I am a 15-year-old student, currently attending the City of London School. I have always had a deep interest in programming, first starting with Scratch when I was quite young, then moving onto Python, then finally, a few years ago, Julia. I have since presented at JuliaCon 2018, 2019 and 2020 and therefore have some experience with the language.

I got PhD in 2015 about physics, particle physics, in Osaka university in Japan. From June 2021, I am a faculty member of International Professional University of Technology in Osaka in Japan. September 2018 - May 2021, a postdoc in Riken Brookhaven research center in the USA. October 2015 - August 2018, a postdoc in central china normal university in China. I am interested in application of machine learning on lattice QCD and physics. I published a book "Deep Learning and Physics" from springer.

Computer Science undergraduate student at the University of Pisa. Interested in functional programming, programming language theory, category theory, algebraic and symbolic computation, computability theory.

I am a grad student working in computational climate science at MIT. In the Julia world I've mostly been developing Oceananigans.jl. We're building a new climate written in Julia as part of the Climate Modeling Alliance project.

I am pursuing a Ph.D. in Computational Science and Engineering at Georgia Institute of Technology. Currently, my research is mainly focused on applications of deep learning in inverse problems and uncertainty quantification.

Alissa is a Senior Research Analyst at the Federal Reserve Bank of New York. She has a B.A. from Grinnell College, double-major in Economics and Computer Science.

I am interested in the intersection of mathematics and physiology. I am a computer science student at the University of Chicago on a gap year. I am working at Julia Computing on building surrogates of systems biological models for JuliaSim.

Ander Gray received  an MSci in Physics from Queen’s University Belfast (2017). Since then, he has been a PhD student at the University of Liverpool and  Culham  Centre for Fusion Energy, studying Uncertainty Quantification. For his thesis work, Ander  researches  methods for efficiently propagating uncertainty in radiation transport simulations. He is also involved in developing methods and software for calibrating and propagating uncertainties through computational models, in the form of imprecise probabilities.

I am a PhD student and I've studied Mathematics. I am working with Julia since 2017. I'm a developer of a modeling and simulation environment of 3D-systems called Modia3D. Our Julia package is https://github.com/ModiaSim/Modia3D.jl.
I'm interested modeling and numerical analysis in general and I want to learn more about Julia.

Chemical engineering

Andrew Dolgert is a computational scientist at the University of Washington. He has been a high-performance computing consultant for many years, working on diverse projects such as parallelization of molecular dynamics, immersive visualization of fracture mechanics, provenance for the Large Hadron Collider, and time series analysis of the world's global health. His recent work is on continuous-time, discrete-event simulation and on testing of scientific code.

I first used Julia to perform research during postdocs in quantum simulation, before using it in industry with a team working on various geospatial applications. Nowadays we use Julia at ELARA AI to simulate, analyze and optimize real-world businesses. I have contributed packages including StaticArrays, CoordinateTransformations, Rotations, TypedTables, SplitApplyCombine, AcceleratedArrays and Dictionaries.

I am a doctoral candidate in Vanderbilt’s human genetics PhD training program.

Arsh Sharma is a student at the National Institute of Technology, Hamirpur, India. He is currently working as a GSoC student for the Julia Language Community building Javis.jl, a math viz tool.

Senior year CS undergraduate at BITS Pilani - Pilani campus India. As a JSOC '20 student I worked on Fairness.jl . I am interested in fairness in machine learning, causality, counterfactual fairness and reinforcement learning. Lately, I have been exploring Quantum AI and causal RL. Happy to chat at ashryaagr@gmail.com or julia slack :-)
To know more about me, visit www.ashrya.in

Avik Sengupta is the head of product development and software engineering at Julia Computing, contributor to open source Julia and maintainer of several Julia packages, including JavaCall, TextAnalysis and GameZero. Avik is the author of Julia High Performance, co-founder of two artificial intelligence start-ups in the financial services sector and creator of large complex trading systems for the world's leading investment banks.

Hi there! My name is Axel and I'm an intern at TNO, the Netherlands Organisation for Applied Scientific Research, working with Scientific Machine Learning for predicting structural responses. The internship is a part of my master thesis at University College London.

My fields of interest are computational structural engineering and computational design. Over the years, I've become more interested in more computing related topics like HPC, parallell processing and machine learning. This fall, I will move to the U.S. to start my PhD in Civil Engineering at Princeton University. I'm very interested in Julia for my future research and seeking collaborators in the Julia community.

Bas Peters is visiting assistant professor in the mathematics department at Emory University. Previously, Bas worked for Computational Geosciences Inc as a research scientist, and received his PhD degree from the University of British Columbia in 2019. His main research interests are constrained optimization; design, optimization, and regularization of deep neural networks, geoscientific and geospatial applications, inverse problems, reinforcement learning, image processing, and numerical linear algebra.

Benoît Legat is a postdoctoral associate at MIT with Prof. Pablo Parrilo
in the Laboratory for Information and Decision Systems (LIDS).
He received his Ph.D. degree in applied mathematics from the UCLouvain, Belgium, in 2020.
His research interests include mathematical optimization, invariant set computation and
optimal control.

Postdoc at the University of Southern California

I am a researcher in the fields of operations research and computational social science.
For development I use the Julia language. I currently mostly contribute to DataFrames.jl and related packages.

Brian Guenter is a Senior Principal Researcher at Microsoft Research, where he manages the Interactive Media Group.

Dr. Carleton Coffrin is a staff scientist at Los Alamos National Laboratory in the Advanced Network Science Initiative, an interdisciplinary team that investigates the application of emerging optimization and machine learning methods to problems in critical infrastructure systems. Dr. Coffrin’s work focuses on developing novel optimization methods for network design, operation, and restoration for power networks. His work on power system optimization has been recognized by the IEEE PES 2014 Optimal Power Flow Competition, the ARPA-e 2020 Grid Optimization Competition and Los Alamos National Laboratory's Early Career Researcher award. Dr. Coffrin is also exploring how novel computing devices, such as quantum computers and memristor networks, can improve the next generation of optimization algorithms. Dr. Coffrin received his Ph.D. in Computer Science from Brown University in 2012, under the supervision of Pascal Van Hentenryck.

Hi, I'm Chad. My interests range from applied problems through "technology transfer", to applied research. I've been involved in probabilistic programming for the last ten years, and have led design of a few prototype systems. Since 2015 I've been very interested in Julia, resulting in Soss.jl and MeasureTheory.jl, as well as some utility packages around these.

Most recently I've founded Informative Prior, where I'm available for contract consulting work involving teaching, development, or application of probabilistic modeling software.

Research scientist at Microsoft mixed reality and AI labs. Interested in computer graphics and vision.

Quantum computing | quantum machine learning

GitHub: ChenZhao44,
Twitter: @ChenZhao44

Statistician and SIMD-enthusiast.

Chris Hill is a principal researcher at MIT. He is a lead architect of the widely used MIT General Circulation Model (MITgcm). He has been working with the Julia community for almost a decade.

Chris ran the Bay Area Julia Users group from 2016 to 2020.

Chris Rackauckas is an Applied Mathematics Instructor at MIT and the Director of Scientific Research at Pumas-AI. He is the lead developer of the SciML open source scientific machine learning organization which develops widely used software for scientific modeling and inference. One such software is DifferentialEquations.jl for which its innovative solvers won an IEEE Outstanding Paper Award and the inaugural Julia Community Prize. Chris' work on high performance differential equation solving is seen in many applications from the MIT-CalTech CLiMA climate modeling initiative to the SIAM DSWeb award winning DynamicalSystems.jl toolbox. Chris is also the creator of Pumas, the foundational software of Pumas-AI for nonlinear mixed effects modeling in clinical pharmacology. These efforts on Pumas led to the International Society of Pharmacology's (ISoP) Mathematical and Computational Special Interest Group Award at the American Conference of Pharmacology (ACoP) 2019 for his work on improved clinical dosing via Koopman Expectations, along with the ACoP 2020 Quality Award for his work on GPU-accelerated nonlinear mixed effects modeling via generation of SPMD programs. For this work in pharmacology, Chris received the Emerging Scientist award from ISoP in 2020, the highest early career award in pharmacometrics.

Christian Schilling received his Ph.D. degree in computer science from the University of Freiburg, Germany, in 2018 under the supervision of Andreas Podelski. He was a postdoctoral research fellow at IST Austria in the group of Thomas A. Henzinger. Since 2020 he is the interim professor for cyber-physical system at the University of Konstanz, Germany. Christian's research in the area of formal methods is focused on the analysis, verification, and synthesis of systems with dynamical or machine-learned components. He is a co-lead developer in the JuliaReach ecosystem.

I'm a long time enthusiastic user of Julia and enjoy contributing to various packages across the open source ecosystem, Julia standard libraries and compiler. I love hearing about people's fascinating technical computing adventures of all types! Find me at https://github.com/c42f

Collaborator on YAML, HTSQL, DataKnots, and other projects that advance the usability of software systems.

I am an assistant professor at the Wisconsin Institute for Discovery and the Department of Plant Pathology at the University of Wisconsin-Madison. Originally from Mexico City, I did my Undergraduate degrees in Actuarial Sciences and Applied Mathematics at ITAM. Then, I did a MA in Mathematics and a PhD in Statistics at the University of Wisconsin-Madison.
In my spare time, I enjoy swimming, running, biking, climbing and yoga!
Pronouns: she/her

I am a professor for computational number theory at Kaiserslautern University. I a PI of the DFG (German research association) funded OSCAR project as well as a developer of both OSCAR and Hecke.

Clayton Barrows is a member of the Forecasting and Modeling Group at the National Renewable Energy Laboratory. His research focuses on improving the technical and economic efficiency of energy systems through advanced computation and analysis. At NREL, Clayton leads a team in developing and utilizing energy and infrastructure systems models to gain new insights into pathways towards system modernization. In his research, Clayton draws upon deep experience in applying the tools of network science and optimization to improve the fidelity and scalability of infrastructure systems models. He has applied these techniques to inform policy in studies and applications around the world.

Soon-to-be college student who loves everything related to computers and virtual reality

Developer of Ahorn and similar tools.

I have been working with Julia since 2015 and have made a variety of contributions to the ecosystem. I am a member of the Julia Community Prize committee and work for Beacon Biosignals as a Principal Software Architect.

Dan Padilha is a Masters (Aerospace Engineering) student at The University of Tokyo and JAXA Institute of Space & Astronautical Science (ISAS). He has a background in computer science and 6 years of professional experience as a software systems and research engineer in quantum computing (at Rigetti in London), machine learning (at QxBranch in Adelaide), and embedded systems (at NICTA in Sydney). He has been involved in two successful start-up companies, presented at high-performance computing and emerging technologies conferences, run software workshops at over a dozen multinational corporations and universities, and led technical engagements designing novel algorithms and analytics platforms. He is currently a member of the Tsuda Laboratory at ISAS, working on software tools for astrodynamics research.

David Anthoff is an environmental economist who studies climate change and environmental policy. He co-develops the integrated assessment model FUND that is used widely in academic research and in policy analysis. His research has appeared in Nature, Science, the American Economic Review and other academic journals. He contributed a background research paper to the Stern Review and has advised numerous organizations (including US EPA and the Canadian National Round Table on the Environment and the Economy) on the economics of climate change.

He is an assistant professor in the Energy and Resources Group at the University of California, Berkeley. Previously he was an assistant professor in the School of Natural Resources and the Environment of the University of Michigan, a postdoc at the University of California, Berkeley and a postdoc at the Economic and Social Research Institute in Ireland. He also was a visiting research fellow at the Smith School of Enterprise and the Environment, University of Oxford.

He holds a PhD (Dr. rer. pol.) in economics from the University of Hamburg (Germany) and the International Max Planck Research School on Earth System Modelling, a MSc in Environmental Change and Management from the University of Oxford (UK) and a M.Phil. in philosophy, logic and philosophy of science from Ludwig-Maximilians-Universität München (Munich, Germany).

David Fobes (Ph.D.) is a scientist at Los Alamos National Laboratory in the Information Systems and Modeling group. Prior to joining LANL in 2015, he obtained a PhD in physics from Tulane University. At LANL, he specializes in development and implementation of algorithms for critical infrastructure systems, focusing on optimization of unbalanced multi-phase electric distribution systems, and post-event system evaluation and restoration, and has served on multiple projects for DOE and DHS in these areas. He is the lead developer of PowerModelsDistribution, a critical library for distribution system optimization relied upon by several ongoing projects, and has led and participated in a variety of other software development efforts in the infrastructure modeling space, including serving as Scrum Master and developer for a team focused on modeling the inter-dependencies between electric and natural gas infrastructure systems using co-simulation. He is an author of over 43 peer reviewed journal and conference publications, is a certified Scrum Master, and has received multiple awards for his infrastructure optimization works.

Professor of Computational Science at the Universidad Nacional Autónoma de México and visiting professor at MIT.

Interested in computational science, interval arithmetic, and numeric-symbolic computing.

Author of the JuliaIntervals suite of packages for interval arithmetic, and various tutorials on Julia.

I am a PhD student at the Division of Systems and Control within the Department of Information Technology and the Centre for Interdisciplinary Mathematics in Uppsala, supervised by Fredrik Lindsten, Dave Zachariah, and Erik Sjöblom. The main focus of my PhD studies is uncertainty-aware deep learning. Currently, I am particularly interested in analyzing and evaluating calibration of probabilistic models. Please visit my webpage for more information.

My GitHub profile provides an overview of my contributions to the Julia ecosystem. Currently, I am a member of the steering council of SciML and the Turing team.

Dhairya Gandhi is a data scientist at Julia Computing Inc. and is the lead developer of the Machine Learning framework Flux.

I am a PhD candidate in the SPS group of Electrical Engineering department in Eindhoven University of Technology. My research interests lie in the fields of computer science, software developing, numerical modeling, machine learning, computational optimization and high-performant parallelized applications.

Group leader for Statistical Sciences at Los Alamos National Laboratory

I am a Ph.D. student in the Theoretical Systems Biology Group at the University of Melbourne led by Prof. Michael Stumpf. I focus my research on hybrid models, in which I combine mechanistic modelling techniques, e.g. differential equations, with machine learning approaches such as neural networks. I am passionate about using hybrid models in developmental biology to enhance our understanding of cell fate decision making.

In 2017, I received a Bachelor of Science in Bioinformatics from the Technical University of Munich and the Ludwig Maximilian University of Munich. In 2018, I graduated from Imperial College London with a Master of Science in Bioinformatics and Theoretical Systems Biology.

Engineer at BlackRock AI Labs

Postdoctoral researcher at the International Centre for Numerical Methods in Engineering

I recently finished my PhD in quantum information theory at the University of Cambridge. I’m now a Research Scientist working at Beacon Biosignals, trying to make brain monitoring easily accessible, interpretable, and actionable.

Fearghal O’Donncha is a research scientist at IBM Research — Ireland. His work focuses on applying simulation models, analytics, and machine learning techniques to assist industry operations. This encompasses developing and deploying simulation-based models, integrating sensor data from a variety of IoT platforms, developing AI-based models that extract value from sensor or expert data, and optimizing these tools to a variety of HPC and cloud-based platforms. He is an adjunct faculty member at the National University of Ireland, Galway.

Felix J. Herrmann graduated from Delft University of Technology in 1992 and received his Ph.D. in engineering physics from that same institution in 1997. After research positions at Stanford University and the Massachusetts Institute of Technology, he became back in 2002 faculty at the University of British Columbia. In 2017, he joined the Georgia Institute of technology where he is now a Georgia research Alliance Scholar Chair in Energy, cross-appointed between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering. His cross-disciplinary research program spans several areas of computational imaging including seismic, and more recently, medical imaging. Dr. Herrmann is widely known for tackling challenging problems in the imaging sciences by adapting techniques from randomized linear algebra, PDE-constrained and convex optimization, high-performance computing, machine learning, and uncertainty quantification. Over his career, he has been responsible for several cost-saving innovations in industrial time-lapse seismic data acquisition and wave-equation based imaging. In 2019, he toured the world presenting the SEG Distinguished Lecture "Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition". In 2020, he was the recipient of the SEG Reginald Fessenden Award for his contributions to seismic data acquisition with compressive sensing. At Georgia Tech, he leads the Seismic Laboratory for Imaging and modeling and he is co-founder/director of the Center for Machine Learning for Seismic (ML4Seismic), designed to foster industrial research partnerships to drive innovations in artificial-intelligence assisted seismic imaging, interpretation, analysis, and time-lapse monitoring.

Felix Wechsler studied Physics and Informatics at the Technical University of Munich in Germany. For his master studies in Photonics he moved to Jena (city of light). Currently he finishes his master thesis in the field of computational microscopy at the Biomedical Imaging Group of Leibniz Institute of Photonic Technology under the joint supervision of Rainer Heintzmann and Ivo Ihrke.

Computers are too difficult!

https://github.com/fonsp

Lyndon White (@oxinabox) is a research software engineer at Invenia Labs (Cambridge, UK). He helps researchers use machine learning, constrained optimization, and generally tools from the technical computing domain to optimize the power grid. He get to do all the best parts of being a software developer and all the best parts of being a researcher, its great.
He works a lot on the Julia AutoDIff code, and is the leader of the ChainRules project.

I am in the second year of the Bachelor Degree in Physics at the University of Turin. I am mainly interested in the theoretical and mathematical aspects of physics. I also conduct research in machine learning, with a particular interest towards the connection between machine learning and physics. Recently I have worked in the field of Scientific Machine Learning using Julia libraries, such as NeuralPDE.jl.

I am a PhD student enrolled at the University of Leipzig working with the Remote Sensing Center for Earth System Research and funded by the Center for Scalable Data Analytics and Artificial Intelligence. I am interested in data-driven exploration of extreme events and their consequences. At the moment my focus is mainly in Reservoir Computing approaches.

Francesc Verdugo PhD is assistant research professor at CIMNE (Barcelona, Spain) and he is interested in the research of new discretization methods to solve partial differential equations (PDEs). He is co-founder of the Gridap.jl project to solve PDEs in Julia.

Professor in Applied Mathematics at the University of Waterloo

François Pacaud is a postdoctoral appointee at Argonne National Lab, supervised by Mihai Anitescu. His work focuses on the development of new nonlinear optimization algorithms on GPU architectures.

Prof of marine hydrodynamics & biologically inspired engineering at University of Southampton, physics-based machine learning at Alan Turing Institute. https://weymouth.github.io/

Postdoc at Utrecht University

I work as a reseach scientist at the Massachusetts Institute of Technology (MIT) where I investigate oceanography and climate. As part of the Department of Earth, Atmospheric and Planetary Sciences, my work focuses on ocean modeling and the analysis of global ocean data sets such as Argo profile collections, satellite records of sea level, or ocean color retrievals. I co-develop computer programs in various languages and carry out ocean state estimation using the MIT general circulation model in order to interpolate and interpret ocean observations. My scientific interests include: ocean circulation and climate variability; tracer transport and turbulent transformation processes; interaction of ecological, geochemical, and physical processes; global cycles of heat, water, and carbon; observational statistics; forward and inverse modeling.

Postdoc in climate physics in the MPI for Meteorology (Hamburg) and professional drummer. Lead dev for JuliaDynamics and JuliaMusic.

PhD student in Physics at University of Buenos Aires.

Glen Hertz has spent 15 years as a field applications engineer supporting customers of commercial SPICE simulators. He joined Julia Computing as the Principal EDA Solutions Architect in 2021 to direct the JuliaSPICE product.

I'm a Research Software Engineer at InveniaLabs, UK.
Interested in the application of Julia for scalable, sustainable, research.

Goran Frehse has a Diploma in Electrical Engineering and Information Technology from Karlsruhe Institute of Technology, Germany, and a PhD in Computer Science from Radboud University Nijmegen, the Netherlands. From 2006 to 2018, he was an associate professor at the University Grenoble Alpes, from which he obtained a habilitation in 2016. From 2016 to 2018, he held a research chair (Chaire Initiative Universitaire Alpes) at the Univ. Grenoble Alpes. Since 2018, he is a professor at ENSTA Paris, where he continues his research on safe cyber-physical systems.

Gregor Kappler carries out psychometric research and data science consulting, and is founder of FilingForest, a julia-focused startup developing solutions for fast unbiased measurement in graph data.

Gregor was initially trained as a mathematician and psychologist, has implemented solutions for semantic text analytics for his PhD in 2007, and developed psychometric models for measuring with texts.
He has worked as a lecturer and researcher at the University of Vienna and the University of Jena and worked on a series of predictive analytic projects for software vendors and customers.

Gregor has switched to Julia from R in 2018, and is creator of the CombinedParsers package which provides parser combinators for fast, recursive and type-save parsing in pure Julia.

Research scientist and ocean physicist at MIT

Russian/Hungarian biophysics and machine learning PhD student at King's College London, collaborating with Microsoft Research Cambridge. Born in Vienna. Wanted to become an actor but luckily landed in research

I am a late-stage Ph.D. student in Robotics at MIT CSAIL, and I first used "Julia" in 2012!

I am a mechanical engineer working on ground systems software in the European space industry.

Working on reimagining space exploration with Open Source at JuliaAstro, JuliaSpace, and OpenAstrodynamics.

I am a Postdoctoral Fellow in the Cluster of Excellence at the University of Münster, Germany. My research is focused on the analysis and development of numerical methods for partial and ordinary differential equations. In particular, I am interested in the stability of these schemes as well as mimetic and structure-preserving techniques, allowing the transfer of results from the continuous level to the discrete one.

PhD from Department of Automatic Control, Lund Institute of Technology in 1978 about a new modeling language. Started Dynasim 1992, sold to Dassault Systèmes in 2006. Chief architect for modeling software Dymola. Initiated design of Modelica modeling language in 1996.

Previous experience include: Mogram AB (CEO), Dassault Systèmes (CTO Systems), Dynasim AB (CEO).

Working with Julia since 2015 and with Modia since 2016.

Ian Slagle is an junior undergraduate student studying Computer Science, Mathematics, and Physics at Coe College in Cedar Rapids, IA who utilizes Julia in his research. He currently plans to attend graduate school for Data Science.

Been a Julia enthusiast for a long time, since Julia 0.1! Always been interested in data engineering, making data processing more efficient, and various data formats, and Julia is just such a fun tool to dive into these kinds of problems.

My Name Is Jacob S. Zelko – Pleasure To Meet You!

I am a graduate of Georgia Institute of Technology with a BS in biomedical engineering. While pursuing my BS, I worked as a data information specialist and biomedical informaticist in the Department of Biomedical Informatics at Emory University doing research in the areas of computational psychiatry and worked with The Center for Discovery as an engineering consultant to aid in caring for their juvenile neurocognitively diverse population. Currently, I work as a Health Data Analytics and Informatics Researcher at Georgia Tech Research Institute and as a Consultant with the Centers for Disease Control.

I work for Julia Computing on Julia compiler technology.

Jean-François Baffier is an academic researcher at the RIKEN Center for Advanced Intelligence Project (AIP), and a consultant in Artificial Intelligence, Big Data Science, Data Structures, and Algorithms. As an academic, he gives back to society through fundamental research in computer science supplemented by open source libraries and softwares.
His current research project involves the study of the “Analysis of information networks,” the “Smart compression for high-scalability of data structures,” and “Explainable Artificial Intelligence.” Other topic of interest covers modeling of failures and routing in Networks, Game Analysis, and AI for Games.

I'm a dynamics and controls engineer who recently completed his MS in aerospace engineering at the University of Maryland. My research largely focused in space robotics and astrodynamics. I'm the package developer for several open source astrodynamics package, including the superpackage GeneralAstrodynamics.jl.

I am a PhD candidate in economics at the European University Institute in Florence, a guest researcher at the University of Oslo and a visiting scholar at the University of Minnesota and the Federal Reserve Bank of Minneapolis.

My research interests are macroeconomics, fiscal policy and computational methods. I also work on household finance and machine learning.

https://www.jofleck.com // https://twitter.com/_jofleck

PhD Candidate in Computer Science at Cornell University

MRI physics PhD student at the University of British Columbia. I study brain tissue microstructure by simulating MRI signals and using Bayesian learning for fast parameter inference.

Jonathan Laurent is a PhD student in Computer Science at Carnegie Mellon University. His current research interests lie at the intersection of machine learning and automated theorem proving.

Jorge Pérez Zerpa obtained a Doctorate in Engineering degree by Universidad de la República in Uruguay, a MSc in Mechanical Engineering degree by Universidade Federal de Rio de Janeiro in Brazil, and did a stage at INRIA's TAO team in France. He is Assistant Professor at the Structures Department of the School of Engineering in Universidad de la República, and Researcher level 1 at ANII.uy . His research work includes the development and application of numerical methods in computational modelling of solids and structures, with focus in constitutive parameter identification methods. He leads the development of the Open Nonlinear Structural Analysis Solver (onsas.org) based in the application of the Finite Element Method.

I'm a senior software engineer with GN Advanced Science and have been working with Julia since 2014. My Julia work is focused on platform development for rapid prototyping of audio signal processing algorithm personalization and cloud-based deployment. I'm passionate about making software development accessible from a technical perspective through abstraction and automation.

I am an HPC software engineer working at the JuliaLab. I maintain Dagger.jl, AMDGPU.jl, and BPFnative.jl, and generally enjoy the challenge of hacking on compilers and HPC runtimes.

Ph.D. candidate at the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) of Université Catholique de Louvain (UCL), Louvain-la-Neuve, Belgium.

Dr. Júlio Hoffimann is a research scientist with more than 10 years of experience in advanced statistical theories for geosciences. He is the author and lead developer of the GeoStats.jl framework, as well as various other open source projects that are widely used by geoscientists around the world: https://juliohm.github.io

I'm a PhD student researching speech processing at the Institute for Systemic Neuroscience in Hamburg, Germany. I'm a core contributor to Makie.jl and the author of packages such as MakieLayout.jl, Chain.jl, Animations.jl.

I am a longtime Julia contributor with a special interest in GPU programming and HPC. I currently work at Amazon Braket as a scientist.

Kevin Squire is a Senior Software Engineer at Second Spectrum, Inc., where he works in the AI group on machine learning algorithms and infrastructure. Previously, he completed a postdoc in Computational Genetics at UCLA and taught Computer Science at the Naval Postgraduate School. He received his PhD in Electrical Engineering from the University of Illinois. Kevin has been interested in Julia since its first public release and was an early contributor to the language.

PhD student in the University of Évora

I am a PhD student in Computational Mechanics at Chalmers University of Technology (Sweden), where I use Julia for doing Finite Element simulations.

Senior Machine Learning Engineer at Beacon Biosignals, working with julia since 1.0.

I'm a contributor to the Julia language and many packages.

Working on a sparse-AI actor runtime, researching decentralized algorithms, playing with runtime code generation.

A Biostatistics PhD student from the University of Oslo. I'm working on combining computer simulations with Bayesian statistical models. I have been a big fan of Julia since version 0.4. My research interests are Bayesian statistics, symbolic computing and applied category theory.

The author of PyYAML, LibYAML, HTSQL, and DataKnots.

Mathematics undergraduate student and Economics graduate. I love playing computational statistics using Julia.

Research Associate and PhD candidate at TU Berlin, open-science and macro-energy system modeling, https://github.com/leonardgoeke

Lingling Fan is a full professor at University of South Florida, working on the following research areas: control, dynamics, optimization, and system identification of power systems, power electronics and electric machines. Her recent research interests include mechanism analysis of various dynamics seen in real world (wind farm oscillations, solar PV tripping), computing, and programming with Julia.

Dr Tang is a research scientist in the CCS-7 Programming Models team at Los Alamos National Laboratory. His research interests include programming model, co-design, performance/energy analysis and modeling. Dr Tang received his Ph.D. from the Department of Computer Science and Engineering at University of Notre Dame in 2017.

Logan is the Community Manager for the Julia Language. Get in contact with him here: https://twitter.com/OfficialLoganK

I am an undergraduate physics student at the University of Turin. I have a strong interest in many aspects of physics, ranging from theoretical physics to HEP and cosmology. Currently I am doing my undergraduate thesis about the study of the properties of the hypertriton nuclei with ALICE's data using machine learning techniques. Meanwhile, I am one of the co-founder of MLJC, a student association that focuses on ML research and know-how sharing. My interest are mainly on scientific ML, NLP & NLU, and the theoretical aspects of ML. One of my strongest motivation is to help accelerating pure sciences using ML approaches. I participated in the ProjectX 2020 competition held by the University of Toronto in the UniTo team.

PhD student in computer science at the University of Vaasa. Research interests in compuational methods for efficient and reliable positioning.

Geo-HPC, GPUs, supercomputing & Julia @ VAW ETHZ.

Malcolm is a simulation engineer at Deloitte Digital Australia with a background in real-time simulation in the automotive and transport domains. Currently he is developing real-time simulations of large transport networks as part of the Optimal Reality team, and enjoying contributing to Julia.

Manuela Vanegas Ferro is a PhD candidate in Biological Design at Arizona State University. She earned a Bachelor of Science degree in Biology and Microbiology and a Master of Science degree in Computational Biology from Universidad de los Andes in Colombia. Manuela has experience in modeling complex biological systems at different scales. Currently, she is developing an individual-based model that integrates biological features of coffee rust disease and socio-economic aspects of coffee farmers' management practices to find optimal long-term farming strategies.

PhD student at Paderborn University. My main research interest lies in multiobjective (non-linear) optimization.

Marcelo Forets is an Applied Mathematician that works as Assistant Professor at Universidad de la República (Uruguay). Born in Uruguay (Montevideo, 1988), he graduated in Physics and in Electrical Engineering, then moved to France for a PhD in Mathematics and Informatics (Univ. Joseph Fourier, France) on the quantum random walk, a model of particular interest to Quantum Computing. He was a post-doc researcher at VERIMAG laboratory of Université Grenoble Alpes (France) under the supervision of Oded Maler and Goran Frehse, where he started to develop what is now the JuliaReach package ecosystem. His research has to do with developing innovative numerical tools that impact decisions regarding reliability, correctness and safety of control systems, hybrid dynamical systems, and robustness analysis of neural networks.

I'm a mathematician, researcher in geometric group theory at KIT (Karlsruhe, Germany); I received my PhD in pure mathematics in 2014 and since then changed my scientific focus to aspects including more computational problems. I've been coding in julia since 2016, mostly around group theory, mathematical optimization and certified computation.

Senior Lecturer (associate professor) with the University of Cambridge Department of Computer Science.

Ph.D. student in Electrical Engineering @KU Leuven and @Energyville, Belgium.

Mary is a Senior Research Software Engineer at Brown University’s Center for Computation and Visualization, and a consulting Senior Software Engineer at RelationalAI. She works with researchers to provide scientific and technical computing expertise, combined with best practices, to advance computational research. She received her B.S. in Engineering Science from Smith College and her M.S. in Computer Science from Brown University.

Post Docotoral Fellow at Georgia Institute of technology.
My main research focuses on high-performance computing for large-scale PDE constraints optimization (medical imaging, seismic imaging) on standard clusters and in the Cloud. In particular I work intensively on open source solutions in Julia and Python and high-level abstractions for high-performance computing such as Devito (Finite difference DSL) or JUDI.jl (linear algebra abstraction for PDE constraint optimization).
My secondary research project is aimed at computational and algorithmic solutions for large-scale machine learning.

Mathieu is a researcher in computational mathematics working at the Zuse Institute Berlin. His interests span mixed-integer, convex optimization, applications in engineering and statistics.

Matthew Fishman received a PhD in physics from Caltech in the spring of 2018. His thesis was on the development of new tensor network algorithms for studying quantum many-body systems. In the fall of 2019, he started as an Associate Data Scientist at the Center for Computational Quantum Physics, part of the Flatiron Institute in New York City. There, he is lead developer of the C++ and Julia versions of the ITensor library, a leading software package for performing tensor network calculations.

MIT Chemical Engineering Ph.D. Student

Matthew E. Wilhelm received a B.S. in Applied Mathematics from the University of North Carolina at Greensboro, Greensboro, NC, USA (2009), and a M.S. in Chemical Engineering from Columbia University, New York, NY, USA (2011). He is currently a PhD Candidate in Chemical and Biomolecular Engineering at the University of Connecticut where his research interests include: nonconvex optimization, dynamic simulation and optimization, mathematical biology, and engineering & STEM pedagogy.

Since April 2020 I am full professor for Algorithmic Algebra and Geometry at TU Kaiserslautern, Germany. My research focuses on computer algebra, group theory and algebraic Lie theory.

Besides being one of the principal investigators on the grant behind OSCAR, and one of its developers, I am also core developer of the GAP computer algebra system for computational group theory.

Outside of mathematics, I've been involved in many open source projects over the past decades; e.g. in the distant past, I served as project leader for ScummVM as well as the Fink project, and contributed to TeXShop.

I am an AI Researcher at Ericsson Research. My research interests lie in reinforcement learning, planning under uncertainty, and AI safety. Prior to joining Ericsson I completed my PhD at Stanford University under the supervision of Prof. Mykel Kochenderfer. My thesis was about safe and scalable planning under uncertainty for autonomous driving and all the code was written in Julia. At Juliacon 2021 I will present work I did during my thesis.

Mia is a graduate student at the University of British Columbia, working with Michael Friedlander. She's been interested in computers and programming since being in elementary school, and after finishing her undergraduate in astrophysics at UBC she is very excited to continue learning in (and hopefully contributing to!) the field.

By training a chemist, who got more and more twisted towards the "dark side" of numerical analysis. Now working as a PostDoc at the Applied and Computational Mathematics lab of RWTH Aachen University, Germany. Lead developer of DFTK, a Julia code for mathematical research in on density-functional theory (a quantum chemistry method).

I am a postdoctoral fellow and research software engineer at the Numerical Simulation Research Group of the University of Cologne, Germany. My research focus is on numerical methods for adaptive multi-physics simulations, high-order schemes, and high-performance computing.

I am a postdoctoral researcher at Ghent University. My interest is in using computational intelligence to understand and design biological systems.

Physics Undergraduate based in Mexico City, UNAM. I enjoy handstands and coding in Julia.

Miha is a theoretical physicist by training and holds an MSci degree from Imperial College London. After completing his Masters, he moved to the countryside to pursue a PhD at the University of Oxford where he analysed the data from the Large Hadron Collider at CERN searching for a rare decay of the Higgs boson. He now works as a Research Software Engineer at Invenia Labs, where he builds tools that accelerate research in electricity grid efficiency.

I am a physicist studying exotic matter formation via resonance phenomena of the QCD.

Field: elementary particles, hadron spectroscopy

Organizations: CERN (LHCb, COMPASS), JPAC, ORIGINS Cluster

PhD student in Climate Computing
Atmospheric, Oceanic and Planetary Physics
University of Oxford

milan.kloewer@physics.ox.ac.uk
www.milank.de
twitter @milankloewer
github @milankl

A topology optimization researcher, a co-maintainer of Turing.jl, a scientist at Pumas-AI, and an enthusiastic learner of anything "scientific computing".

I am Associate Senior Lecturer at the Department of Mathematical Sciences of Chalmers University of Technology and University of Gothenburg and working on statistical theory and methodology for dynamical stochastic models. In general, dynamical stochastic models describe the evolution of processes and systems which have dynamics with temporal or spatial interactions and show stochastic behaviour. Applications of such models are found in all areas, be it to model the change in the extension of the West Antarctic ice shelf, the interaction of neurons in the brain or the deformation of tissue during tumour growth.

• Github: https://github.com/mschauer
• Academic website: http://www.math.chalmers.se/~smoritz/index.htm
• Twitter: @MoritzSchauer

Research Software Developer at UCL during the day, binary builder during the night.

Economist living in Montreal. I'm interested in numerical methods and scarred for life by the experience of waiting six months for my estimates to converge.

Senior Machine Learning Engineer at Second Spectrum.

Oleg is a data scientist at Fonterra, a New Zealand Dairy Co-operative. Much of his work concerns milk production planning problems. Previously, he worked at Suez, who supply systems for the control of water utilities based on integer programming.

I'm a master student in computer science in Heidelberg, Germany. I like to teach and blog about programming especially about Julia.

Oscar Dowson is a core-developer of JuMP and member of the JuMP steering committee.

I am a master's student at Utrecht University, and a contributor for Catlab.jl

I'm currently a postdoctoral researcher at the University of Chicago working on a class of strategies used to accelerate learning the properties of systems simulated with molecular dynamics.

Profession: Mechanical engineer
Location: Germany, Landshut
Github: https://github.com/pbayer
Linkedin: https://www.linkedin.com/in/paul-bayer-4104a78/

I am a research scientist at the IBM Research working on the following areas: AutoML, AutoAI, RL/ML Optimization, and Decision Optimization.

I am a systems biology PhD student with background in molecular biology. My doctoral research at the University of Oxford aims at improving our mechanistic understanding of cell cycle dynamics with mathematical models. This involves (1) casting chemical reaction networks into executable models, (2) obtaining highly multiplexed snapshot measurements of cell cycle regulators, from which we reconstruct their time-courses, and (3) developing parameter optimisation and model reduction/selection toolboxes.

Thermodynamics enthusiast.

Software developer, electrical engineer, IC designer. Author of Apicula FPGA tools and Mosaic IC design software.

Philipp A. Witte is a researcher at Microsoft Research for Industry (RFI), a new initiative within Microsoft for developing innovative research solutions for industry-related problems ranging from AI/ML to edge- and high-performance computing. Prior to Microsoft, Philipp received his B.Sc. and M.Sc. in Geophysics from the University of Hamburg and his Ph.D. in Computational Science and Engineering from the Georgia Institute of Technology. During his Ph.D., Philipp worked with Professor Felix J. Herrmann at the Seismic Laboratory for Imaging and Modeling (SLIM) on computational aspects of least squares seismic imaging and full-waveform inversion. He has authored and contributed to multiple open-source software packages, including Devito, the Julia Devito Inversion framework (JUDI) and InvertibleNetworks.jl, a Julia framework for deep learning with normalizing flows.

Phillip was a struggling mathematician, then a linguist and now a neuroscientist, but always a hacker.

I am an Imperial chemical engineering undergraduate with a great interest in all things related to thermodynamics! I’ve been using Julia for the past two years to develop an extensible open-source implementation of equations of state: OpenSAFT

Przemysław Szufel is an Assistant professor at SGH Warsaw School of Economics, Poland and an Adjunct Professor, Cybersecurity Research Lab, Ted Rogers School of Management, Ryerson University, Toronto.

He has first started using Julia at the version 0.3. He is a co-author of the book “Julia 1.0 Programming Cookbook: Over 100 numerical and distributed computing recipes for your daily data science workflow” which has been translated by O’Reilly from English to Japanese. Przemysław also holds currently 4-th place on the StackOverflow portal answering Julia-related questions. He is the maintainer and co-auhtor of three Julia libraries in the public repository: OpenStreetMapX, OpenStreetMapXPlot and SimpleHypergraphs.
He is a co-author of over 40 publications, including handbooks and journal papers, in the area of applying advanced analytics, machine learning, simulation methods and optimization.

Usually writing code (or docs!) or triathlon-ing.

More at my website.

A random Julia enthusiast, occasional contributor.

My research focuses on developing tools to image cellular function at high resolution. We develop techniques to measure multidimensional information in small biological objects such as cells, cellular organelles or other small structures of interest. Computer-based reconstruction methods, especially inverse-modeling based in automatic differentiation are of primary interest.

4th year PhD student at the Julia Lab, MIT.

Richard Gankema is a Computer Scientist at RelationalAI, working on various systems-related topics such as data structures, memory management and query execution. Before joining RelationalAI he worked as a PhD candidate at CWI’s Database Architectures group in Amsterdam, which sparked his interest in vectorization and other techniques for optimizing the performance of data-processing.

I am a researcher at INRIA Sophia Antipolis (France) with interest in Mathematical neurosciences, modeling synaptic plasticity and analysis of PDEs. Please, have a look at my website.

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Santiago Badia is Professor of Computational Mathematics at Monash since June 2019. He obtained his PhD at Universitat Politècnica de Catalunya (UPC) in 2006. Previously, he worked at the Applied Mathematics departments at Politecnico di Milano (Italy) in 2006 and Sandia National Labs (New Mexico, USA) in 2007-08. He joined UPC in 2009, where he was appointed Professor of Computational Science and Engineering in 2017. He is adjoint researcher at CIMNE (Barcelona), where he leads the Large Scale Scientific Computing Department.

He works on the numerical approximation of partial differential equations (PDEs), e.g., using finite element methods, for modelling fluid and solid mechanics, electromagnetics, and multiphysics problems. He is particularly interested in large scale scientific computing and numerical linear algebra.

As a by-product of his research, Prof Badia leads some high-performance scientific projects, like FEMPAR. FEMPAR provides state-of-the-art numerical discretizations of PDEs and highly scalable numerical linear algebra solvers. FEMPAR has been used to model metal additive manufacturing, superconductor devices, breeding blankets in fusion reactors, or nuclear waste repositories. It has attained perfect weak scalability up to 458,672 cores in JUQUEEN (Germany) solving up to 60 billion unknowns. In 2019 he co-started the Gridap project, which heavily relies on functional programming and multiple dispatching in Julia, with the aim to create an easy-to-use but very efficient PDE solver.

I'm a software engineer at Amazon working on Amazon Braket, a managed quantum computing platform. I graduated from The Ohio State University with a Master's in Computer Science in August 2019. I worked on high performance computing for representation learning in graphs at the Data Mining Research Laboratory at OSU. I dabble in quantum computing, graphs, high performance computing and most recently in serverless compute.

Satvik is a co-founder and head of Machine Learning at Temple Capital, a crypto currency quant fund.

PhD Candidate at Cornell University

Software engineer at Julia Computing and one of the maintainers of the Julia extension for VSCode.

Shahriar Iravanian, MD, MSE, is a practicing cardiac electrophysiologist and biophysical researcher with an interest in non-linear dynamics and high-performance computing with a focus on the modeling of cardiac arrhythmias. He received a Master of Science in computer science from Johns Hopkins University and finished his cardiology and electrophysiology training at the Emory University in 2011.

Shashi is a grad student at MIT. Yingbo is an undergrad at UMBC and works at Julia Computing. We like coding in Julia.

Shlok is a Senior Research Analyst at the Federal Reserve Bank of New York working on the DSGE team in the Research group. He holds a B.S. in Economics and a B.S. in Statistics and Machine Learning from Carnegie Mellon University. He is interested in using Julia to develop scalable models for macroeconomic forecasting and spatial economics research.

A research programmer working at Julia Lab, MIT. Working on Julia's compiler technology stack, mainly around its abstract interpretation based type inference. Also a maintainer of Julia IDEs, julia-vscode and Juno.

Simon is the lead software engineer at the CliMA project.

Postgraduate computer science student at the University of Cambridge, currently working with Markus Kuhn.

Sophie is a math PhD student at Stanford University. Her research interests are focused on dynamical systems including topological invariants of dynamical systems, composing dynamical systems, and the question of how continuous dynamical systems compute.

Julia co-creator & co-founder of Julia Computing.

Stephan Sahm is Senior Data Science and ML Engineering Consultant. Having programmed in Java, Matlab, Python, R, Scala and Julia he appreciates the combination of simplicity and speed which the Julia language brings to Data Science. With master degree in math/stochastics and cognitive science together with 5 years industry experience he can help you bring your favourite Data idea into production.

I hold a PhD Economics from the University of Edinburgh. I work in London as a quantitative research in cash equities. I have done a few open source numerical mathematics packages mainly in fixed point acceleration and shape preserving splines.

I'm a postdoc at Julia Lab. I'm working on parallelism and compiler.

Theo Diamandis is a PhD student studying optimization at MIT.

I'm a software engineer at Julia Computing, working on Julia's GPU packages and compilers.

Head of Scientific Computing at Cervest, working on Climate Intelligence solutions which quantify climate risk on a per-asset level, globally.

PhD in Physics from RMIT University in Melbourne, Australia; focusing on defects in the Josephson junctions of superconducting phase-qubits. Followed up by a postdoctoral position at Chalmers University in Gothenburg, Sweden investigating high energy laser-plasma interactions and fusion energy. More recently a researcher at the Stockholm Resilience Centre, with research areas concerning Planetary Boundaries, global climate-economy models, social ecological systems and other Earth system sciences.

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.

Tobias Knopp received his Diplom degree in computer science in 2007 and his PhD in 2010, both from the University of Lübeck with highest distinction. For his PhD on the tomographic imaging method Magnetic Particle Imaging (MPI) he was awarded with the Klee award from the DGBMT (VDE) in 2011. From 2010 until 2011 he led the MAPIT project at the University of Lübeck and published the first scientific book on MPI. In 2011 he joined Bruker Biospin to work on the first commercially available MPI system. From 2012 until 2014 he worked at Thorlabs in the field of Optical Coherence Tomography (OCT) as a software developer. Since 2014, Tobias Knopp is a professor for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology in Hamburg, Germany. Beside his work as a researcher in the field of tomographic imaging method Tobias Knopp is an open-source developer and part of the Julia community since 2012.

Tomas Pevny has graduated from Faculty of Nuclear sciences and Physical Engineering, CTU, Prague, in 2003. From 2004-2008, he was pursuing Ph.D. at Binghamton University, SUNY, USA specializing on Steganalysis. In 2008-2009 he spent a wonderful post-doc year in Grenoble. Since 2009, he is with Faculty of electrical engineering, CTU, at Prague. From 2013-2019, he was also consulting scientist at Cisco and from 2019 he is consulting scientist at Avast. His specialization is machine learning in security domains. He is an active user of Julia since 2015.

Ph.D. candidate at the Stanford Intelligent Systems Laboratory, Stanford (USA).

Tristan Carion is a mechanical engineer from UCL (Université Catholique de Louvain-la-Neuve) in Belgium. After graduating in 2016, he spent a few years working for the industry. He also spent some time traveling the world. He went back to the academic world in 2020 where he joined the Royal Military Academy of Belgium (RMA) as a researcher. He is working for a joint project of RMA, ECMWF and RMI (Royal Meteorological Institute of Belgium), which consists of implementing atmospheric dispersion models and response models on the ECMWF Weather Cloud and use the ensemble forecasts from ECMWF to produce ensemble dispersion modeling.

Vaibhav is involved in building analysis tooling in the SciML ecosystem in Julia and one of the developers of Pumas (https://pumas.ai).

PhD Student at MIT

Valerio is a student in Physics (Bachelor) at the University of Turin. He is interested in electronics, embedded systems, computing and signal processing as tools for investigating physics, such as the technology of the detectors employed in high energy physics.

My expertise is in applied mathematics, computer science and engineering. My research is in the general area of data analytics, model diagnostics and machine learning. I am the inventor and lead developer of a series of novel theoretical methods and computational related to machine learning, data analytics, model diagnostics, and data inference tools. I am also a co-inventor of LANL-patented machine-leaning methodology. Over the years, I have been the principal investigator of several projects for machine learning, model development, model analyses, uncertainty quantification and decision support

Developer of Ahorn and similar tools. Using Julia professionally since 2017.

Recent graduate from the University of Kentucky in Computer Science and Mathematics. Passionate about graphs, machine learning, and logic programming.

PhD Candidate @ MIT

Full Professor in Operations Research and Computer Science, Université de Nantes (France).

Xuan (Sh-YEN, IPA: ɕɥɛn) is a PhD student at MIT in the Computational Cognitive Science and Probabilistic Computing research groups. Their current research focuses on inferring the hidden structure of human motivations by modeling agents as probabilistic programs, in the hope of aligning AI with the higher-order goals, values, and principles that humans strive (in part) to live by.

Associate Professor Yoni Nazarathy from the University of Queensland Australia, specializes in data science, probability and statistics. His specific research interests include scheduling, control, queueing theory, and machine learning. He has been at The University of Queensland for nearly a decade, teaching courses in the Masters of Data Science program and working on research. Prior to his previous academic positions in Melbourne and the Netherlands, he worked in the aerospace industry in Israel. In recent years, he has also been heavily involved with primary and secondary mathematics education and is the co-founder of an EdTech mathematics organization called One on Epsilon. He is also the co-author of a data science book, "Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence". Recently his research has also focused on epidemics and he leads the Safe Blues program dealing with finding efficient and ethical methods to track social mobility with a goal of prediction and control of epidemics.

• B.S. in Mathematics and Computer Science, Pohang University of Science and Technology, 2007
• M.S. in Computer Science, Pohang University of Science and Technology, 2009
• Ph.D. in Computer Science, University of Wisconsin-Madison, 2017
• Postdoctoral Appointee, Argonne National Laboratory, 2018-Current

Ph.D. Senior Scientist, Japan Atomic Enegy Agency

Assistant Professor of Aerospace Engineering at the University of Colorado

Zenna Tavares is a postdoctoral researcher at MIT under the supervision of Armando Solar Lezama. His interests are in probabilistic and causal inference, programming languages, and human-inspired artificial intelligence.

PhD student @ TU-Darmstadt