, we consider the problem of balanced k graph partitioning with the objective of minimizing the sizes of the maximum matchings between partitions, rather than the number of edges cut. We show that our heuristic algorithm, BURY, requires fewer Bell pairs to generate most graph states than state-of-the-art k partition algorithms. Furthermore, we show that BURY reduces the cut-rank of the partitions, demonstrating that the partitioning found by our algorithm is likely to minimize the Bell pair utilization of any distributed graph state generation protocol. Additionally, we discuss how one could straightforwardly apply our methods to the dynamic case where the graph state generation and measurement are performed concurrently. Our study of the balanced minimum maximum matching k-partition problem and the heuristic algorithm we design provides a scalable foundation for reducing quantum network overhead for distributed measurement-based quantum computation (MBQC), as well as any scheme where distributed graph state generation is desired.\n\nFurthermore, this talk will attempt to introduce all QIS concepts needed for understanding, assuming the audience has a proper computer science background. Specifically, one goal of this talk is to explain graph states to the subset of the audience that is interested in graph algorithms but may be unaware of graph problems in QIS. We hope this can foster further development of algorithms for graph states.", "recording_license": "", "do_not_record": false, "persons": [{"code": "MGWY73", "name": "Anthony Micciche", "avatar": "https://pretalx.com/media/avatars/T7BEZP_no6pNCh.webp", "biography": "Anthony Micciche is a PhD student at the University of Massachusetts Amherst. He studies topics related to quantum computation, quantum error correction and fault tolerance, and quantum circuit compilation.", "public_name": "Anthony Micciche", "guid": "5e7f2f7c-255a-5e7e-bd21-af6b952cd044", "url": "https://pretalx.com/juliacon-2026/speaker/MGWY73/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/M3HL7P/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/M3HL7P/", "attachments": []}, {"guid": "5903792d-be41-51b1-8602-ac476707e517", "code": "F3RNYU", "id": 92121, "logo": "https://pretalx.com/media/juliacon-2026/submissions/F3RNYU/image_9SppyGD.webp", "date": "2026-08-14T12:15:00+02:00", "start": "12:15", "end": "2026-08-14T12:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92121-multivariate-multicycle-codes-for-complete-single-shot-decoding", "url": "https://pretalx.com/juliacon-2026/talk/F3RNYU/", "title": "Multivariate Multicycle codes for Complete Single-shot decoding", "subtitle": "", "track": "Quantum Mini", "type": "Short talk", "language": "en", "abstract": "We introduce multivariate multicycle (MM) codes, a new family of quantum error correcting (QEC) codes that unifies and generalizes many of the established families of QEC codes and possess record-breaking capabilities by measure of confinement for single-shot decoding.  Our work is enabled by Open Source Computer Algebra Research (OSCAR.jl) which provides capabilities for computations in commutative and homological algebra and QuantumClifford.jl, an open source package for stabilizer tableaux algebra which provides tools for working with graph states and vast array of error correction codes and capabilities.", "description": "The property of  single-shot decoding is a crucial requirement for low-overhead error correction and it is one of the hallmarks of fault-tolerant quantum error correction, however very few codes possess this property. The concept of single-shot decoding was recognized as important early in the history of our field [Bombin2015], but significant progress did not happen until recently [Campbell2019], with the introduction of \u201cmetachecks\u201d, i.e. \u201cchecks on checks\u201d that are solely meant to detect measurement errors in the syndrome itself. Only in 2020 [Quintavalle2020], a universal measure of the \u201csingle-shot\u201d capability was well defined, namely \u201cthe confinement profile\u201d, which can be used to compare the resilience of a code to measurement errors (the main impediment to single-shot performance). By the dawn of 2026, only a few families of \u201ccomplete single-shot\u201d QEC codes exist, namely abelian-multicycle codes (AMCs) and 4D homological product codes that include 4D toric and 4D surface codes. The code construction we present changes this by providing a very simple principled technique for generating metachecks and gives us instances of codes with record breaking confinement.\n\nOur Multivariate Multicycle codes are CSS codes defined from length-t chain complexes with t \u2265 4. The chief advantage of these codes is that they possess metachecks and high confinement that permit complete single-shot decoding, while also having additional algebraic structure that might enable logical non-Clifford gates. We offer a framework that facilitates the construction of long-length chain complexes through the use of Koszul complex. In particular, obtaining explicit boundary maps (parity check and metacheck matrices) is particularly straightforward in our approach. This simple but very general parameterization of codes permitted us to efficiently perform a numerical search, where we identify several MM code candidates that demonstrate these capabilities at high rates and high code distances. Moreover, many known families of codes can be expressed simply as special cases of the construction we have discovered. To put this into perspective, n-dimensional Toric, AMC, bivariate bicycle, trivariate tricycle, symmetric cyclic hypergraph product (C2), repeated cyclic hypergraph product (CxR), multivariate bicycle, generalized bicycle, abelian two-block group algebra, Haah\u2019s cubic codes, and \u201csquare\u201d La-Cross codes are easily recovered as subfamilies of our codes. Examples of new codes with parameters [[n, k, d]] include [[96, 12, 8]], [[96, 44, 4]] [[144, 40, 4]], [[216, 12, 12]], [[360, 30, 6]], [[384, 80, 4]], [[486, 24, 12]], [[486, 66, 9]] and [[648, 60, 9]]. Notably, our codes achieve confinement profiles that surpass all known single-shot decodable quantum CSS codes of practical blocksize.\n\n\n[Bombin2015]: Bomb\u00edn, H., 2015. Single-shot fault-tolerant quantum error correction. Physical Review X, 5(3), p.031043.\n[Campbell2019]: Campbell, E.T., 2019. A theory of single-shot error correction for adversarial noise. Quantum Science and Technology, 4(2), p.025006.\n[Quintavalle2020]: Quintavalle, A.O., Vasmer, M., Roffe, J. and Campbell, E.T., 2021. Single-shot error correction of three-dimensional homological product codes. PRX Quantum, 2(2), p.020340.", "recording_license": "", "do_not_record": false, "persons": [{"code": "S3DCCX", "name": "Feroz Ahmed Mian", "avatar": "https://pretalx.com/media/avatars/XC38YS_VXUgNwE.webp", "biography": "Feroz Ahmed Mian is a PhD student at the University of Massachusetts Amherst. He studies quantum error correction and fault-tolerance in quantum computation. \n\nHe is a prolific developer of quantum error-correcting codes, implementing them in QuantumClifford.jl and QuantumExpanders.jl using OSCAR.jl.", "public_name": "Feroz Ahmed Mian", "guid": "89b8d977-3aee-5ed5-8be7-f07da4f29131", "url": "https://pretalx.com/juliacon-2026/speaker/S3DCCX/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/F3RNYU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/F3RNYU/", "attachments": []}, {"guid": "3e80ace8-1b40-5392-acd6-310f772244ed", "code": "RN8YTY", "id": 92817, "logo": "https://pretalx.com/media/juliacon-2026/submissions/RN8YTY/image_6nnFfsK.webp", "date": "2026-08-14T12:30:00+02:00", "start": "12:30", "end": "2026-08-14T12:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92817-quantum-programming-in-ordinary-julia-assisted-by-llm-agents", "url": "https://pretalx.com/juliacon-2026/talk/RN8YTY/", "title": "Quantum programming in ordinary Julia, assisted by LLM agents", "subtitle": "", "track": "Quantum Mini", "type": "Short talk", "language": "en", "abstract": "Every textbook quantum algorithm begins with \"given an oracle for f\", and every quantum programming framework answers with a circuit-drawing API and good luck. In an effort to address this challenge, I have been experimenting with an end-to-end Julia toolchain that closes the gap between ordinary code and quantum programs. Given a plain Julia function, no macros, no special types, still callable, Bennett.jl extracts its LLVM IR and compiles it into a provably reversible circuit, which Sturm.jl, a quantum programming language where the quantum\u2013classical boundary is a type boundary, then calls as an oracle. I describe the design choices behind this pipeline across three packages: Sturm.jl (quantum programs that read like ordinary Julia), Bennett.jl (the reversible compiler), and BennettVM.jl (a reversible interpreter for programs no fixed circuit can express). In practice, the compiler routinely handles code it has never seen, including Base.sin, unchanged, bit-faithful to 1 ulp. LLM coding agents built most of this system, automating workflows that previously would have involved much yak shaving and frustration. As a consequence, time-poor persons with little software engineering expertise (such as myself!) can now build the tools they wish existed. I will directly address practical challenges including keeping agent-generated code honest (physics-level law tests caught real bugs that output statistics missed), the true cost of compiled circuits against hand-optimized ones, and the hard boundary where unbounded loops stop being oracles.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "CAGL3K", "name": "Tobias J. Osborne", "avatar": "https://pretalx.com/media/avatars/UA88UU_udI0XlT.webp", "biography": "I am a theoretical physicist.\n\nI am passionate about diversity in science and quantum mechanics.", "public_name": "Tobias J. Osborne", "guid": "efdb5537-b523-597d-9655-9d7272a4e713", "url": "https://pretalx.com/juliacon-2026/speaker/CAGL3K/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/RN8YTY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/RN8YTY/", "attachments": []}, {"guid": "d784b163-c09a-5f5a-a867-544712a79986", "code": "JA8JFE", "id": 92924, "logo": "https://pretalx.com/media/juliacon-2026/submissions/JA8JFE/image_DPHzEeS.webp", "date": "2026-08-14T12:45:00+02:00", "start": "12:45", "end": "2026-08-14T13:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92924-fast-and-reliable-quantum-state-tomography-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/JA8JFE/", "title": "Fast and reliable quantum state tomography in Julia", "subtitle": "", "track": "Quantum Mini", "type": "Short talk", "language": "en", "abstract": "Quantum computing, communication, and sensing technologies rely on precise knowledge of quantum states. Quantum states cannot be directly measured. Quantum state tomography (QST) reconstructs these states from indirect measurements, similar to how CT imaging combines multiple 2D projections into a 3D model. In QST, the goal is to minimize the statistical discrepancy between experimentally observed data and predictions from quantum theory. This optimization problem is nonlinear and subject to physical constraints on the states. We present a Julia implementation that efficiently and robustly minimizes this statistical distance while enforcing these constraints. Our work provides a practical, extensible toolkit for QST and a comparative guide to choosing optimizer based on accuracy, speed, and robustness.", "description": "In quantum mechanics, the full quantum state is represented by a density matrix. It cannot be directly measured. Only partial information, obtained through different measurement is accessible. Quantum state tomography (QST) reconstructs the complete state from these measurement that yield a measurement statistics. Quantum theory provides predicted statistics for any assumed state, and QST identifies the state whose predicted statistics best match the observed data by minimizing a suitable statistical distance.\nWe present a Julia implementation of a QST pipeline that reconstructs density matrices while ensuring physicality: each density matrix must be positive semi-definite and have unit trace. Our approach minimizes a residual between predicted and measured statistics and supports both least-squares and log-likelihood formulations. Physical constraints are enforced through Cholesky parameterization or projection methods. We benchmark on simulated data several optimization strategies, like projected Gauss\u2013Newton, L-BFGS, and trust-region solvers. The benchmarks evaluate reconstruction accuracy, scaling with the number of qubits, convergence speed, and stability, offering practical guidance for selecting optimizer in QST applications.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7E9R9A", "name": "Fabian M\u00fcller", "avatar": null, "biography": "During my previous studies, I theoretically investigated and optimized quantum optical and measurement systems using tools from quantum information theory. Since 2025, I have been a PhD student in physics at Charles University in Prague, where my theoretical research focuses on fast and reliable quantum state tomography. My work emphasizes developing and implementing improved methods that enable efficient tomography even for high\u2011dimensional systems.", "public_name": "Fabian M\u00fcller", "guid": "83ffd6e2-dcab-5996-9e48-2d7cb181b3ef", "url": "https://pretalx.com/juliacon-2026/speaker/7E9R9A/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/JA8JFE/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/JA8JFE/", "attachments": []}, {"guid": "0b8a5d9c-6706-5ecf-86b0-9902e8e84a07", "code": "NEWC8H", "id": 92864, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NEWC8H/image_Emedxrb.webp", "date": "2026-08-14T14:30:00+02:00", "start": "14:30", "end": "2026-08-14T14:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92864-building-a-composable-julia-ecosystem-for-infectious-disease-modelling-a-roadmap-challenges-and-questions", "url": "https://pretalx.com/juliacon-2026/talk/NEWC8H/", "title": "Building a composable Julia ecosystem for infectious disease modelling: a roadmap, challenges, and questions", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Infectious disease models that integrate multiple data sources provide better evidence for outbreak response than chains of separate models, but building them is slow and requires expertise across domains.\nComposable modelling, where validated components combine into joint models that properly propagate uncertainty, addresses this but requires an ecosystem of reusable infectious disease model components.\nWe believe Julia is the best language for this ecosystem due to its type system, multiple dispatch, automatic differentiation support, and existing scientific computing infrastructure ([SciML](https://sciml.ai), [Turing.jl](https://turinglang.org), [Distributions.jl](https://github.com/JuliaStats/Distributions.jl)), which provide the foundations composable modelling needs.\nIn this talk, we present the [EpiAware](https://github.com/EpiAware) roadmap for creating and sustaining that ecosystem, our current progress, and our questions for the Julia community.\n\nIn R, we have built the [epinowcast](https://github.com/epinowcast) ecosystem (packages, community forum, seminar series) and developed several other widely used packages including [EpiNow2](https://github.com/epiforecasts/EpiNow2) and [scoringutils](https://github.com/epiforecasts/scoringutils).\nWe want to create something equivalent in Julia: a domain-focused ecosystem in the mould of [SciML](https://sciml.ai) or [Turing.jl](https://turinglang.org), with the community infrastructure of [rOpenSci](https://ropensci.org) and the domain specificity of [SpeedyWeather.jl](https://github.com/SpeedyWeather/SpeedyWeather.jl).\n\nSo far, we have [CensoredDistributions.jl](https://github.com/EpiAware/CensoredDistributions.jl), which handles common biases in epidemiological delay distributions, and an R interface prototype ([EpiAwareR](https://github.com/sbfnk/EpiAwareR)).\nWe initially plan to implement packages covering distribution extensions for epidemiological use, delay and generation time estimation, disease dynamics components, and forecast evaluation, alongside a centralised documentation site.\n\nAt the package level, we need to answer questions about what makes a good Julia package in our ecosystem: consistent documentation via [DocStringExtensions](https://github.com/JuliaDocs/DocStringExtensions.jl) and [DocumenterCiterepress](https://github.com/JuliaDocs/DocumenterCitations.jl), robust testing with [Aqua.jl](https://github.com/JuliaTesting/Aqua.jl) and [JET.jl](https://github.com/aviatesk/JET.jl), automatic differentiation backend testing via [DifferentiationInterfaceTest](https://github.com/gdalle/DifferentiationInterface.jl), and where we need package extensions (e.g. for [Turing.jl](https://turinglang.org) integration).\n\nAt the ecosystem level, we need to understand how to manage releases so that package versions work together, how to run reverse dependency checks before publishing, how to set up shared CI and centralised documentation across many packages, and how to help users understand which automatic differentiation backends are compatible when they combine multiple packages.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "SGPNJN", "name": "Sam Abbott", "avatar": "https://pretalx.com/media/avatars/SBLN8B_L1KfDIw.webp", "biography": "I am an Assistant Professor at the London School of Hygiene & Tropical Medicine. I did my PhD in the optimal usage of the BCG vaccine, transitioning to work on real-time modelling of infectious disease outbreaks on the 3rd of January 2020. Four days later, I switched to work on what was then known as 2019-NCoV. I did early work on the size and scale of the initial outbreak, tracking transmissions in different countries, and exploring the potential role of different interventions. Throughout the pandemic, I ran a dashboard that was used by over a million people. I also provided estimates, forecasts, and analyses weekly to the UK government advisory bodies. I developed the tools and methods we used into open source software and these were used by upwards of 30 public health agencies around the world. I have continued to work in this area with a focus on improving tools and methods used both in research and in public health practice. I have recently transitioned to Julia for my work and am exploring how to propogate Julia based tools to the users of our current tooling and to the wider infectious disease modelling community.", "public_name": "Sam Abbott", "guid": "7aa3797d-932b-542d-ba81-d1d2b10724c0", "url": "https://pretalx.com/juliacon-2026/speaker/SGPNJN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NEWC8H/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NEWC8H/", "attachments": []}, {"guid": "6ba47cac-0592-5aea-8912-cd90834d9570", "code": "ZUMSFD", "id": 92853, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ZUMSFD/image_1MIM8cY.webp", "date": "2026-08-14T14:45:00+02:00", "start": "14:45", "end": "2026-08-14T15:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92853-estimating-epidemiological-delay-distributions-from-r-stan-to-julia", "url": "https://pretalx.com/juliacon-2026/talk/ZUMSFD/", "title": "Estimating epidemiological delay distributions: from R/Stan to Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Delay distributions describe the time between epidemiological events, such as infection to symptom onset or symptom onset to hospitalisation.\nEstimating these distributions from outbreak data is difficult because both the primary event (e.g. infection) and the secondary event (e.g. symptom onset) are usually only known to have occurred within a time window, such as a day.\nReal-time outbreak data is also often right-truncated as longer delays have not yet been observed.\nIgnoring double interval censoring and truncation biases parameter estimates which are then used for forecasting and transmission modelling.\n\nAdjusting distributions for primary event censoring addresses this by integrating the delay CDF over the primary event window, weighted by the density of when, within the window, the event occurred.\nThis can then be combined with truncation and secondary interval-censoring adjustments to produce a double-interval-censored and right-truncation-adjusted distribution.\n\nIn this talk, we present [CensoredDistributions.jl](https://censoreddistributions.epiaware.org), which implements these adjustments as `primary_censored`, `interval_censored`, and `double_interval_censored`, composable [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) wrappers.\nMultiple dispatch selects closed-form CDFs for delay and primary event distribution pairs where these are available, and falls back to numerical integration otherwise.\nWe demo the package standalone and with [Turing.jl](https://turinglang.org/) for parameter estimation.\n\nWe then compare to [primarycensored](https://primarycensored.epinowcast.org), our equivalent R package, which also ships a duplicate set of [Stan](https://mc-stan.org/) functions so users can fit models in either language.\nMaintaining two parallel implementations required reimplementing distribution functions in Stan, building tooling to vendor Stan code into downstream projects, and replacing types with integer distribution identifiers.\nStan's integral solver was also unstable for this problem, so we had to recast it as an ODE.\nJulia's multiple dispatch and ecosystem composability eliminates all of this.\n\nWe then summarise our plans to build a composed Julia version of our [epidist](https://epidist.epinowcast.org) R package, using CensoredDistributions.jl as a foundation with Turing.jl submodels for partially pooled and flexible delay estimation.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "SGPNJN", "name": "Sam Abbott", "avatar": "https://pretalx.com/media/avatars/SBLN8B_L1KfDIw.webp", "biography": "I am an Assistant Professor at the London School of Hygiene & Tropical Medicine. I did my PhD in the optimal usage of the BCG vaccine, transitioning to work on real-time modelling of infectious disease outbreaks on the 3rd of January 2020. Four days later, I switched to work on what was then known as 2019-NCoV. I did early work on the size and scale of the initial outbreak, tracking transmissions in different countries, and exploring the potential role of different interventions. Throughout the pandemic, I ran a dashboard that was used by over a million people. I also provided estimates, forecasts, and analyses weekly to the UK government advisory bodies. I developed the tools and methods we used into open source software and these were used by upwards of 30 public health agencies around the world. I have continued to work in this area with a focus on improving tools and methods used both in research and in public health practice. I have recently transitioned to Julia for my work and am exploring how to propogate Julia based tools to the users of our current tooling and to the wider infectious disease modelling community.", "public_name": "Sam Abbott", "guid": "7aa3797d-932b-542d-ba81-d1d2b10724c0", "url": "https://pretalx.com/juliacon-2026/speaker/SGPNJN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ZUMSFD/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ZUMSFD/", "attachments": []}, {"guid": "7eeabeb9-746f-54f5-8702-50b6b13dba22", "code": "7VFDJF", "id": 92283, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7VFDJF/image_6fgn8zB.webp", "date": "2026-08-14T15:00:00+02:00", "start": "15:00", "end": "2026-08-14T15:30:00+02:00", "duration": "00:30", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92283-signature-tensors-in-oscar", "url": "https://pretalx.com/juliacon-2026/talk/7VFDJF/", "title": "Signature Tensors in OSCAR", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "We introduce SignatureTensors.jl, a new package for computing signature tensors of paths and membranes.  By leveraging the symbolic computation framework provided by OSCAR, the package implements flexible algebraic structures for truncated \ntensor signatures, and provides efficient constructors for path signatures. Furthermore, it features implementations of Lie group barycenters and optimized algorithms for learning from signature tensors.  We illustrate the package\u2019s versatility with practical applications in geometric statistics, feature extraction, \nspline interpolation, and computational algebraic geometry.", "description": "Path signatures are fundamental objects in rough path theory and serve as a noncommutative feature that captures the essential geometry of sequential data. Their utility has expanded across diverse fields including mathematical finance, machine learning, or topological data analysis. \nRecently, a tangible link to algebraic geometry was established through the study of signature varieties associated with specific families of paths. \nThis viewpoint proved particularly useful for studying the problem of learning paths from their signature tensors. \n\nFor this purpose, a practical and easily extendable package within a \nmodern computer algebra system is required, providing access to multivariate arrays, Lie theory, \nnon-commutative polynomials, Gr\u00f6bner bases, and other structures. We introduce SignatureTensors.jl, a new package that leverages the symbolic computation capabilities of OSCAR, a modern open source computer algebra system written in Julia. \nThe package provides a general framework for computing and manipulating path signatures using algebraic \nand symbolic methods while seamlessly interacting with the OSCAR ecosystem.\n\nIn this talk, we will provide a brief introduction to signatures and their implementation within our package. We present efficient algorithms to compute signatures for (piecewise) polynomial paths. Furthermore, we provide an implementation of the recently introduced two-parameter signature of membranes. The package supports several operations on signatures such as group multiplication, the logarithm, or the geometric group barycenter. A key advantage is that our constructions work over arbitrary OSCAR rings and thus combine with common symbolic computation techniques.\n\nWe conclude with two illustrative examples of the package in action. First, we present efficient tensor learning arising in rough analysis, where recovering a path from its signature can be formalized by stabilizers with respect to congruence group actions. The second example focuses on understanding the image of the two-parameter signature, when restricted to piecewise bilinear and polynomial membranes.\n\nOur long-term goal is for SignatureTensors.jl to become a foundational tool for theoretical research on signature tensors, while providing a flexible base framework for interdisciplinary application, such as time series, image analysis, spatial data, and more. \n\nThis is a joint work with Leonard Schmitz (TU Berlin) and the code is available at https://github.com/leonardSchmitz/signature-tensors-in-OSCAR", "recording_license": "", "do_not_record": false, "persons": [{"code": "A87VAC", "name": "Gabriel Riffo", "avatar": "https://pretalx.com/media/avatars/NP9QJS_APru5ql.webp", "biography": "I\u2019m a PhD student with Professor Carlos Am\u00e9ndola at the research group of Algebraic and Geometric Methods in Data Analysis at Technische Universit\u00e4t Berlin. I am part of the DFG Collaborative Research Center  Rough Analysis, Stochastic Dynamics and Related Fields CRC/TRR 388 as a research assistant for project B01: Statistical Learning from Path Observations.\nMy academic interests lie in the field of statistics, particularly in areas such as Algebraic Statistics and Topological Data Analysis, focusing on the application of algebra and geometry to statistical problems. I am especially interested in tackling problems that involve integrating multiple branches of mathematics. Additionally, I have strong interests in Spatial Statistics, Time Series, and Computational Statistics.\n\nPreviously, I obtained my Bachelor's degree in Mathematical Engineering and my Master's degree in Mathematics from Universidad T\u00e9cnica Federico Santa Mar\u00eda.", "public_name": "Gabriel Riffo", "guid": "541155eb-9190-5352-bbec-3ab64144a302", "url": "https://pretalx.com/juliacon-2026/speaker/A87VAC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7VFDJF/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7VFDJF/", "attachments": []}, {"guid": "679dbc26-8f88-57e5-a539-8bf355605e12", "code": "7YGMC3", "id": 92689, "logo": "https://pretalx.com/media/juliacon-2026/submissions/7YGMC3/image_RoHV0Xt.webp", "date": "2026-08-14T15:45:00+02:00", "start": "15:45", "end": "2026-08-14T16:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92689-phoxonic-jl-unified-interface-for-calculating-photonic-and-phononic-bandgaps-with-pure-julia", "url": "https://pretalx.com/juliacon-2026/talk/7YGMC3/", "title": "PhoXonic.jl: Unified interface for calculating photonic and phononic bandgaps with pure Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Periodic structures create band gaps that restrict electromagnetic and elastic wave propagation. These gaps enable control of light and sound at the wavelength scale. `PhoXonic.jl` is the first pure Julia tool computing both photonic and phononic dispersion relations through a unified interface using plane wave expansion. It supports 1D, 2D, and 3D with dense and sparse solvers, and includes topological invariant analysis. Results reproduce published literature.", "description": "Photonic and phononic crystals are periodic structures that exhibit band gaps\u2014frequency ranges where electromagnetic or elastic waves cannot propagate. These materials enable precise control over light and sound at the wavelength scale, with applications ranging from optical fibers and lasers to acoustic filters and vibration isolation. When a single structure simultaneously exhibits both photonic and phononic band gaps, it is called a phoxonic crystal, enabling coupled optomechanical interactions. Existing tools such as [`MPB`](https://mpb.readthedocs.io/en/latest/) focus on photonic crystals only and require C++/Scheme.\n\n[`PhoXonic.jl`](https://github.com/hsugawa8651/PhoXonic.jl) is the first pure Julia tool computing both photonic and phononic dispersion relations using plane wave expansion (PWE). It offers: \n- **Unified API across 1D, 2D, and 3D**: The same workflow for photonic and phononic crystals\u2014define materials, geometry, and wave type, then compute band structures.\n- **Multiple solver backends**: Dense eigensolvers for small systems, Krylov methods for large-scale problems, and LOBPCG with warm-start acceleration for efficient band structure sweeps.\n- **Green's function method and supercell supports**: Density of states (DOS) and local density of states (LDOS) calculations for defect mode analysis. Point and line defect simulations via supercell construction.\n- **Transfer matrix method**: Exact solutions for 1D multilayer structures, including transmission/reflection spectra, oblique incidence with TE/TM polarization, and support for lossy materials.\n- **Topological Invariant Analysis**: The ability to compute the 2D Wilson loop spectrum and winding number enables its use as a research tool in topological photonics/phononics.\n\n## Validation / Features / Future Directions\n\nThe accompanying figure formation of phoxonic bandgap reproducing the paper by [Maldovan & Thomas (2006)](https://doi.org/10.1063/1.2216885).\n\nPhoXonic.jl has been validated against:\n- (Photonic bandgap) [MIT Photonic Bands](https://mpb.readthedocs.io/en/latest/) (MPB) and [textbook examples from Joannopoulos](http://ab-initio.mit.edu/book/).\n- (Phononic bandgap) Published results from [Kushwaha et al.]\n(https://doi.org/10.1103/PhysRevLett.71.2022) for phononic crystals\n- [Tanaka et al.](https://doi.org/10.1103/PhysRevB.62.7387) for phononic crystals with void inclusions\n- [Dobrzynski et al., \"Phononics\" textbook](https://doi.org/10.1016/C2015-0-06475-9) (2017, Elsevier, Ch.5) for Si/Epoxy and C/Epoxy phononic crystals (circular and square cross-section inclusions, examples 216--218)\n\n\nThis package is open to contributions and designed for extensibility.\n\n## Links\n- PhoXonic.jl: https://github.com/hsugawa8651/PhoXonic.jl, DOI: 10.5281/zenodo.18055170\n- Docs: https://hsugawa8651.github.io/PhoXonic.jl/dev/\n- Colab:  https://colab.research.google.com/gist/hsugawa8651/6e19b5d1c083e925aa1642a2fab6f0fc/colab_demo.ipynb", "recording_license": "", "do_not_record": false, "persons": [{"code": "K3MXFP", "name": "Hiroharu Sugawara", "avatar": null, "biography": "Hiroharu Sugawara is an associate professor in the Graduate School of Systems Design at Tokyo Metropolitan University, Tokyo, Japan. He received his Ph.D. in electronic engineering from the University of Tokyo in 1994. \nHis research focuses on eco-friendly semiconductor functional materials. \nHe has been a Julia user since Julia 0.5. \nHe has been teaching a programming exercise course using the Julia language for university freshmen in the Department of Mechanical Systems Engineering every year since the 2018 academic year.\n\nHe translated Tanmay Bakshi's \"Tanmay Teaches Julia for Beginners\" into Japanese ([ISBN 978-4807920211](https://www.tkd-pbl.com/book/b598314.html)) in 2022.", "public_name": "Hiroharu Sugawara", "guid": "1c4f99fb-cef5-579a-af6c-f206e0f9c60c", "url": "https://pretalx.com/juliacon-2026/speaker/K3MXFP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/7YGMC3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/7YGMC3/", "attachments": [{"title": "flyer-7YGMC3", "url": "/media/juliacon-2026/submissions/7YGMC3/resources/7YGMC3_Fghr4tp.png", "type": "related"}]}, {"guid": "fd0d89a6-d23b-5b9a-aec0-4f997717697f", "code": "NMJBP9", "id": 92804, "logo": "https://pretalx.com/media/juliacon-2026/submissions/NMJBP9/image_lE37fwN.webp", "date": "2026-08-14T16:00:00+02:00", "start": "16:00", "end": "2026-08-14T16:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92804-microstructure-simulation-in-pure-julia-phase-fields-with-calphad-coupling", "url": "https://pretalx.com/juliacon-2026/talk/NMJBP9/", "title": "Microstructure Simulation in Pure Julia: Phase Fields with CALPHAD Coupling", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "The phase field method simulates microstructure evolution and phase transitions. `PhaseFields.jl` is the first pure Julia package providing major phase field models with built-in FDM and FEM (via Gridap.jl) and adaptive time stepping (DifferentialEquations.jl). It couples with `OpenCALPHAD.jl` for chemical potentials via automatic differentiation from CALPHAD databases.  We demonstrate spinodal decomposition, CALPHAD-driven solidification, and Stefan problem validation.", "description": "Understanding and predicting the internal structure of materials at the microscale (microstructure) is essential in materials science and engineering. The arrangement of different phases and their interfaces determines key material properties including mechanical strength and thermal conductivity.\n\nThe phase field method simulates microstructure evolution during solidification and phase separation.\nIt represents different phases (e.g., solid and liquid, matrix and precipitates) using a continuous field variable `phi` that varies smoothly across interfaces, avoiding explicit interface tracking. The value `phi = 1` represents the solid phase and `phi = 0` represents the liquid phase, with a diffuse interface region in between.\n\nThe driving force of the field variable `phi` is based on the thermodynamic principle of Gibbs free energy minimization. In the Allen-Cahn model (interface motion) it drives the order parameter `phi`, while in the Cahn-Hilliard model (phase separation) it drives the composition `c`. We present how automatic differentiation with Julia is effectively utilized in phase field simulations.\n\nThe accompanying figure shows 2D spinodal decomposition simulated by the Cahn-Hilliard equation. The color map represents the local composition `c` (mole fraction of component B in a binary A-B system). Starting from a nearly uniform mixture (left, `c` = 0.5 with small fluctuations), the system spontaneously separates into two coexisting phases: an A-rich phase (`c` = 0.3, blue) and a B-rich phase (`c` = 0.7, red). This phase separation is driven by a double-well free energy whose two minima correspond to the equilibrium compositions of the  coexisting phases. The simulation uses `OrdinaryDiffEq.jl` for time integration via the unified `PhaseFields.solve` API.\n\nWe have developed two pure Julia packages for phase field simulation.\n\nThe first package [`PhaseFields.jl`](https://github.com/hsugawa8651/PhaseFields.jl) implements a framework for multiple phase field models. Each phase field model defines a set of equations governing the field variable `phi` and the associated physical fields such as concentration and temperature. These equations are spatially discretized using the built-in FDM or FEM (via `Gridap.jl`). Then, time evolution is solved using `DifferentialEquations.jl`,  applying an appropriate time stepping method as needed.\n\nThe second package, [`OpenCALPHAD.jl`](https://github.com/hsugawa8651/OpenCALPHAD.jl), predicts phase diagrams mapping the thermodynamically stable phases of materials as functions of temperature, pressure, and composition. It also evaluates Gibbs free energy from thermodynamic databases described in TDB format or as pure Julia functions. The driving force of the field variable `phi` is directly evaluated through automatic differentiation, eliminating the need for symbolic differentiation and hand-coded derivatives.\n\nTwo packages integrate seamlessly with the Julia ecosystem, including `Plots.jl` for visualization of phase diagrams and phase field simulations.\n\nWe attach [a Google Colab notebook](https://colab.research.google.com/gist/hsugawa8651/483b77d1526ee9a5fe370db159192275/colab_demo_ocpf.ipynb) that highlights several features of the two packages including Ag-Cu binary phase diagram (CALPHAD), Allen-Cahn 1D interface migration, 2D spinodal decomposition (Cahn-Hilliard), and the Stefan problem (thermal solidification).\n\n## Validation / Features / Future Directions\n\n`OpenCALPHAD.jl` is a pure Julia port of openCALPHAD, an open-source CALPHAD software package developed by Bo Sundman, a co-developer of Thermo-Calc and one of the pioneers of computational thermodynamics. `OpenCALPHAD.jl` has been validated for binary phase diagram calculations against the reference Fortran implementation openCALPHAD v6.100, demonstrating numerical agreement within 0.02% for phase boundaries. `PhaseFields.jl` was validated by its ability to reproduce results from significant papers in this field including the Stefan problem (thermal solidification).\n\nCALPHAD-coupled phase field simulation has been demonstrated in 1D (Allen-Cahn with Ag-Cu driving force from TDB database via automatic differentiation). Extension to 2D CALPHAD-coupled Cahn-Hilliard (spinodal decomposition with real alloy thermodynamics) is in progress. The `CahnHilliardProblem` interface accepts any custom free energy function via duck typing, making it straightforward to plug in CALPHAD Gibbs energy as the driving force.\n\nWe plan to implement the Kim-Kim-Suzuki (KKS) model, which resolves the driving force scaling mismatch between CALPHAD thermodynamics and the phase field double-well barrier by separating the concentration into phase-specific values at the interface.\nSince `OpenCALPHAD.jl` already provides ForwardDiff-compatible Gibbs energy functions,\nthe chemical potentials required by the KKS equal-potential condition can be obtained directly via automatic differentiation.\n\nBoth packages are freely available for academic and commercial use.  They are designed with educational use in mind, featuring readable code that closely follows the mathematical formulation of the underlying theory. This makes them suitable for teaching computational thermodynamics and for researchers who wish to understand or extend the underlying algorithms.\n\nWe aim to expand the range of material systems and phase field models that can be handled by both packages.\n\nBoth packages are open to contributions and designed for extensibility. Contributions are welcome.\n\n## Links\n- PhaseFields.jl: https://github.com/hsugawa8651/PhaseFields.jl, https://hsugawa8651.github.io/PhaseFields.jl\n- OpenCALPHAD.jl:  https://github.com/hsugawa8651/OpenCALPHAD.jl, https://hsugawa8651.github.io/OpenCALPHAD.jl\n- Colab: https://colab.research.google.com/gist/hsugawa8651/483b77d1526ee9a5fe370db159192275/colab_demo_ocpf.ipynb", "recording_license": "", "do_not_record": false, "persons": [{"code": "K3MXFP", "name": "Hiroharu Sugawara", "avatar": null, "biography": "Hiroharu Sugawara is an associate professor in the Graduate School of Systems Design at Tokyo Metropolitan University, Tokyo, Japan. He received his Ph.D. in electronic engineering from the University of Tokyo in 1994. \nHis research focuses on eco-friendly semiconductor functional materials. \nHe has been a Julia user since Julia 0.5. \nHe has been teaching a programming exercise course using the Julia language for university freshmen in the Department of Mechanical Systems Engineering every year since the 2018 academic year.\n\nHe translated Tanmay Bakshi's \"Tanmay Teaches Julia for Beginners\" into Japanese ([ISBN 978-4807920211](https://www.tkd-pbl.com/book/b598314.html)) in 2022.", "public_name": "Hiroharu Sugawara", "guid": "1c4f99fb-cef5-579a-af6c-f206e0f9c60c", "url": "https://pretalx.com/juliacon-2026/speaker/K3MXFP/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/NMJBP9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/NMJBP9/", "attachments": [{"title": "flyer-NMJBP9", "url": "/media/juliacon-2026/submissions/NMJBP9/resources/NMJBP9_YtmCErm.png", "type": "related"}]}, {"guid": "926291ef-5f27-5ea9-ace4-27a6f3407281", "code": "3MV3BW", "id": 92762, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3MV3BW/image_EIfthIc.webp", "date": "2026-08-14T16:15:00+02:00", "start": "16:15", "end": "2026-08-14T16:30:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92762-kapseudospectra-jl-gpu-accelerated-pseudospectra-via-kernelabstractions-jl", "url": "https://pretalx.com/juliacon-2026/talk/3MV3BW/", "title": "KAPseudospectra.jl: GPU-Accelerated Pseudospectra via KernelAbstractions.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Pseudospectra generalize eigenvalue analysis by characterizing how the resolvent norm of (zB - A)^{-1} varies over the complex plane, revealing transient behavior and stability properties that eigenvalues alone miss.\nGrowing demand from non-Hermitian physics, power systems, and data-driven robust control necessitates large-scale pseudospectral computations that existing CPU-based tools cannot efficiently handle for large dense matrices.\n\nIn this talk, we present KAPseudospectra.jl, the first GPU-accelerated pseudospectra package, built on KernelAbstractions.jl for vendor-neutral execution across supported backends.\nThe package implements a batched Inverse Hermitian Lanczos (IHL) iteration that approximates the smallest singular value at each grid point in only a few steps, requiring O(N^2) operations at each grid point after a single O(N^3) CPU-only Schur decomposition.\nCentral to the IHL iteration is KATRSM.jl, a submodule providing batched triangular solvers that keep the pencil (zB - A) factored on-device, largely eliminating host-device data movement.\nMulti-device parallelism is achieved by partitioning the complex grid across available GPUs with automatic memory-aware batching.\n\nWe demonstrate the package on matrices up to dimension 2^14, and discuss the design decisions that enable this codebase to target multiple compute backends through Julia's package extension system and KernelAbstractions.jl.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "SYVDMN", "name": "Dan Folescu", "avatar": "https://pretalx.com/media/avatars/8DM8EY_ckmgOSL.webp", "biography": "I am a PhD student at Virginia Tech working on robustness of data-driven system identification methods and their application to solving nonlinear eigenvalue problems using contour integral methods.", "public_name": "Dan Folescu", "guid": "e81d03c7-a480-5b56-b5a9-ca6c9f881203", "url": "https://pretalx.com/juliacon-2026/speaker/SYVDMN/"}], "links": [{"title": "KAPseudospectra.jl", "url": "https://github.com/dan123222123/KAPseudospectra.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3MV3BW/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3MV3BW/", "attachments": [{"title": "flyer-3MV3BW", "url": "/media/juliacon-2026/submissions/3MV3BW/resources/3MV3BW_cFCHI1E.png", "type": "related"}]}, {"guid": "9dec7836-20a6-5971-a862-81b29b6acd00", "code": "738AXH", "id": 90685, "logo": "https://pretalx.com/media/juliacon-2026/submissions/738AXH/image_1Dwcy26.webp", "date": "2026-08-14T16:30:00+02:00", "start": "16:30", "end": "2026-08-14T16:45:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-90685-optimal-control-of-a-field-generator-using-jump-jl-and-ipopt-jl", "url": "https://pretalx.com/juliacon-2026/talk/738AXH/", "title": "Optimal Control of a Field Generator using JuMP.jl and IPOPT.jl", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Dynamic magnetic field generation is essential for numerous applications but grows power-intensive with system scale. Non-linear current-to-field relationships in soft iron core designs complicate the inverse current problem. This work formulates power-efficient control as a nonlinear program with algebraic constraints on field and gradient strength, solved using JuMP.jl and IPOPT.jl. Spherical harmonic expansions provide the polynomial field representation for efficient optimization on consumer hardware.", "description": "This talk details the implementation of a constrained nonlinear optimization framework for controlling soft iron core magnetic field generators. Unlike linear air-core systems, these generators exhibit complex current-to-field relationships, necessitating robust nonlinear programming techniques.\n\nThe control objective of minimizing power consumption subject to dynamic field constraints is formulated as a nonlinear algebraic problem within [JuMP.jl](https://github.com/jump-dev/JuMP.jl). Key technical aspects include:\n- Symbolic-Numeric Representation: Magnetic fields are represented using truncated spherical harmonic expansions (implemented via [SphericalHarmonicExpansions.jl](https://github.com/IBIResearch/SphericalHarmonicExpansions.jl) and [DynamicPolynomials.jl](https://github.com/JuliaAlgebra/DynamicPolynomials.jl)), providing polynomial field representations that satisfy the quasi-static Maxwell equations.\n- Nonlinear Constraint Algebra: Strict feasibility is enforced through hardware constraints, algebraic constraints on the magnetic field vector (polynomial inequalities), as well as constraints on the smallest singular value of the field Jacobian (ensuring gradient strength for spatial encoding), registered as user-defined nonlinear functions in JuMP.\n- Interior Point Solution: [IPOPT.jl](https://github.com/jump-dev/Ipopt.jl) solves the resulting Karush-Kuhn-Tucker (KKT) system, leveraging barrier methods to handle the bound and inequality constraints inherent to hardware current limits.\n\nAlthough neural networks ([Flux.jl](https://github.com/FluxML/Flux.jl)) provide the forward model surrogate for field coefficient prediction, the focus remains on the algebraic structure of the optimization problem and the numerical methods employed to solve it. The framework demonstrates how Julia\u2019s algebraic modeling ecosystem enables real-time optimal control, achieving precise field generation with minimal power consumption.", "recording_license": "", "do_not_record": false, "persons": [{"code": "KRP8EJ", "name": "Philip Suskin", "avatar": "https://pretalx.com/media/avatars/KRP8EJ_jspVrC3.webp", "biography": "PhD student in the group of Tobias Knopp for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology.", "public_name": "Philip Suskin", "guid": "39095bf9-ab7d-5c53-a025-0ca63c2cdb7b", "url": "https://pretalx.com/juliacon-2026/speaker/KRP8EJ/"}], "links": [{"title": "SphericalHarmonicExpansions.jl", "url": "https://github.com/IBIResearch/SphericalHarmonicExpansions.jl", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/738AXH/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/738AXH/", "attachments": [{"title": "flyer-738AXH", "url": "/media/juliacon-2026/submissions/738AXH/resources/738AXH_R0O7GNi.png", "type": "related"}, {"title": "Master's thesis: \"Optimal Control of a Field-Free-Point Using a Multi-Coil Magnetic Field Generator\"", "url": "/media/juliacon-2026/submissions/738AXH/resources/thesis_yzYfVja.pdf", "type": "related"}, {"title": "Visualization of optimized field trajectory with associated power consumption", "url": "/media/juliacon-2026/submissions/738AXH/resources/trajectory10_5t2CQpD.jpg", "type": "related"}, {"title": "IPOPT Constraints", "url": "/media/juliacon-2026/submissions/738AXH/resources/IPOPT_Constr_dXypTcW.jpg", "type": "related"}]}, {"guid": "5dff6017-e823-5bc9-8c65-d4da4a615f67", "code": "AMFLYU", "id": 92844, "logo": "https://pretalx.com/media/juliacon-2026/submissions/AMFLYU/image_9m418rj.webp", "date": "2026-08-14T16:45:00+02:00", "start": "16:45", "end": "2026-08-14T17:00:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92844-composable-probabilistic-models-can-lower-barriers-to-rigorous-infectious-disease-modelling", "url": "https://pretalx.com/juliacon-2026/talk/AMFLYU/", "title": "Composable probabilistic models can lower barriers to rigorous infectious disease modelling", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Recent outbreaks of Ebola, COVID-19 and mpox, alongside routine surveillance of endemic pathogens, have demonstrated the value of modelling for synthesising data to inform decision making. For modelling evidence to effectively inform policy it must be timely, rigorous, and collaborative, yet current approaches struggle to be all three. Methods broadly fall into approaches that chain separate models together, offering flexibility but losing information and introducing bias, or approaches that rigorously analyse all data together but cannot be separated into reusable parts. Composable models, where components can be reused across contexts, can be both rigorous and flexible, enabling rapid collaborative model development. We outline design considerations for a composable infectious disease modelling framework and present a proof of concept domain-specific language built on the Turing.jl probabilistic programming language in Julia with an R interface. We demonstrate our approach conceptually using models from published epidemiological analyses, and in practice through a worked autoregressive example. We replicate three published analyses, composing elements of our autoregressive example with shared and novel components: a COVID-19 analysis for South Korea using a renewal process, adding components for reporting delays and day-of-week effects to replicate EpiNow2 for real-time nowcasting, and an ordinary differential equation analysis of influenza outbreak data. We then discuss strengths, limitations, and alternative approaches. We find that our proof of concept can address the tension between rigour and flexibility, though work remains to realise this potential. Our approach enables interdisciplinary collaboration by lowering technical barriers for domain experts to contribute specialised components, supporting both routine surveillance and outbreak response. For multi-model efforts, common components enable attribution of differences to assumptions rather than implementation. Our approach is also well suited for large language model assisted model construction. This study demonstrates that a composable modelling approach has the potential to incorporate diverse modelling approaches and domain knowledge across different infectious disease contexts.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "SGPNJN", "name": "Sam Abbott", "avatar": "https://pretalx.com/media/avatars/SBLN8B_L1KfDIw.webp", "biography": "I am an Assistant Professor at the London School of Hygiene & Tropical Medicine. I did my PhD in the optimal usage of the BCG vaccine, transitioning to work on real-time modelling of infectious disease outbreaks on the 3rd of January 2020. Four days later, I switched to work on what was then known as 2019-NCoV. I did early work on the size and scale of the initial outbreak, tracking transmissions in different countries, and exploring the potential role of different interventions. Throughout the pandemic, I ran a dashboard that was used by over a million people. I also provided estimates, forecasts, and analyses weekly to the UK government advisory bodies. I developed the tools and methods we used into open source software and these were used by upwards of 30 public health agencies around the world. I have continued to work in this area with a focus on improving tools and methods used both in research and in public health practice. I have recently transitioned to Julia for my work and am exploring how to propogate Julia based tools to the users of our current tooling and to the wider infectious disease modelling community.", "public_name": "Sam Abbott", "guid": "7aa3797d-932b-542d-ba81-d1d2b10724c0", "url": "https://pretalx.com/juliacon-2026/speaker/SGPNJN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/AMFLYU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/AMFLYU/", "attachments": []}, {"guid": "df534c77-dc93-5656-8730-1a401efe31bc", "code": "GQ8NM3", "id": 92716, "logo": "https://pretalx.com/media/juliacon-2026/submissions/GQ8NM3/image_erDgnKV.webp", "date": "2026-08-14T17:00:00+02:00", "start": "17:00", "end": "2026-08-14T17:15:00+02:00", "duration": "00:15", "room": "Muschel \u2014 N3", "slug": "juliacon-2026-92716-zed-support-for-julia", "url": "https://pretalx.com/juliacon-2026/talk/GQ8NM3/", "title": "Zed support for Julia", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "Zed support for Julia is growing!\n\nWe'll talk about working and missing features and how Zed today against other Julia supporting editors.", "description": "Zed is the hot new Rust-based editor in town that's fast and open soruce.\n\nWe'll go over the basic architecture of Zed and its extension ecosystem, and tour through important working and missing items including\n* Debugger support\n* Test item runner integration\n* REPL integration\n* Notebook integration\n* Revise.jl integration\n* Profiler charts\n* Plotting support", "recording_license": "", "do_not_record": false, "persons": [{"code": "GZTVYN", "name": "Miguel Raz Guzm\u00e1n Macedo", "avatar": "https://pretalx.com/media/avatars/JE89PF_MAYAznx.webp", "biography": "I work as a support engineer at Zed Industries, the company behind Zed.\n\n...I try to smuggle in goodies for Julia whenever I can.", "public_name": "Miguel Raz Guzm\u00e1n Macedo", "guid": "4d31ffc3-6edf-5ab0-ad32-779c4ccaf866", "url": "https://pretalx.com/juliacon-2026/speaker/GZTVYN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/GQ8NM3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/GQ8NM3/", "attachments": []}], "Alte Mensa \u2014 Audi Max": [{"guid": "b4be3b0e-b86c-5cd9-bbbc-ee9901c5cadb", "code": "YNFSLT", "id": 92639, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YNFSLT/image_xUuGDp5.webp", "date": "2026-08-14T10:00:00+02:00", "start": "10:00", "end": "2026-08-14T10:30:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92639-how-we-made-julia-make-microchips", "url": "https://pretalx.com/juliacon-2026/talk/YNFSLT/", "title": "How We Made Julia Make Microchips", "subtitle": "", "track": "Julia in Industry", "type": "Long talk", "language": "en", "abstract": "ASML builds the lithography machines that enable the world\u2019s most advanced microchips. For our newest DUV systems, Julia has become part of the control software stack\u2014directly participating in algorithms that influence wafer quality and overall system performance.\n\nAt JuliaCon 2025 we shared our early exploration and our intention to use Julia in production. One year later, we are excited to report concrete results: we successfully exposed wafers on a prototype machine using a Julia library built with juliac/PackageCompiler, and the performance, stability, and developer experience were all very promising.\n\nIn this talk, we will share how we designed, optimized, and deployed time\u2011critical Julia code in an environment where algorithms must complete within strict millisecond\u2011level deadlines, remain predictable, and integrate with a large, safety\u2011critical control system written in multiple languages.\n\nWe will highlight the architecture patterns we adopted, the trade\u2011offs we had to make, and a collection of \u201cunexpected lessons\u201d from working with Julia in a real industrial setting.", "description": "Control algorithms inside a lithography system must satisfy demanding timing constraints. They are mathematically nontrivial, involve real\u2011time data flows, and are executed on machines that simply cannot miss a deadline. Bringing Julia into this environment required us to think carefully about compilation pipelines, memory behavior, determinism, integration boundaries, and observability.\n\nIn this talk we will discuss:\n\n* **Our general approach to time\u2011critical Julia algorithms**\n  How we design the algorithmic code, how we structure the surrounding Julia modules, and how we ensure that the core logic can be analyzed, tested, and optimized independently of the machine.\n* **Bridging production and testing environments**\n  How we co\u2011develop algorithms in simulation and on\u2011machine setups, what kinds of differences matter, and how we keep Julia code consistent across both.\n* **Compilation and deployment challenges**\n  A practical look at our experience with PackageCompiler and JuliaC, including:\n  * latency considerations\n  * binary portability\n  * ABI boundaries\n  * linking against a larger C/C++ ecosystem\n\n  We will share the pitfalls that surprised us the most\u2014especially those specific to embedding Julia in a non\u2011Julia control stack.\n* **What worked extraordinarily well**\n  Where Julia exceeded our expectations in performance, productivity, or reliability, and which language features made the biggest difference.\n* **What to watch out for when bringing Julia to production**\n  Realistic expectations, dos and don\u2019ts, and general advice for teams embedding Julia into industrial systems.\n\nOur goal is to give the Julia community a realistic, experience-based look at using Julia for production-grade, time\u2011critical applications. We hope this will help others who are considering Julia for high\u2011performance or industrial workloads.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZAAWQQ", "name": "Yury Nuzhdin", "avatar": "https://pretalx.com/media/avatars/WEJRES_JZhIvzf.webp", "biography": "Software Architect in ASML working on Julia algorithms in the near real time system.\n[GitHub](https://github.com/tz-lom)", "public_name": "Yury Nuzhdin", "guid": "875f23e9-c926-5991-bb1a-569f3d231ef2", "url": "https://pretalx.com/juliacon-2026/speaker/ZAAWQQ/"}, {"code": "PVVSXK", "name": "Jorge Alberto Vieyra Salas", "avatar": null, "biography": "Born in Mexico City. Studied a Bachelors in Chemical Engineering at UNAM. M.Sc. on Materials Science and Engineering at MIT. Studied PhD at TU Eindhoven on Applied Physics.\nWorked for Philips Research 1 year.\nWorking at ASML for 13 years on algorithms.", "public_name": "Jorge Alberto Vieyra Salas", "guid": "37fbaeb6-ab74-5f69-8b77-40fdd41c7774", "url": "https://pretalx.com/juliacon-2026/speaker/PVVSXK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YNFSLT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YNFSLT/", "attachments": []}, {"guid": "9d17c0d8-88be-5016-a293-b84cc714089c", "code": "TVG9AR", "id": 90858, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TVG9AR/image_srAMNSX.webp", "date": "2026-08-14T10:45:00+02:00", "start": "10:45", "end": "2026-08-14T11:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-90858-bi-engine-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/TVG9AR/", "title": "BI Engine in Julia", "subtitle": "", "track": "Julia in Industry", "type": "Short talk", "language": "en", "abstract": "This talk presents JADE (Julia Analytics Decision Engine), a production system serving a wide variety of companies, that resolves BI development tension through rapid model development alongside dynamic Julia package generation, intelligent precompilation, and cache-first architecture.\n\n  JADE generates complete Julia packages at runtime from domain-specific analytical models. Each generated package contains 10K+ lines of Julia code implementing hundreds of analytical functions, multi-dimensional data structures, dependency graphs, and model-specific formula chains. The system currently serves enterprise clients with production models processing gigs of data and supporting thousands of function calls per evaluation with minimal query latency for warm queries.\n\n  The core innovation is our three-stage precompilation strategy that balances compilation overhead against runtime performance. First, we precompile reusable function templates once and distribute them via a shared depot, covering hundreds of analytical functions relevant to financial modeling, statistical analysis, and multi-dimensional array operations.\n  Second, when models change, we generate model-specific code and combine it with precompiled templates without triggering full recompilation. \n  Third, we maintain a hash-based cache of compiled packages that delivers instant results for cache hits while compiling updated packages in the background.\n\n  We support two deployment patterns with different performance characteristics. In local mode, Julia processes run on workstations or servers, achieving fast cold starts and near instant warm queries.\n  In Hub mode, distributed Julia processes use shared Registries and caches, delivering low latency queries with horizontal scaling.\n\n  Performance optimization is central to JADE's architecture. We employ automatic multi-threading for large array operations with custom chunking strategies, coordinate remapping systems that precompute dimension maps to avoid allocations in hot loops, smart dirty state tracking via dependency graphs that reduces recomputation, and union splitting macros to limit reliance on type dispatch while maintaining flexibility for heterogeneous data.\n\n  The talk will cover practical engineering challenges we solved: managing Julia depot paths across deployment environments, implementing intelligent cache invalidation strategies, optimizing precompilation workloads with custom compile statements, handling package versioning and upgrades in production, along with debugging performance issues in generated code. We'll share performance measurements, code examples, and lessons learned from a year of production deployment.\n\n  This work demonstrates Julia's readiness for enterprise-critical systems and provides an inspiration for organizations building dynamic code generation platforms and formula chain engines. The techniques we present\u2014precompilation strategies, caching architectures, and performance optimization patterns\u2014are broadly applicable to any domain requiring flexible, high-performance analytics.\n  Our experience shows that Julia's combination of performance, metaprogramming capabilities, and ecosystem maturity enables production systems that were previously impractical.\n\n  Target audiences include enterprise developers integrating Julia into business intelligence platforms, developers building code generation systems for domain-specific languages, performance-focused Julia users, and organizations evaluating Julia for production analytics workloads.", "description": "This talk presents JADE (Julia Analytics Decision Engine), a production system serving a wide variety of companies, that resolves BI development tension through rapid model development alongside dynamic Julia package generation, intelligent precompilation, and cache-first architecture.\n\n  Key Takeaways: Attendees will learn proven patterns for dynamic Julia package generation, effective precompilation strategies, cache architecture designs, and practical advice for deploying Julia in enterprise environments. This talk provides evidence that Julia can power mission-critical business systems with production-grade reliability.\n\n  Target Audience: Enterprise developers, performance engineers, code generation system builders, and organizations evaluating Julia for production analytics.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HRBUXF", "name": "Matthew Muyres / Chase Cowart", "avatar": null, "biography": "Matt Muyres is a Principal Engineer at Planview specializing in complex data systems and enterprise application optimization, with a background in engineering from the US Navy where he managed network infrastructure. His career includes 15 years at Enrich Consulting leading database architecture and application performance optimization, providing a perspective that bridges deep technical work with business strategy. \n\nChase Cowart is an Analytics Engineering Manager at Planview with a background as a Bioenvironmental Engineer and Captain in the United States Air Force. He holds a Master of Science from Duke University with a focus on medical imaging physics and data science.", "public_name": "Matthew Muyres / Chase Cowart", "guid": "999a137b-263a-525a-a8dc-7a4737ce4eeb", "url": "https://pretalx.com/juliacon-2026/speaker/HRBUXF/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TVG9AR/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TVG9AR/", "attachments": []}, {"guid": "d288a8af-b762-5cba-8a9b-4c296d84d40c", "code": "CXRHSP", "id": 92058, "logo": "https://pretalx.com/media/juliacon-2026/submissions/CXRHSP/image_xUgUgFk.webp", "date": "2026-08-14T11:15:00+02:00", "start": "11:15", "end": "2026-08-14T11:45:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92058-building-production-desktop-guis-in-julia-at-nasa-with-dear-imgui-and-mirage-jl", "url": "https://pretalx.com/juliacon-2026/talk/CXRHSP/", "title": "Building Production Desktop GUIs in Julia at NASA with Dear ImGui and Mirage.jl", "subtitle": "", "track": "Julia in Industry", "type": "Short talk", "language": "en", "abstract": "What if building an interactive scientific GUI in Julia didn't require a web stack, a server boundary, or a second language? We'll share how our team replaced a React frontend and Julia backend with a standalone Julia application for planning lunar rover routes at the Moon's south pole\u2014rendering terrain, illumination, waypoints, solar exposure, and communications windows in one process.\n\nThe talk introduces a practical immediate-mode stack: Dear ImGui via CImGui.jl for responsive UI, and Mirage.jl for simple Canvas2D-style 2D and 3D rendering on OpenGL. Along the way, we'll cover the architectural tradeoffs, our REPL-driven workflow, and patterns applicable to geospatial viewers, simulation dashboards, instrument panels, and data-exploration tools. No graphics programming experience required.", "description": "When our team needed an interactive planning tool for lunar surface missions, we started with what seemed like the obvious approach: a React web frontend backed by a Julia server. It worked, but the developer experience was not ideal. Every piece of data had to be serialized over HTTP. UI bugs could live in JavaScript, Julia, or the communication layer between them. Adding a feature meant touching two codebases in two languages, as well as maintaining state in two different places using two different metaphors. Not to mention trying to do custom 2D / 3D rendering meant mixing React and HTML5 Canvas. For a small team, the overhead was becoming too much to maintain.\n\nSo we tried something different - build the whole thing in Julia. The result is a standalone desktop application for planning rover routes on the lunar south pole. The GUI renders terrain maps with sun illumination overlays, lets users place and adjust waypoints, and visualizes time-varying data like solar exposure and communications windows. One language, one process, no server.\n\nThe stack has two pieces:\n\nDear ImGui (via CImGui.jl) handles all the UI: windows, sliders, buttons, menus, tables. It's an immediate-mode library, which means every frame your code says \"draw a button here, a slider there,\" and ImGui handles interaction. If your state changes, the UI reflects it next frame using Dear ImGui's immediate-mode model. No widget trees, no callbacks, no syncing. This is a good match for scientific tools where the interface evolves constantly.\n\nMirage.jl handles rendering. I wrote it because the available Julia OpenGL wrappers were too low-level for rapid prototyping. The API feels like HTML5 Canvas2D: draw_image(), fill_rect(), draw_circle(). It manages shaders and vertex buffers internally so you don't have to. I later added 3D mesh rendering following the same minimal, immediate-mode philosophy.\n\nThe immediate-mode approach has another real benefit beyond simplicity, one that is aligned with Dear ImGui. There's no retained state to go stale. Your render function is a function of your current application state, and your current application state are simply Julia variables. When we needed a new overlay or visualization, it is simply just a few draw calls in the right place.\n\nBut the biggest accelerator was the REPL workflow. Start the GUI from the Julia REPL, use it, close the window, change some code and see it update live. This turns GUI development into the same fast iteration loop Julia developers already use for everything else, and it's something you really can't get with compiled GUI frameworks. It brings the fast-paced iteration of the web ecosystem (like React) into Julia.\n\nThis stack is general-purpose. Any application that puts an interactive visual frontend on Julia computation could work this way: instrument control panels, geospatial viewers, simulation monitors, data exploration tools. The patterns are the same regardless of domain.\n\nIn this talk I'll cover:\n- Why we moved away from a React/Julia split architecture and what we gained\n- How Dear ImGui and OpenGL compose into an application framework in Julia\n- How Mirage.jl maps Canvas2D-style calls to OpenGL\n- A live demo of the tools in action\n- Practical patterns for structuring a Julia GUI app, and the rough edges to watch for\n\nNo graphics programming experience needed.", "recording_license": "", "do_not_record": false, "persons": [{"code": "RL7GXH", "name": "Zach Booth", "avatar": "https://pretalx.com/media/avatars/MWCY9H_pAvympk.webp", "biography": "Zach Booth Lead Software Engineer at the Automated Decision Making Group at NASA Ames Resarch Center working on SHERPA, strategic planning software for lunar rovers using MDPs and POMDPs. On the side, Zach develops indie games and loves to study financial markets.", "public_name": "Zach Booth", "guid": "c00b90ea-3fd0-5ea5-9c35-d79012609535", "url": "https://pretalx.com/juliacon-2026/speaker/RL7GXH/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/CXRHSP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/CXRHSP/", "attachments": []}, {"guid": "28b0d4e4-0495-5519-a8c2-4cc7f52b850f", "code": "JNATTB", "id": 93485, "logo": "https://pretalx.com/media/juliacon-2026/submissions/JNATTB/image_54LnlCf.webp", "date": "2026-08-14T11:45:00+02:00", "start": "11:45", "end": "2026-08-14T12:15:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93485-building-a-quantum-control-startup-on-julia-piccolo-jl-compiled-sysimages-and-ai-agents", "url": "https://pretalx.com/juliacon-2026/talk/JNATTB/", "title": "Building a quantum control startup on Julia: Piccolo.jl, compiled sysimages, and AI agents", "subtitle": "", "track": "Julia in Industry", "type": "Short talk", "language": "en", "abstract": "Harmoniqs is a startup building quantum control infrastructure entirely in Julia. Our open-source stack, Piccolo.jl, and its private extension Piccolissimo.jl share one language from API to numerics. We discuss compiling and delivering Julia without exposing source code, deploying on HPC resources including GPU clusters, and how Julia's single-language design makes AI coding agents unusually effective for a small team. A case study in why Julia is ready for startups.", "description": "[Harmoniqs](https://harmoniqs.ai) builds control software infrastructure for quantum computing. Our entire stack is Julia, from the open-source [Piccolo.jl](https://github.com/harmoniqs/Piccolo.jl) framework for quantum optimal control to Piccolissimo.jl, a private extension providing specialized integrators, operators, and constraints for production workloads.\n\n**Why Julia for a startup?** The conventional wisdom says startups should use Python for speed-to-market and C++ for speed-to-solution. Julia gives us both. The same code a researcher writes to prototype a new integrator is the code that ships to customers. No rewrite step, no glue layer, no second language. For a small team, this is existential -- we cannot afford to maintain two codebases.\n\n**Compiled delivery without source exposure.** Julia is traditionally distributed as source, which is a problem when your code contains trade secrets. We have developed a compilation pipeline that delivers Piccolissimo as prebuilt sysimages -- protecting proprietary code while giving customers instant startup and zero compilation wait. We will discuss the challenges and tradeoffs of shipping Julia as a compiled product.\n\n**HPC and GPU deployment.** Piccolissimo runs on HPC resources including GPU clusters, leveraging Julia's native GPU and parallelism ecosystem. The same single-language advantage applies here: the control code, the numerics, and the GPU kernels are all Julia -- no CUDA C++ side-channel to maintain.\n\n**AI agents as force multipliers.** A single-language codebase turns out to be a gift for AI coding agents. An LLM can read the entire stack without context-switching between languages. We ship structured context files and have built reusable agent skills for common workflows -- problem setup, physics references, testing, demo generation. These agents now accelerate both internal development (new features, performance tuning) and user workflows (going from a gate specification to an optimized pulse conversationally). For a small team competing with well-funded incumbents, this is a genuine competitive advantage.\n\n**Open core, open science.** Piccolo.jl remains fully open source and has been used in peer-reviewed research on robust quantum control ([Kamen et al., arXiv:2602.10349](https://arxiv.org/abs/2602.10349)) and experimental demonstrations of universal dynamics on Rydberg-atom arrays ([Hu et al., arXiv:2508.19075](https://arxiv.org/abs/2508.19075)). Piccolissimo extends this with production-grade integrators and compiled delivery, but the science stays open.\n\nIn this talk, we share what we have learned building a quantum startup on Julia -- the wins, the workarounds, and why we would do it again.\n\n- [Harmoniqs](https://harmoniqs.ai)\n- [Piccolo.jl](https://github.com/harmoniqs/Piccolo.jl)\n- [Documentation](https://docs.harmoniqs.co/Piccolo/dev/)", "recording_license": "", "do_not_record": false, "persons": [{"code": "FV7D7C", "name": "Aaron Trowbridge", "avatar": null, "biography": "Currently co-founder and CEO at Harmoniqs, a startup building Julia-based quantum optimal control and calibration software. Previously, a research associate in the Robotics Institute at Carnegie Mellon University. An avid reader, climber, runner, and Bialetti coffee drinker.", "public_name": "Aaron Trowbridge", "guid": "8298c138-9e35-55b4-888c-3b526de617af", "url": "https://pretalx.com/juliacon-2026/speaker/FV7D7C/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/JNATTB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/JNATTB/", "attachments": []}, {"guid": "d95e8e6e-129e-55bb-b555-a867cbe35c71", "code": "ARE8YL", "id": 95483, "logo": "https://pretalx.com/media/juliacon-2026/submissions/ARE8YL/image_hI036qj.webp", "date": "2026-08-14T12:15:00+02:00", "start": "12:15", "end": "2026-08-14T12:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-95483-evolution-and-application-of-model-based-design-in-boeing-vertical-lift-vehicle-management-systems", "url": "https://pretalx.com/juliacon-2026/talk/ARE8YL/", "title": "Evolution and Application of Model-Based Design in Boeing Vertical Lift Vehicle Management Systems", "subtitle": "", "track": "Julia in Industry", "type": "Short talk", "language": "en", "abstract": "This presentation reviews the evolution and application of model-based design in Boeing\u2019s Vertical Lift Vehicle Management Systems (VMS). It highlights the long-standing use of MATLAB/Simulink for requirements development, built-in test implementation, component modeling, and software/hardware-in-the-loop testing, while also exploring the emerging potential of Julia/Dyad for acausal physics-based modeling, reusable libraries, and faster execution. The presentation includes a recent IRAD effort demonstrating parallel modeling in Simulink and Dyad and discusses how these tools may support future aircraft development processes aligned with ARP-4754B.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"code": "VN9WXZ", "name": "Matt Yu", "avatar": "https://pretalx.com/media/avatars/KLPNPK_r50cBb4.webp", "biography": "Boeing Flight Controls Engineer", "public_name": "Matt Yu", "guid": "9f176303-29fe-515e-8281-dfa08a2d155d", "url": "https://pretalx.com/juliacon-2026/speaker/VN9WXZ/"}, {"code": "7QEEZY", "name": "Fernando Dones", "avatar": null, "biography": "Fernando Dones, Boeing", "public_name": "Fernando Dones", "guid": "249bd59b-1e84-5845-8e86-58b08ab890ae", "url": "https://pretalx.com/juliacon-2026/speaker/7QEEZY/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/ARE8YL/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/ARE8YL/", "attachments": []}, {"guid": "e64509ba-179f-5b0a-a1ff-c6821c45ae00", "code": "BR3CQM", "id": 95485, "logo": "https://pretalx.com/media/juliacon-2026/submissions/BR3CQM/image_wmD0vTB.webp", "date": "2026-08-14T12:30:00+02:00", "start": "12:30", "end": "2026-08-14T12:45:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-95485-bringing-scientific-machine-learning-to-industrial-digital-twins-with-dyad-and-ansys-twinai", "url": "https://pretalx.com/juliacon-2026/talk/BR3CQM/", "title": "Bringing Scientific Machine Learning to Industrial Digital Twins with Dyad and Ansys TwinAI", "subtitle": "", "track": "Julia in Industry", "type": "Short talk", "language": "en", "abstract": "Digital twins are evolving from simulation models into adaptive, continuously improving representations of real systems. This talk introduces Ansys TwinAI\u2122, part of the Synopsys portfolio, and outlines how the integration of JuliaHub's Dyad brings Scientific Machine Learning (SciML), differentiable programming, and physics-informed artificial intelligence into digital twin workflows. We will highlight the value of hybrid digital twins and present representative engineering use cases.", "description": "Digital twins are increasingly expected to do more than replicate system behaviour\u2014they must adapt to new data, improve over time, and remain trustworthy for engineering decision-making. Achieving this requires combining the predictive power of artificial intelligence with the robustness and explainability of physics-based models.\n\nIn this session, we will introduce Ansys, now part of Synopsys, and provide a brief overview of Ansys TwinAI\u2122, artificial intelligence-powered digital twin software designed to support validation, operation, and deployment of digital twins in cloud environments. We will then discuss the recently announced collaboration between Synopsys and JuliaHub to integrate Dyad, JuliaHub's next-generation simulation platform, into TwinAI.\n\nThe core of the talk will focus on why bringing Julia and Dyad into the TwinAI ecosystem matters. Dyad combines component-based acausal modelling, automatic equation generation, Scientific Machine Learning (SciML), and differentiable programming, enabling the creation of hybrid digital twins that blend first-principles physics with data-driven learning. These capabilities create opportunities to improve model fidelity, accelerate model development, and continuously update digital twins as operational data becomes available.\n\nThe session will provide a high-level overview of representative use cases, including:\n\nAdaptive asset monitoring and predictive maintenance, where physics-based models are enhanced using operational data to improve forecasting accuracy and fault detection.\nEngineering system optimisation and calibration, where differentiable models and SciML techniques enable efficient tuning of digital twins against measured data while preserving physical consistency.\nAttendees will gain an understanding of the strategic vision behind the TwinAI\u2013Dyad integration, the role that Julia and SciML can play in next-generation digital twins, and how engineers can benefit from hybrid approaches that bridge simulation and real-world operation.\n\nThis talk is intended for both Julia users interested in industrial deployment of SciML and engineers exploring the future of AI-powered digital twins.", "recording_license": "", "do_not_record": false, "persons": [{"code": "WXCVL9", "name": "Edward Carman", "avatar": "https://pretalx.com/media/avatars/TZQFZU_8QhG9tm.webp", "biography": "Edward Carman is a Lead Application Engineer at Ansys, part of Synopsys, specializing in engineering analysis and model-based approaches to product development, with a particular focus on packaging and deployment of physics-based simulation models into workflow automation, democratisation and digital twin applications.", "public_name": "Edward Carman", "guid": "11e8a3ef-e2c5-54ca-a2b6-4cfe95b8ab63", "url": "https://pretalx.com/juliacon-2026/speaker/WXCVL9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/BR3CQM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/BR3CQM/", "attachments": []}, {"guid": "24467898-eb0d-5540-80f6-401f663f84a5", "code": "RKVDD9", "id": 95484, "logo": "https://pretalx.com/media/juliacon-2026/submissions/RKVDD9/image_wiPM7Ir.webp", "date": "2026-08-14T12:45:00+02:00", "start": "12:45", "end": "2026-08-14T13:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-95484-from-design-to-orbit-julia-powered-gnc-for-geo-satellites", "url": "https://pretalx.com/juliacon-2026/talk/RKVDD9/", "title": "From Design to Orbit: Julia-Powered GNC for GEO Satellites", "subtitle": "", "track": "Julia in Industry", "type": "Short talk", "language": "en", "abstract": "Guidance, Navigation, and Control (GNC) for geostationary satellites is traditionally built with a patchwork of tools: MATLAB for analysis, C/C++ for flight software, and custom scripts for simulation and verification. This session details a case study in replacing those various toolsets with a single Julia-based workflow that runs from early design to orbit.", "description": "Julia can be used for:\n   - The plant and \"truth\" models for orbital and attitude dynamics\n   - The GNC flight software module itself\n   - The integrated simulation and requirement verification framework\n   - The Monte Carlo engine (multi-process, multi-machine)\n   - The driver for hardware-in-the-loop (HIL) testing", "recording_license": "", "do_not_record": false, "persons": [{"code": "MATYNK", "name": "Nik Descher", "avatar": null, "biography": "Nik Descher, Boeing Space", "public_name": "Nik Descher", "guid": "3d4165c9-e089-5145-83a5-e33fa65fae1b", "url": "https://pretalx.com/juliacon-2026/speaker/MATYNK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/RKVDD9/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/RKVDD9/", "attachments": []}, {"guid": "3c5f3d5a-670e-5ed4-9ca2-5fada721ef9a", "code": "TVKAZB", "id": 93152, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TVKAZB/image_9ViEcZz.webp", "date": "2026-08-14T14:30:00+02:00", "start": "14:30", "end": "2026-08-14T15:30:00+02:00", "duration": "01:00", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-93152-dagger-jl-birds-of-a-feather", "url": "https://pretalx.com/juliacon-2026/talk/TVKAZB/", "title": "Dagger.jl Birds of a Feather", "subtitle": "", "track": "General", "type": "Birds of Feather (BoF)", "language": "en", "abstract": "Round-table open discussion of everything about Dagger.jl. Success or failure stories, gripes and joys, ideas for new features, discussion of existing bugs or missing documentation, and more!", "description": "Dagger.jl is a rising star in the landscape of High Performance Computing, striving to make parallel computing easy and productive for everyone. Dagger has grown significantly over the last few years, and many more improvements are already planned for 2026.\n\nBut during this BoF, we want to hear from you, the community, to understand why you do (or don't) use Dagger to solve your problems, and how Dagger can do better. We welcome both positive feedback and negative constructive criticism, and would like to find out what you want to see change in Dagger in 2026, 2027, and beyond! We'll also cover some of the new features and benchmarks of Dagger so you can see what new things have dropped since 2025.\n\nWe welcome past and current users of Dagger, and also those just interested in sitting in to learn more about Dagger.", "recording_license": "", "do_not_record": false, "persons": [{"code": "GRFD9D", "name": "Julian P Samaroo", "avatar": "https://pretalx.com/media/avatars/GRFD9D_X04BPZD.webp", "biography": "Julian is a Research Software Engineer at MIT's JuliaLab, where he focuses on improving Julia's support for HPC and GPU computing. Julian has previously authored and maintained the AMDGPU.jl package (for programming AMD's GPUs from Julia), and now focuses his efforts on maintaining and developing the Dagger.jl package, to improve the state of productive parallel programming.", "public_name": "Julian P Samaroo", "guid": "545e5d52-47fb-56ff-99ce-9ee52d0dc560", "url": "https://pretalx.com/juliacon-2026/speaker/GRFD9D/"}, {"code": "VXYFQN", "name": "Felipe Tom\u00e9", "avatar": "https://pretalx.com/media/avatars/JLJBPE_ZnCs9zO.webp", "biography": "Consultant at MIT's JuliaLab, Co-maintainer of Dagger. My interests span from more broad topics such as the accessibility and educational initiatives for parallel computing to Applied Physics and Numerical Linear Algebra.", "public_name": "Felipe Tom\u00e9", "guid": "1cd1cbc0-bbe9-50b6-8986-e160a69196aa", "url": "https://pretalx.com/juliacon-2026/speaker/VXYFQN/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TVKAZB/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TVKAZB/", "attachments": [{"title": "flyer-TVKAZB", "url": "/media/juliacon-2026/submissions/TVKAZB/resources/TVKAZB_HkoRrgk.png", "type": "related"}]}, {"guid": "493137ee-bcd5-5def-9c6e-3e837b2402c8", "code": "EHEXV3", "id": 92654, "logo": "https://pretalx.com/media/juliacon-2026/submissions/EHEXV3/image_ERLkkX8.webp", "date": "2026-08-14T15:45:00+02:00", "start": "15:45", "end": "2026-08-14T16:45:00+02:00", "duration": "01:00", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92654-birds-of-a-feather-julia-for-biology", "url": "https://pretalx.com/juliacon-2026/talk/EHEXV3/", "title": "Birds of a Feather: Julia for Biology", "subtitle": "", "track": "General", "type": "Birds of Feather (BoF)", "language": "en", "abstract": "Round table discussion on using Julia for computational biology: Use cases, limitations and concerns we should address, and where to focus our collective efforts.", "description": "Julia is especially well suited for computational biology, and BioJulia was one of the earliest Julia organizations. Due to the decentralized and ad hoc nature of Julia communities, we rarely have a chance to reflect and take stock of how the organization is doing, and whether we are serving our users and our own research needs to the best of our ability.\n\nIn this Birds of Feather, we want to hear from users and developers about how and why they use Julia for computational biology, or what problems prevent them from doing so. We welcome feedback on the BioJulia organization, the ecosystem of Julia for computational biology, and the state of relevant packages.\nAlthough no one has the authority to delegate developer efforts, we encourage discussion about whether our collective programming effort is well spent, and how BioJulia and other Julia organizations can improve to better serve our users.\n\nThis discussion is for everyone interested in Julia for computational biology; whether you are a BioJulia developer, a Julia developer in another field interested in biology, or someone interested in what Julia can bring to biology.", "recording_license": "", "do_not_record": true, "persons": [{"code": "DHLEWB", "name": "Jakob Nybo Andersen", "avatar": "https://pretalx.com/media/avatars/TB3PQB_xiikT0J.webp", "biography": "I am a research software engineer from Copenhagen, Denmark.\nI currently work for the Danish health authorities, writing software for pathogen surveillance. I am trained as a molecular biologist, and have previously been working as an academic researching bioinformatics.\nI program in Python, Rust and Julia, and am an active developer in the BioJulia ecosystem. I write Julia packages for efficient I/O and parsing, and foundational bioinformatics functionality such as BioSequences and Kmers.jl.", "public_name": "Jakob Nybo Andersen", "guid": "38b80cb2-0f3d-5559-9ecd-224070b8b191", "url": "https://pretalx.com/juliacon-2026/speaker/DHLEWB/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/EHEXV3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/EHEXV3/", "attachments": []}, {"guid": "72f2f44e-eb1a-50f9-9c46-c0af40f34f1e", "code": "3KTAGM", "id": 92942, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3KTAGM/image_9DzXoru.webp", "date": "2026-08-14T16:45:00+02:00", "start": "16:45", "end": "2026-08-14T17:15:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Audi Max", "slug": "juliacon-2026-92942-scalable-bayesian-spatial-modeling-in-julia-with-gmrfs-and-inla", "url": "https://pretalx.com/juliacon-2026/talk/3KTAGM/", "title": "Scalable Bayesian Spatial Modeling in Julia with GMRFs and INLA", "subtitle": "", "track": "General", "type": "Long talk", "language": "en", "abstract": "Bayesian spatial modeling is critical across science, yet most practitioners are locked into R due to R-INLA.\nWe present a Julia ecosystem to change this: **GaussianMarkovRandomFields.jl** provides fast sparse precision-based inference via SPDE discretizations & more, while **IntegratedNestedLaplace.jl** brings the full INLA methodology to Julia with a familiar formula interface.\nWe demonstrate the ecosystem on spatial disease mapping, showing competitive results with R-INLA and native Julia advantages.", "description": "Bayesian spatial modeling underpins research across epidemiology, ecology, climate science, and the social sciences. The dominant tool for this is R-INLA, an R package implementing Integrated Nested Laplace Approximation for fast approximate Bayesian inference in latent Gaussian models. Despite its success, R-INLA effectively locks researchers into the R ecosystem - there has been no equivalent in Julia or Python.\n\nThis talk presents two packages that together bring this capability to Julia:\n\n**GaussianMarkovRandomFields.jl** provides the sparse precision foundation. It constructs Gaussian Markov random fields e.g. via finite element discretizations of stochastic partial differential equations (SPDEs), turning dense covariance matrices into sparse precision matrices that scale to large-scale inference problems.\nThe package supports multiple solver backends via LinearSolve.jl, autodiff support, and integrates with Ferrite.jl for finite element assembly.\n\n**Latte.jl** implements the full INLA methodology on top of this foundation:\nGaussian approximation of the latent posterior, grid-based hyperparameter exploration with numerical integration, and Laplace-corrected marginals for the latent field, all in a probabilistic programming interface.\n\nThe talk will walk through the ecosystem from theory to practice, culminating in a live demonstration of a complete nontrivial spatial analysis: specifying the model via the PPL, fitting with INLA, and visualizing the results - all in Julia.", "recording_license": "", "do_not_record": false, "persons": [{"code": "WASMCV", "name": "Tim Weiland", "avatar": "https://pretalx.com/media/avatars/UN7NCR_EBWCLlH.webp", "biography": "Tim Weiland is a PhD student in the Methods of Machine Learning group at the University of T\u00fcbingen, where he works on scalable probabilistic PDE solvers.\nHis research combines Bayesian inference, sparse linear algebra, and physics-informed priors to make uncertainty quantification practical for large-scale scientific computing problems.", "public_name": "Tim Weiland", "guid": "228e0fc4-6af9-5d84-b695-4389a8399690", "url": "https://pretalx.com/juliacon-2026/speaker/WASMCV/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3KTAGM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3KTAGM/", "attachments": []}], "Alte Mensa \u2014 Atrium Maximum": [{"guid": "a854f1d4-4a16-5d60-8d76-415d27e30a53", "code": "TGC3ZM", "id": 93375, "logo": "https://pretalx.com/media/juliacon-2026/submissions/TGC3ZM/image_WgMAbUs.webp", "date": "2026-08-14T10:00:00+02:00", "start": "10:00", "end": "2026-08-14T10:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-93375-how-implementing-a-differentiable-model-for-electron-microscopy-epma-accelerated-the-forward-simulation", "url": "https://pretalx.com/juliacon-2026/talk/TGC3ZM/", "title": "How implementing a differentiable model for Electron Microscopy (EPMA) accelerated the forward simulation", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Short talk", "language": "en", "abstract": "Electron Probe Microanalysis (EPMA) is an imaging technique for the quantitative analysis of solid material samples relying on measurements of characteristic X-ray emission induced by electron irradiation.\nThe determination of the material constitutes an inverse problem, hence an efficient reconstruction requires differentiability of the forward model.\nThe mathematical model employed in EPMA is governed by a linear transport equation that for heterogeneous materials is commonly approximated using Monte Carlo simulation, where statistical noise complicates the computation of gradients.\nFor reconstruction, there exist surrogate models that are well tested in practice, but are very restrictive in the parametrization of the material, allowing only homogeneous or depth-layered materials, which ultimately limits the spatial resolution of quantitative analysis in EPMA.\n\nIn this short talk, we present an implementation of a deterministic, heterogeneous, and differentiable model for EPMA in Julia.\nReconstruction can then be implemented as a gradient-based optimization using the model as a PDE constraint.\nCompatibility with algorithmic differentiation allows us to tailor the material parametrization to a set of quantities of interest, depending on the requirements of a specific sample.\nReconstruction results using realistic as well as synthetic measurements demonstrate potential for further development.\n\nAdditionally, we briefly discuss a structural similarity of the forward model in EPMA to the structure in which adjoint methods can be effectively applied for gradient computation. It allows us to \"apply adjoints twice\" leading also to a more efficient computation of the forward problem, ultimately accelerating reconstruction approaches.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "KGWJBD", "name": "Tamme Claus", "avatar": null, "biography": "Since 04/2022: PhD Student at ACoM, RWTH Aachen University\n10/2018 - 11/2021: Master of Science in Computational Engineering Science (CES), RWTH Aachen University\n10/2014 - 10/2018: Bachelor of Science in Computational Engineering Science (CES), RWTH Aachen University", "public_name": "Tamme Claus", "guid": "7cad6aa9-3cc5-5c98-9da7-4750f4f0d039", "url": "https://pretalx.com/juliacon-2026/speaker/KGWJBD/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/TGC3ZM/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/TGC3ZM/", "attachments": []}, {"guid": "00784b2a-3038-58f3-ad75-0305123fb1f5", "code": "YCXBKT", "id": 92046, "logo": "https://pretalx.com/media/juliacon-2026/submissions/YCXBKT/image_AvW0U8P.webp", "date": "2026-08-14T10:15:00+02:00", "start": "10:15", "end": "2026-08-14T10:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92046-fluxoptics-jl-a-composable-framework-for-optical-inverse-design-in-julia", "url": "https://pretalx.com/juliacon-2026/talk/YCXBKT/", "title": "FluxOptics.jl: A Composable Framework for Optical Inverse Design in Julia", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Short talk", "language": "en", "abstract": "[FluxOptics.jl](https://github.com/anscoil/FluxOptics.jl) is a Julia framework for differentiable optical inverse design. It enables both rapid prototyping relying on automatic differentiation with Zygote.jl and production performance through algorithmic differentiation and custom gradient rules. The framework provides memory-efficient optimization through controlled buffer management, while maintaining a composable architecture. Benchmarks against JAX show competitive performance while handling larger problems, demonstrating Julia's strengths for computational physics applications.", "description": "## Context: optical systems and inverse design\n\nThe laws of optics are expressed as a set of partial differential equations (PDEs) known as Maxwell's equations. Finite difference and finite element methods are the standard and most accurate methods to solve rigorously such equations at the nanoscale level, but are intractable to describe large optical systems made of traditional elements such as lenses, mirrors and spatial light modulators. Instead, some simplifying assumptions are commonly made to transform the original PDEs into simpler equations that can often be solved efficiently using Fourier methods, while remaining accurate enough for a large number of applications.\n\nAn optical system can naturally be described as a cascade of optical elements through which light propagates. Recent work has drawn analogies with neural networks, employing the term Diffractive Optical Neural Networks (DONNs), where optical components act as trainable weights and physical propagation models replace traditional convolutions or matrix multiplications. This vision has led to using machine learning frameworks such as Tensorflow, Pytorch and more recently JAX, to optimize optical systems for specific functions like beam shaping and mode multiplexing, with applications ranging from telecommunications to optical computing.\n\n## FluxOptics.jl architecture\n\n[FluxOptics.jl](https://github.com/anscoil/FluxOptics.jl) brings this composable approach to Julia with an architecture that allows selecting the trade-off between fast prototyping and performance when developing a new differentiable optical component. A key challenge in inverse design problems is memory management during the forward pass and gradient computation. One can easily achieve fast prototyping of differentiable programming in Julia by writing pure functions and using Zygote.jl. However, this often limits the performance of the forward model which could benefit from in-place mutable operations, and also leads to dynamic memory allocation which triggers the garbage collector and slows down the computation.\n\nTo achieve the best efficiency, FluxOptics.jl implements algorithmic differentiation through manual gradient rules defined by extending an interface that leverages ChainRulesCore.jl. This enables controlled buffer management and reuse of intermediate computations during backpropagation. The framework distinguishes between `Pure` components for rapid prototyping using automatic differentiation and `Custom` components for production-level optimization where developers write efficient forward and backward passes with fine-grained control over memory allocation, while still relying on Zygote.jl for composing gradients across the system.\n\nComponents can be marked `Static` for fixed geometry or `Trainable` for optimizable parameters. The `FieldProbe` component captures intermediate field states, and makes them accessible at the system output through a dictionary, which is useful for multi-objective optimization or visualization. Adjacent non-trainable components with compatible types can be automatically merged for efficiency, such as consecutive phase masks or propagation steps.\n\nThe framework integrates naturally with Optimisers.jl and extends it with proximal operators for constrained optimization including Total-Variation regularization, sparsity-inducing penalties (Iterative Shrinkage-Thresholding Algorithm and its accelerated variant FISTA), and box constraints.\n\n## Performance and validation\n\nTo enable honest comparison with the Python ecosystem, [JaxOptics](https://github.com/anscoil/jaxoptics) was developed as a minimal JAX reimplementation covering the core free-space propagation methods and phase modulation. Preliminary benchmarks show better performance on isolated propagation tasks, while JAX's XLA compiler can show a slight advantage on certain optimization workflows. However, Julia's key strength lies in memory efficiency and the ability to handle larger problems. The framework has been published in JOSS and demonstrates its capabilities through real research applications including field retrieval, waveguide tomography, multimode intensity shaping, and 45-mode Hermite-Gaussian mode sorting.\n\nThis talk presents FluxOptics.jl's architecture and demonstrates design patterns for composable differentiable systems that could extend to other domains of computational physics beyond optics.", "recording_license": "", "do_not_record": true, "persons": [{"code": "8RAM93", "name": "Nicolas Barr\u00e9", "avatar": "https://pretalx.com/media/avatars/8RAM93_QN0GjYE.webp", "biography": "Independent computational physicist working on optical propagation, inverse design, and differentiable programming. Former postdoc at University of Innsbruck, FAU Erlangen, and University of Rennes. Developer of [FluxOptics.jl](https://github.com/anscoil/FluxOptics.jl)", "public_name": "Nicolas Barr\u00e9", "guid": "ab37ad79-ecc0-51a7-b5f9-48fbc575d764", "url": "https://pretalx.com/juliacon-2026/speaker/8RAM93/"}], "links": [{"title": "Github repository", "url": "https://github.com/anscoil/FluxOptics.jl", "type": "related"}, {"title": "JOSS article", "url": "https://joss.theoj.org/papers/10.21105/joss.09734", "type": "related"}], "feedback_url": "https://pretalx.com/juliacon-2026/talk/YCXBKT/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/YCXBKT/", "attachments": []}, {"guid": "0785259f-6fe1-5b9f-b2d6-a671bfaa04d9", "code": "GFVKR3", "id": 93426, "logo": "https://pretalx.com/media/juliacon-2026/submissions/GFVKR3/image_2FkzCzx.webp", "date": "2026-08-14T10:30:00+02:00", "start": "10:30", "end": "2026-08-14T11:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-93426-automatic-and-fixed-point-differentiation-in-tensor-network-algorithms", "url": "https://pretalx.com/juliacon-2026/talk/GFVKR3/", "title": "Automatic and fixed-point differentiation in tensor network algorithms", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Long talk", "language": "en", "abstract": "Automatic differentiation (AD) is gaining ground as a technique for optimization of tensor networks (TN), which are widely used simulation tools in quantum computing, condensed matter, and high energy physics. In this talk we will provide an overview of the ongoing work to add support for end-to-end AD in our large, complex set of physics simulation packages at the \"QuantumKitHub\". Efficient AD of these networks involves differentiation through complex linear algebra, complicated tensor operations, and other constructs that push the boundaries of what Julia's AD frameworks are capable of.", "description": "In recent years, AD-based optimization for tensor networks has become more popular. Using AD in these cases requires support for complex numbers, differentiation through linear algebra factorizations, and other features that are often not at the core of \"traditional\" AD frameworks such as JAX or PyTorch. For these reasons, the flexibility and extensibility of Julia's next generation AD tooling allows us to explore the use of AD in large scale simulation of quantum systems. In this talk we will discuss some of the challenges we have encountered integrating AD into the TN workflow, such as supporting rules for truncated SVD for complex double valued matrices, and some of the innovative techniques that AD allows us to explore, such as the ongoing research into the optimization of tensor network states based on performing AD around a fixed point of an operator.", "recording_license": "", "do_not_record": false, "persons": [{"code": "YYDPCZ", "name": "Katharine Hyatt", "avatar": null, "biography": "I am a Julia contributor since 2015. I work mostly on GPUs, quantum packages, and linear algebra.", "public_name": "Katharine Hyatt", "guid": "729bd10c-f6f5-53bf-b7b6-fff8ee7a09ae", "url": "https://pretalx.com/juliacon-2026/speaker/YYDPCZ/"}, {"code": "DP3BCC", "name": "Lukas Devos", "avatar": null, "biography": "Software Research Fellow at the Flatiron Institute, CCQ, studying tensor network methods and algorithms for classical and quantum physics simulations.", "public_name": "Lukas Devos", "guid": "118e4bd4-be2d-5977-ba8c-2662d450fb33", "url": "https://pretalx.com/juliacon-2026/speaker/DP3BCC/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/GFVKR3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/GFVKR3/", "attachments": []}, {"guid": "9e03b102-9ef5-5782-a351-077d79e0182d", "code": "EGUEJP", "id": 92718, "logo": "https://pretalx.com/media/juliacon-2026/submissions/EGUEJP/image_Q4N6AaH.webp", "date": "2026-08-14T11:15:00+02:00", "start": "11:15", "end": "2026-08-14T11:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92718-differentiable-climate-modeling-calibrating-speedyweather-with-enzyme", "url": "https://pretalx.com/juliacon-2026/talk/EGUEJP/", "title": "Differentiable Climate Modeling: Calibrating SpeedyWeather with Enzyme", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Short talk", "language": "en", "abstract": "Climate models rely on parameterizations that are traditionally tuned manually. We present a differentiable calibration framework using Enzyme.jl to compute exact reverse mode gradients of energy-balance diagnostics in SpeedyWeather.jl. By batching single- timestep gradients across chaotic dynamics, we enable systematic, reproducible optimization of shortwave radiation parameters, establishing an extensible workflow for objective calibration in Earth system models.", "description": "Climate models simulate many atmospheric processes such as radiation, convection, clouds, and turbulent fluxes. As these processes are not explicitly resolved, they are represented through so-called parameterizations, which fit hundreds of parameters. Traditionally, tuning these parameters is a manual, expert-driven process guided by physical intuition and iterative experimentation given the computational cost of climate models. In this talk, we present a different approach: With automatic differentiation via Enzyme.jl we calibrate the parameters in  SpeedyWeather.jl towards the observed Earth\u2019s global energy budget.  We differentiate individual timesteps in reverse mode but batch to stabilise gradients across the chaotic weather time scales. Given a target energy budget, we train on data that is continuously simulated and therefore allow slow processes to adapt despite single timestep gradients, which bypasses the need for checkpointing. Exact gradients are computed of physical diagnostics, such as top-of-atmosphere radiation and surface fluxes, with respect to model parameters that we chose for tuning. Our work focuses on the shortwave radiation, including cloud and surface albedos as the primary source of energy in the Earth system. Enzyme propagates the loss back through the full atmospheric physics, including radiative transfer and surface processes. We use Julia's optimization ecosystem for systematic calibration. SpeedyWeather is a large code base with parameters placed in most branches of large nested structs. For this we implemented a parameter handling scheme that allows convenient parameter updates and model reconstruction despite a missing central model configuration interface. The result is not only improved agreement with observed energy balance, but a reproducible and extensible workflow for parameter optimization in Earth system models. Beyond climate science, this work demonstrates how Julia enables new paradigms for scientific model development leveraging automatic differentiation for objective calibration.\n\nAuthors: \n\nNiklas Viebig (1,2), Milan Kl\u00f6wer (1), Maximilian Gelbrecht (3,4) , Brian Groenke (3), Gregory Munday (1)\n\n1. University of Oxford, UK\n2. ETH Z\u00fcrich, Switzerland\n3. Potsdam Institute for Climate Impact Research, Germany\n4. Technical University of Munich, Germany", "recording_license": "", "do_not_record": false, "persons": [{"code": "UWLWET", "name": "Niklas Viebig", "avatar": "https://pretalx.com/media/avatars/787YWJ_j6AIbZs.webp", "biography": "Master\u2019s student in Physics at ETH Zurich, currently completing my Master\u2019s thesis in the [climate modeling group ](https://climate-modelling.github.io)at AOPP, University of Oxford. Im researching differentiable programming and systematic parameter calibration for Earth system models, with interests in exoplanet climates, high-performance computing, and scientific software engineering.", "public_name": "Niklas Viebig", "guid": "6da790fe-d52e-57e6-9775-5fda4d6081c0", "url": "https://pretalx.com/juliacon-2026/speaker/UWLWET/"}, {"code": "A9SQSW", "name": "Milan Kl\u00f6wer", "avatar": "https://pretalx.com/media/avatars/A9SQSW_nKoqOOA.webp", "biography": "Milan Kl\u00f6wer is a NERC Independent Research Fellow at the University of Oxford. He did his postdoc at the Massachusetts Institute of Technology (MIT) working on climate model development in Julia. He started SpeedyWeather.jl, a global atmospheric model designed as a research playground to develop prototype ideas on machine-learned representations of climate processes and computationally efficient climate models. He also works on low precision computing, data compression and information theory, predictability of weather and climate, and software engineering.", "public_name": "Milan Kl\u00f6wer", "guid": "da34690e-e394-5c01-a2f8-6876c357d29c", "url": "https://pretalx.com/juliacon-2026/speaker/A9SQSW/"}, {"code": "FRX3ZM", "name": "Maximilian Gelbrecht", "avatar": null, "biography": "Researching differentiable programming and machine learning for Earth system models and dynamical systems", "public_name": "Maximilian Gelbrecht", "guid": "73255bbe-2284-5620-b0a1-2c3519a50514", "url": "https://pretalx.com/juliacon-2026/speaker/FRX3ZM/"}, {"code": "3HQE7H", "name": "Greg Munday", "avatar": "https://pretalx.com/media/avatars/WTKEQU_VoAJ6Yz.webp", "biography": "DPhil Research Student at the University of Oxford. Interested in all things hybrid climate modelling.", "public_name": "Greg Munday", "guid": "08798aac-997c-5346-b964-4cc6dc6aac6b", "url": "https://pretalx.com/juliacon-2026/speaker/3HQE7H/"}, {"code": "KCHMD8", "name": "Brian Groenke", "avatar": "https://pretalx.com/media/avatars/KCHMD8_GHddQ9c.webp", "biography": "I am a postdoctoral researcher at the Potsdam Institute for Climate Impact Research in Potsdam, Germany. My primary research interests are in applications of differentiable and probabilistic programming, uncertainty quantification, and scientific machine learning to geophysical modeling of Earth systems.\n\nIn my PhD, I worked on probabilistic inverse modeling of subsurface heat transfer in terrestrial permafrost. Prior to that, I worked on the application generative deep learning to statistical downscaling of climate and weather variables from coarse scale model outputs.\n\nMy industry background consists primarily of software engineering, both front-end and back-end development, with a wide range of frameworks and languages.", "public_name": "Brian Groenke", "guid": "69b61b03-ee37-57b3-b183-914b8eb4f18b", "url": "https://pretalx.com/juliacon-2026/speaker/KCHMD8/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/EGUEJP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/EGUEJP/", "attachments": []}, {"guid": "e450f830-4486-5762-8571-3b577a8d9bbb", "code": "KKHD78", "id": 92562, "logo": "https://pretalx.com/media/juliacon-2026/submissions/KKHD78/image_yyQXWFC.webp", "date": "2026-08-14T11:30:00+02:00", "start": "11:30", "end": "2026-08-14T11:45:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92562-differentiating-functional-mock-up-units-fmus-with-enzyme-fast-ad-for-black-box-simulation-models", "url": "https://pretalx.com/juliacon-2026/talk/KKHD78/", "title": "Differentiating Functional Mock-up Units (FMUs) with Enzyme: Fast AD for Black-Box Simulation Models", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Short talk", "language": "en", "abstract": "Functional Mock-up Units (FMUs) are widely used in industry for exchanging dynamical system models, but their black-box binary nature makes them inaccessible to traditional AD tools. Built-in derivative support in the FMI standard is limited in scope and often relies on slow finite differences. We present a novel approach: by embedding LLVM bitcode into FMU binaries during compilation, we make them accessible to Enzyme.jl, enabling fast, automatic differentiation of virtually any FMU function.", "description": "The Functional Mock-up Interface standard (FMI) is widely used for the exchange of dynamical system models, particularly in industry. Computing derivatives of these models is relevant for a variety of use-cases, including optimization, control, and building hybrid models that combine physics-based simulations with machine learning.\n\t\nThe Julia ecosystem is already uniquely positioned in this space. Packages such as FMISensitivity.jl and FMIFlux.jl enable computation of various derivatives, for example with respect to solutions, even through discontinuities. This makes Julia the only currently viable platform for working with FMUs in a differentiable programming context.  \n\t\nHowever, FMUs are generally distributed as black-box binaries, which makes them inaccessible to traditional automatic differentiation tools. The FMI standard does include some built-in mechanisms for providing derivatives. But these are limited: they do not cover all the kinds of derivatives one might want to compute (such as derivatives with respect to discontinuities or time). Often they are realized through finite differences internally, if present at all. There are active efforts to address this by enhancing the FMI specification [1], but this path requires tool vendors to implement additional functionality in their tools with FMI support.  \n\t\nThis talk presents a different approach that leverages the LLVM ecosystem and Enzyme.jl. By embedding the LLVM bitcode generated during compilation of the FMU into the binary itself, we can make the compiled code accessible to Enzyme, which can then generate fast, exact derivatives for virtually any function the FMU provides. These also integrate neatly with other code from the Julia ecosystem. In practice, this yields speedups of multiple orders of magnitude over finite differencing in some cases, while in the best case requiring only passing some additional compiler flags during the FMU's compilation from source code.  \n\nThe talk will cover the approach, discuss some challenges we encountered, and demonstrate performance gains.  \n\t\n[1] T. Thummerer, H. Olsson, C. Song, J. Gundermann, T. Blochwitz, and L. Mikelsons, \u201cLS-SA: Developing an FMI layered standard for holistic & efficient sensitivity analysis of FMUs,\u201d Link\u00f6ping Electronic Conference Proceedings, vol. 218. Link\u00f6ping University Electronic Press, Oct. 24, 2025. doi: 10.3384/ecp218681.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UR8TDR", "name": "Valentin H\u00f6pfner", "avatar": "https://pretalx.com/media/avatars/BVBYFF_I0S3HFR.webp", "biography": "Research scientist and PhD student @ University of Augsburg, [chair of mechatronics](https://www.uni-augsburg.de/de/fakultaet/fai/informatik/prof/imech/)\n[GitHub](https://github.com/halentin)", "public_name": "Valentin H\u00f6pfner", "guid": "258b03ee-71b9-550b-9d00-8e6bb13f19e4", "url": "https://pretalx.com/juliacon-2026/speaker/UR8TDR/"}, {"code": "8U88RU", "name": "Lars Mikelsons", "avatar": null, "biography": "Lars Mikelsons holds a diploma in Mathematics and a Ph.D. in Mechatronics. He began his professional career at Bosch Corporate Research before transitioning to academia. Currently, he is the Head of the Chair for Mechatronics at the University of Augsburg. His research focuses on Scientific Machine Learning and Mechatronic Systems Engineering, contributing to the advancement of intelligent, data-driven approaches in engineering applications.", "public_name": "Lars Mikelsons", "guid": "fb4a744b-7a9c-5616-ae25-9c7d65453f28", "url": "https://pretalx.com/juliacon-2026/speaker/8U88RU/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/KKHD78/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/KKHD78/", "attachments": []}, {"guid": "4199634c-2ebf-5d44-843f-29e1f5943f0f", "code": "3RDRVU", "id": 92664, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3RDRVU/image_Dlvdlgq.webp", "date": "2026-08-14T11:45:00+02:00", "start": "11:45", "end": "2026-08-14T12:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92664-dj4oceananigans-differentiating-an-ocean-general-circulation-model-for-gradient-based-parameter-calibration-and-online-learning", "url": "https://pretalx.com/juliacon-2026/talk/3RDRVU/", "title": "DJ4Oceananigans: Differentiating an ocean general circulation model for gradient-based parameter calibration and online learning", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Short talk", "language": "en", "abstract": "Ocean models simulate complex physics, but struggle from inherent limitations and under-resolved phenomena. This motivates the use of inverse and machine learning methods to inform models with data. We have implemented automatic differentiation in the Ocean modeling package Oceananigans.jl, through the use and enhancement of compiler tools Enzyme.jl and Reactant.jl. Using these open-source packages, we generate gradients for applications like parameter estimation and embedded ML techniques.", "description": "Ocean general circulation models (GCMs) are used for operational forecasting and climate modeling. They simulate a wide range of physical processes, but suffer from biases and uncertainties due to inherent model limitations and inability to model under-resolved processes. These limitations motivate the use of inverse or machine learning methods to systematically constrain models with data from real world observations or high-resolution model runs, thereby reducing both structural and parametric uncertainties. Gradient-based approaches offer a way to \u201clearn\u201d high-dimensional model input spaces, such as physics-based parameters or neural network weights. Combining these methods leads to the notion of neural GCMs that are fully differentiable through automatic differentiation (AD). We have implemented such a capability in the open source ocean GCM Oceananigans.jl, resulting in DJ4Oceananigans (Differentiable Julia for Oceananigans). This required the enhancement of the AD tool Enzyme.jl in conjunction with the compiler tool Reactant.j, which produces a stable multi-level intermediate representation (MLIR) that renders robust, optimized derivative code executable on a wide range of devices including CPUs, TPUs, and GPUs. We demonstrate the computation of accurate gradients across several Oceananigans configurations, for use in sensitivity tests, parameter estimation, and embedded neural network layers. This work represents a significant milestone toward integrating gradient-based inverse methods and machine learning in ocean modeling in Julia, providing open source tools to improve model calibration, reduce persistent model biases, and characterize model uncertainty. It is useful and accessible for other researchers interested in ocean and climate modeling, as well as those interested in an example of differentiability being successfully applied and utilized within a complex scientific model implemented in Julia.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UQDAQ9", "name": "Joseph Kump", "avatar": "https://pretalx.com/media/avatars/PQKKE3_rmkJchd.webp", "biography": "I am a PhD student in the Oden Institute at the University of Texas at Austin. My research is in the design and optimization of linear solvers, and the use of automatic differentiation to enable data driven methods like parameter estimation and scientific machine learning, with an emphasis on applications in ocean and climate modeling.", "public_name": "Joseph Kump", "guid": "4a5e75f8-23dd-5565-ad0e-05e83c721633", "url": "https://pretalx.com/juliacon-2026/speaker/UQDAQ9/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3RDRVU/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3RDRVU/", "attachments": []}, {"guid": "2a911dc7-621a-58c1-8f44-2db0f31a3535", "code": "A79CZS", "id": 92656, "logo": "https://pretalx.com/media/juliacon-2026/submissions/A79CZS/image_5DfY2eM.webp", "date": "2026-08-14T12:00:00+02:00", "start": "12:00", "end": "2026-08-14T12:15:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92656-glissade-jl-differentiable-simulator-for-geophysical-surface-flows", "url": "https://pretalx.com/juliacon-2026/talk/A79CZS/", "title": "GlissADe.jl: Differentiable Simulator for Geophysical Surface Flows", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Short talk", "language": "en", "abstract": "Geophysical surface flow phenomena such as avalanches, landslides, and floods pose significant risks to infrastructure and human safety. Recently, surface flow simulators are used to predict flow dynamics, inundation zones and develop hazard maps, allowing for effective disaster management strategies and safer engineering designs. While existing simulators (e.g. Openfoam-Avalanche, Avaframe, and r.Avaflow) are robust; they lack the differentiability required to efficiently perform uncertainty quantification tasks like sensitivity analysis, parameter calibration, etc., or discover new constitutive relations from observed data. \n\nWe introduce GlissADe.jl, developed for differentiable Finite-Area-Method (FAM) simulations, which enables the integration of physical simulations into gradient-based workflows and scientific machine learning. It is built upon the mathematical framework of surface-aligned depth-integrated shallow water equations [1]. By leveraging Julia\u2019s automatic differentiation (AD) ecosystem, including ForwardDiff.jl and Enzyme.jl, GlissADe.jl enables the direct computation of gradients across all model inputs. This allows for sensitivity analysis with respect to geometry, initial conditions (e.g., release height), and physical process parameters (e.g., friction coefficients and bulk density).\n\nThis talk explores the software architecture of GlissADe.jl, addressing the challenges of maintaining numerical stability while ensuring compatibility with operations that typically pose difficulties for AD, but are essential for geophysical simulators. These operations include differentiating through iterative time-stepping schemes, handling non-smooth flux limiters, and managing in-place memory mutations. We demonstrate how this framework is used for topographic uncertainty quantification. Finally, we discuss the potential application of such differentiable simulators to solve inverse problems in geophysical flow.\n\n\n[1] [M. Rauter, \u017d. Tukovi\u0107, A finite area scheme for shallow granular flows on three-dimensional surfaces,\nComputers & Fluids, Volume 166, 2018, Pages 184-199, ISSN 0045-7930,](https://doi.org/10.1016/j.compfluid.2018.02.017).", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "GVQQVT", "name": "Tanish Jain", "avatar": "https://pretalx.com/media/avatars/XYBDU9_10uME1a.webp", "biography": "Tanish Jain is an amateur researcher and an alumnus from IIT Kharagpur. During his time at IIT, he developed a keen interest in the Finite-Area Method (FAM) and its applications in modeling avalanches and landslides. His current work leverages the Julia SciML ecosystem to explore topographic uncertainty and parameter calibration, aiming to provide researchers with more robust tools for hazard mitigation in mountainous regions. His open-source contributions and current work on GlissADe.jl can be found on his GitHub profile at [@reckylurker](https://github.com/reckylurker).", "public_name": "Tanish Jain", "guid": "be47725f-62be-5c87-b5db-d635ebeb1a9e", "url": "https://pretalx.com/juliacon-2026/speaker/GVQQVT/"}, {"code": "HE8TTK", "name": "Alan Correa", "avatar": "https://pretalx.com/media/avatars/HE8TTK_NeZKgwg.webp", "biography": "I am an enthusiastic doctoral researcher working at the intersection of computational modeling, uncertainty quantification, and sustainable computing. My work focuses on developing robust and sustainable methods for high-dimensional uncertainty propagation and heterogeneous computational workflows in model-based engineering applications. I am also a passionate research software engineer who advocates for open science and actively contributes to collaborative open-source projects. My goal is to create innovative solutions that empower better decision-making in complex systems while ensuring our methodologies prioritize sustainability through responsible resource usage.", "public_name": "Alan Correa", "guid": "991a99e7-2208-5ce9-b920-e6e65f07c705", "url": "https://pretalx.com/juliacon-2026/speaker/HE8TTK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/A79CZS/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/A79CZS/", "attachments": []}, {"guid": "f51760fc-3366-57d9-8678-874d3e06146f", "code": "97YU9L", "id": 93457, "logo": "https://pretalx.com/media/juliacon-2026/submissions/97YU9L/image_uSGLMC8.webp", "date": "2026-08-14T12:15:00+02:00", "start": "12:15", "end": "2026-08-14T12:30:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-93457-memlsretrieval-jl-fast-snow-and-sea-ice-microwave-emission-modeling-for-inversion", "url": "https://pretalx.com/juliacon-2026/talk/97YU9L/", "title": "MemlsRetrieval.jl: Fast Snow and Sea-Ice Microwave Emission Modeling for Inversion", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Short talk", "language": "en", "abstract": "MemlsRetrieval.jl is a Julia reimplementation of the Microwave Emission Model of Layered Snowpacks (MEMLS). It leverages Julia\u2019s type system and generic input types to evaluate typical forward simulations in microseconds with zero allocations while remaining fully differentiable. MemlsRetrieval.jl has been successfully combined with other models in an optimal estimation framework for the retrieval of geophysical parameters from satellite observations.", "description": "The Microwave Emission Model of Layered Snowpacks (MEMLS) is a widely used forward model for microwave emission from snow and sea ice, here we present a reimplementation in Julia as MemlsRetrieval.jl. While the original MATLAB package has been valuable for exploring the parameter space and comparisons to satellite observations, it was not designed for efficient inversion or large-scale application like satellite retrievals. Our Julia implementation shifts the focus toward performance, composability, and retrieval workflows.\n\nBy leveraging Julia\u2019s type system together with packages such as StaticArrays.jl and ForwardDiff.jl, MemlsRetrieval.jl supports fast forward-model evaluation and automatic differentiation for Jacobian computation. In typical use cases of up to 10 layers, both forward evaluations and Jacobian calculations run in the microsecond range with zero allocations on consumer hardware. This enables efficient parallel execution with small memory footprints which is especially useful for retrieval and data-assimilation applications.\n\nAs a first application, we replaced a simplified sea-ice surface-emission parameterization in an ocean\u2013sea-ice\u2013atmosphere microwave emission model with MemlsRetrieval.jl. We then used this model within an optimal-estimation framework to retrieve multiple geophysical parameters, simultaneously from Advanced Microwave Scanning Radiometer 2 (AMSR2) satellite observations over the Arctic. The inclusion of our physical forward model allows us to exploit sensitivities that are often neglected because suitable empirical parameterizations are unavailable in practical retrieval systems. At the presentation, we will present the package design, performance characteristics, and first retrieval results, and discuss how the model can support future cryospheric remote-sensing applications in Julia.", "recording_license": "", "do_not_record": false, "persons": [{"code": "SMZW9B", "name": "Marcus Huntemann", "avatar": "https://pretalx.com/media/avatars/FAYGPV_u4vAz6n.webp", "biography": "Marcus Huntemann is a researcher at the University of Bremen in the Institute of Environmental Physics. He works with Julia since since 2015 in various applications mainly involving geophysical modeling, image processing and Geophysical retrievals from satellite observations.", "public_name": "Marcus Huntemann", "guid": "8fbf6958-d1cc-5b3b-8ac0-088770d19624", "url": "https://pretalx.com/juliacon-2026/speaker/SMZW9B/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/97YU9L/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/97YU9L/", "attachments": []}, {"guid": "271f9f9e-2797-5a40-af80-d335ae38a33b", "code": "KLDKCP", "id": 104397, "logo": "https://pretalx.com/media/juliacon-2026/submissions/KLDKCP/image_q1TZdBm.webp", "date": "2026-08-14T12:30:00+02:00", "start": "12:30", "end": "2026-08-14T13:00:00+02:00", "duration": "00:30", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-104397-differentiable-modeling-bof-discussion-and-future-directions", "url": "https://pretalx.com/juliacon-2026/talk/KLDKCP/", "title": "Differentiable Modeling BoF: Discussion and Future Directions", "subtitle": "", "track": "Differentiable Computational Models and their Applications", "type": "Birds of Feather (BoF)", "language": "en", "abstract": "This BoF is a roundtable discussion on differentiable computational models and their applications, open to anyone using or developing them in Julia or other languages. We'll discuss:\n\nExperiences, challenges, and solutions from working with differentiable models\nWhether existing differentiation packages provide straightforward explanations of their uses and how to implement them in real work\nLimitations in current differentiation packages that future development could address\nThe potential for an org page to consolidate and showcase community efforts and best practices in this space", "description": "This BoF is a roundtable discussion on differentiable computational models and their applications, open to anyone using or developing them in Julia or other languages. We'll discuss:\n\nExperiences, challenges, and solutions from working with differentiable models\nWhether existing differentiation packages provide straightforward explanations of their uses and how to implement them in real work\nLimitations in current differentiation packages that future development could address\nThe potential for an org page to consolidate and showcase community efforts and best practices in this space", "recording_license": "", "do_not_record": false, "persons": [{"code": "FSAGLX", "name": "Sarah Williamson", "avatar": "https://pretalx.com/media/avatars/FSAGLX_OPLqip8.webp", "biography": "I'm a PhD candidate at the University of Texas at Austin. My research lives in the realm of computational oceanography where I broadly work on utilizing differentiable ocean models for training subgrid-scale parameterizations.", "public_name": "Sarah Williamson", "guid": "c229f315-3b5c-5fe0-9f36-e177770cc528", "url": "https://pretalx.com/juliacon-2026/speaker/FSAGLX/"}, {"code": "HE8TTK", "name": "Alan Correa", "avatar": "https://pretalx.com/media/avatars/HE8TTK_NeZKgwg.webp", "biography": "I am an enthusiastic doctoral researcher working at the intersection of computational modeling, uncertainty quantification, and sustainable computing. My work focuses on developing robust and sustainable methods for high-dimensional uncertainty propagation and heterogeneous computational workflows in model-based engineering applications. I am also a passionate research software engineer who advocates for open science and actively contributes to collaborative open-source projects. My goal is to create innovative solutions that empower better decision-making in complex systems while ensuring our methodologies prioritize sustainability through responsible resource usage.", "public_name": "Alan Correa", "guid": "991a99e7-2208-5ce9-b920-e6e65f07c705", "url": "https://pretalx.com/juliacon-2026/speaker/HE8TTK/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/KLDKCP/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/KLDKCP/", "attachments": []}, {"guid": "598d2bbc-7dfc-51ad-9a78-d000e7bfa520", "code": "3Z7LGC", "id": 88586, "logo": "https://pretalx.com/media/juliacon-2026/submissions/3Z7LGC/image_BtrTSAL.webp", "date": "2026-08-14T14:30:00+02:00", "start": "14:30", "end": "2026-08-14T15:30:00+02:00", "duration": "01:00", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-88586-makie-jl-bof", "url": "https://pretalx.com/juliacon-2026/talk/3Z7LGC/", "title": "Makie.jl BoF", "subtitle": "", "track": "General", "type": "Birds of Feather (BoF)", "language": "en", "abstract": "An hour for users of Makie.jl to gather, show off cool plots, and talk about the state of the Makie ecosystem!", "description": "Will be pretty free-form - starting out with a 10-minute recap of what's happened in Makie world since last year, and then going to free-form discussion.  I'll also ask around if people would like to show off their own packages or plots.\n\nWe will also have updates from Makie devs on:\n- Ray tracing backend, fully native in Julia\n- Geospatial plotting on the 3D globe\nand more!  This will keep changing as we get more features in :)", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RQHPG", "name": "Anshul Singhvi", "avatar": "https://pretalx.com/media/avatars/7RQHPG_OxNT4Gf.webp", "biography": "Product engineer for Dyad, the new modeling and simulation language from JuliaHub.  Also heavily involved in geospatial (via JuliaGeo and GeometryOps.jl) and Makie.jl, as well as the Documenter.jl ecosystem.", "public_name": "Anshul Singhvi", "guid": "5b6d2c3a-d127-5c27-8672-a0d779e40d95", "url": "https://pretalx.com/juliacon-2026/speaker/7RQHPG/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/3Z7LGC/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/3Z7LGC/", "attachments": [{"title": "flyer-3Z7LGC", "url": "/media/juliacon-2026/submissions/3Z7LGC/resources/3Z7LGC_hweGZfd.png", "type": "related"}]}, {"guid": "c0e59bd8-a91c-5538-a20f-7a1fe49cda75", "code": "QS37VY", "id": 92572, "logo": "https://pretalx.com/media/juliacon-2026/submissions/QS37VY/image_D1QQDOM.webp", "date": "2026-08-14T15:45:00+02:00", "start": "15:45", "end": "2026-08-14T16:45:00+02:00", "duration": "01:00", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-92572-creating-tooling-as-greedy-as-julia-itself", "url": "https://pretalx.com/juliacon-2026/talk/QS37VY/", "title": "Creating Tooling as Greedy as Julia Itself", "subtitle": "", "track": "General", "type": "Birds of Feather (BoF)", "language": "en", "abstract": "Julia was created by greedy programmers who wanted it all. What if we are equally greedy about tooling? This session invites discussion on missing capabilities in Julia\u2019s development tools. What tools or workflows still fall short? Where should future efforts be made to improve productivity and insight?", "description": "Why they created Julia: [\"In short, because we are greedy.\"](https://julialang.org/blog/2012/02/why-we-created-julia/)\n\nThe goal of this bird of a feather is to explore concrete gaps and pain points across Julia\u2019s tooling such as debugging, performance analysis, compilation visibility, and workflows.\nTopics we hope to talk about include:\n\n- What workflows feel harder than they should?\n\n- Where do current tools fall short?\n\n- What functionalities would significantly improve productivity, confidence in correctness and performance?\n\n- Which tooling issues become most visible in larger or production systems?\n\n- Where do current debugging workflows fail?\n\nThe aim is to better understand community priorities and to connect contributors that are greedy in advancing Julia\u2019s development experience.", "recording_license": "", "do_not_record": false, "persons": [{"code": "GAHGKP", "name": "Tyrone Krieger", "avatar": "https://pretalx.com/media/avatars/PSVYRC_8FYvPC8.webp", "biography": "I've been writing code since I was 11. Nearly two decades later, I'm still baffled by the fact that most developers spend only 32% of their time actually coding.\nMy professors used to say this was just the way things were. But instead of accepting it, I decided to push back. One step at a time.\nWhy? Because we can.\nAs developers, we build the tools that move entire industries forward. So why not turn that same energy inward and improve our own?\n\nWhat I Love:\n\u2022\tDiving deep into complex codebases\n\u2022\tSharing developer knowledge\n\u2022\tBuilding powerful tools (like CodeGlass)\n\u2022\tExploring superconductors and the Meissner effect (hoverboards when?)\n\u2022\tI Like Trains\n\u2022\tLizard Doggo", "public_name": "Tyrone Krieger", "guid": "d21f4b7c-f71d-506b-9287-1228a0bd706d", "url": "https://pretalx.com/juliacon-2026/speaker/GAHGKP/"}, {"code": "ZAAWQQ", "name": "Yury Nuzhdin", "avatar": "https://pretalx.com/media/avatars/WEJRES_JZhIvzf.webp", "biography": "Software Architect in ASML working on Julia algorithms in the near real time system.\n[GitHub](https://github.com/tz-lom)", "public_name": "Yury Nuzhdin", "guid": "875f23e9-c926-5991-bb1a-569f3d231ef2", "url": "https://pretalx.com/juliacon-2026/speaker/ZAAWQQ/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/QS37VY/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/QS37VY/", "attachments": []}, {"guid": "82e3993b-372e-5255-bd17-a494c8b520cc", "code": "CUVPA3", "id": 93476, "logo": "https://pretalx.com/media/juliacon-2026/submissions/CUVPA3/image_gdKXqFU.webp", "date": "2026-08-14T16:45:00+02:00", "start": "16:45", "end": "2026-08-14T17:00:00+02:00", "duration": "00:15", "room": "Alte Mensa \u2014 Atrium Maximum", "slug": "juliacon-2026-93476-llms-agents-and-tools-for-julia-development", "url": "https://pretalx.com/juliacon-2026/talk/CUVPA3/", "title": "LLMs, agents and tools for Julia development", "subtitle": "", "track": "General", "type": "Short talk", "language": "en", "abstract": "The world of AI agents is changing constantly - every couple of weeks, it seems there is some new thing.  In this talk, I'll try to sum up the current state of the art when using AI agents with Julia - from which agents to use, MCP servers, skills and whatever else comes up between now and then.  If you have only dipped your toes into using AI with Julia so far, this talk is for you!", "description": "This talk is mainly to give an overview of what exists and what efforts are still ongoing.  It will mainly focus on the foundational strategies of how to use AI to develop Julia packages and work with Julia code.\n\nI'm_ mainly orienting this talk at folks who may not have used agents much yet, to provide a basic grounding in techniques that they can use going forward.  It probably will be a bit boring for folks who are really using AI agents at this point.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RQHPG", "name": "Anshul Singhvi", "avatar": "https://pretalx.com/media/avatars/7RQHPG_OxNT4Gf.webp", "biography": "Product engineer for Dyad, the new modeling and simulation language from JuliaHub.  Also heavily involved in geospatial (via JuliaGeo and GeometryOps.jl) and Makie.jl, as well as the Documenter.jl ecosystem.", "public_name": "Anshul Singhvi", "guid": "5b6d2c3a-d127-5c27-8672-a0d779e40d95", "url": "https://pretalx.com/juliacon-2026/speaker/7RQHPG/"}], "links": [], "feedback_url": "https://pretalx.com/juliacon-2026/talk/CUVPA3/feedback/", "origin_url": "https://pretalx.com/juliacon-2026/talk/CUVPA3/", "attachments": []}]}}, {"index": 6, "date": "2026-08-15", "day_start": "2026-08-15T04:00:00+02:00", "day_end": "2026-08-16T03:59:00+02:00", "rooms": {}}]}}}