SciPy 2026

Aarti Jha is a Principal Data Scientist at Red Hat, where she leads the development of AI-driven solutions that streamline internal operations and reduce costs. She has more than seven years of experience designing and deploying machine learning and generative AI solutions across multiple industries. A frequent speaker at developer and data science conferences, she shares practical insights on applied AI, LLMs, and building AI systems that deliver measurable business value.

Amber Case is a design advocate, speaker, and author of four books including Calm Technology and A Kids Book About Technology. Fellow at MIT’s Center for Civic Media and Harvard’s Berkman Klein Center for Internet & Society, co-founder and CEO of Geoloqi (acquired by Esri). Named 30 under 30, Fast Company Most Influential Women in Tech, National Geographic Emerging Explorer, she received Bell Labs Claude Shannon Innovation Award. She studies human-technology interaction, culture, design, governance, and AI. At Metagovernance Project, she founded the Calm Tech Institute

I am one of the less-active napari core devs, with most of my contributions these days coming through vispy or on the web infrastructure (npe2api and napari hub). I first started working on napari as part of the CZI Imaging Tech team, but I now participate primarily in my spare time.

I got into Python and open source while studying Medical Physics at UW-Madison. After a few years in academia at Barrow Neurological Institute (Phoenix, AZ), I made the switch to industry. I first worked as an on-site clinical MRI scientist for Philips (Mayo Clinic, Rochester, MN), then joined a low-field MRI startup called Hyperfine (Guilford, CT). I'm now a remote worker for Biohub and still reside in Guilford, CT; but I'm in the middle of a move to Dobbs Ferry, NY.

Ashwin Srinath is a Senior Software Engineer at NVIDIA, where he works on making GPU programming from Python delightful.

I’m a Machine Learning Engineer with experience spanning data science, data engineering, and AI platform development across research, enterprise, and product driven environments. I began my career in public health and infectious disease research, working with academic and government partners on large scale statistical modeling, record linkage, and population-level analysis. I later transitioned into industry, where I’ve built and deployed production-grade data and machine learning systems that power real world products.

My work has covered the full ML lifecycle, including data pipelines, feature stores, model training, explainability, observability, and large scale deployment. I’ve contributed to recommendation systems, ranking and propensity models, generative AI applications, and MLOps platforms that enable teams to ship and maintain models reliably in production.

Beyond my core roles, I’ve worked on applied AI and consulting projects, including an AI powered analytics platform for higher education institutions, and advisory work on computer vision and automation for e-commerce workflows. I’m especially interested in scalable and interpretable AI, responsible deployment, and making advanced AI systems practical for real world teams. Outside of work, I enjoy mentoring, volunteering, traveling, and engaging in conversations around AI education, ethics, and regulation.

Austin Aguilar is a Software Engineer for the Atmospheric Radiation Measurement (ARM) Data Center at Oak Ridge National Laboratory. Austin helps develop and maintains a variety of user facing applications that allow researchers to focus on their science rather than data discovery. Austin is also very passionate about researching and developing agentic AI workflows to be leveraged within the scope of science.

Balaji Sundaram has been a software engineer at Bloomeberg since 2018, where he works on a team that builds and maintains JupyterLab extensions for data analysis in BQuant. Prior to joining Bloomberg, Balaji worked on building greenfield products for a consulting firm. Balaji holds a bachelor's degree in computer science from North Carolina State University.

Ben is the Responsible AI Lead at Philips, working on building AI governance tools and policies. Previously, he led data science teams in academia, industry and government. He has focused primarily on language technology and has delivered tutorials and designed classes on LLMs and agent development. He contributes to the larger data science community through independent projects and leading the PyData Boston chapter.

Bhupendra A. Raut is a Computational Environmental Scientist at Argonne National Laboratory, where his research focuses on the analysis of clouds and precipitation in large-scale remote sensing datasets and numerical model outputs. He has developed convection identification, tracking, and analysis algorithms and applied various clustering and machine learning methods to produce value-added products for multi-platform observational campaigns. Currently, he is co-developing an adaptive sensing framework for the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility and leads the Chicago Urban Flux Network.

His interdisciplinary research leverages statistics, computer vision, machine learning, and edge computing. An active member of the scientific community, he contributes to several prominent open-source atmospheric tools, including Py-ART, TINT, and tobac. Dr. Raut holds a Ph.D. and an M.Sc. in Atmospheric and Space Sciences from the University of Pune, following a B.Sc. from J. B. College of Science.

Bobby Jackson is an atmospheric scientist at Argonne National Laboratory. His research interests include radar meteorology, using AI and edge computing to improve atmospheric observations, and open source software development for the atmospheric sciences. He is a lead developer on PySP2 and PyDDA, two open source Python packages for aerosol and radar wind retrievals. In addition, he is a contributing developer to numerous packages in the Pangeo and Open Source Radar communities, including PyART.

Bradley Dice is a Senior Software Engineer in GPU-Accelerated Data Analytics at NVIDIA, designing high-performance open-source libraries for data analytics (cuDF) with modern CUDA, C++, and Python.

I am an Assistant Professor at the Donders Institute and Radboudumc in The Netherlands. My current research focuses mainly on the intersection of language and memory. I work with human electrophysiological data and have a focus on data analysis methods such as source reconstruction and decoding. I am enthusiastic about open source and am part of the core developer team of MNE-Python since 2019. Since 2024, I am also part of the newly-formed steering council of MNE-Python.

Bryce Adelstein Lelbach has spent over a decade developing programming languages, compilers, and libraries. He is passionate about parallel programming and strives to make it more accessible for everyone.

Bryce is a Principal Architect at NVIDIA, where he founded the Core C++ Compute Libraries team and now leads the Vanguard Programming group that drives NVIDIA's roadmap for programming languages, compilers, and core libraries.

He is a leader of the systems programming language community, having served as chair of the C++ Library Evolution and the US programming language standards committee. He has been an organizer and program chair for many conferences over the years. On the C++ committee, he has worked on concurrency primitives, parallel algorithms, senders, and multidimensional arrays.

He previously worked at Lawrence Berkeley National Laboratory and Louisiana State University. He is one of the founding developers of the HPX parallel runtime system.

Outside of work, Bryce is passionate about airplanes and watches. He lives in Midtown Manhattan with his girlfriend and dog.

Post-Baccalaureate Research Fellow at Northwestern University, Center for the Interdisciplinary Exploration and Research in Astrophysics (CIERA). Assistant Program Coordinator for the new CIERA Tech Council. Studying environmental science at the University of Chicago (M.S. 2027 exp.).

Carolyn Olsen developed HiveGuide as an independent open-source project to solve practical field inspection challenges in beekeeping and generalized for broader scientific applications. In her day role, she is Director of Data Science at The Hartford, where she leads an AI Accelerator for two business areas, helping them leverage generative AI. Previously VP of Data Science at Clearcover, she has extensive experience developing production AI systems including LLM-powered tools, supervised ML models, and reinforcement learning models. She holds a Master of Science in Applied Economics from Marquette University and served 8 years in the U.S. Coast Guard Reserve.

Carson is currently a Principal Software Engineer at Posit Software, PBC. He's an original author and maintainer of projects such as shiny, shinywidgets, shinylive, and chatlas. Prior to joining Posit, Carson was an engineer at Plotly for numerous years, won the ASA's Chambers Award, and received his PhD in 2017.

Charles is a Research Software Engineer at ACCESS-NRI, where he works in the Model Evaluation and Diagnostics team, helping make it easier to access and analyse climate data. He has a PhD in Oceanography, where he first discovered his love of wrangling and disseminating data.

When not in front of a computer, he enjoys routinely injuring himself in a variety of sports.

Chirag Shah is an Environmental Data Science Engineer and full-stack software developer working with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility Data Center. His work focuses on building scalable scientific data systems that improve the discoverability, accessibility, and usability of large-scale atmospheric and environmental observations.

At the ARM Data Center, Chirag leads the design and development of modern research software platforms used by scientists to explore, analyze, and interact with complex observational datasets.
Chirag's technical interests span scientific data management, distributed systems, artificial intelligence, machine learning, and advanced data visualization. His work emphasizes building robust infrastructure and user-centric tools that enable researchers to efficiently work with large observational datasets and accelerate scientific discovery in Earth and environmental systems research.

Committed to advancing modern research software practices, Chirag actively explores emerging technologies that enhance the way scientists interact with complex data ecosystems.

I'm a principal software engineer at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Our prime directive is to archive earth science data and make that data available to the public for free. Since joining the GES DISC, I've mainly focused on the services end of public data access, working on tools that allow users to do some initial data exploration and visualization without having to download, understand, and open raw data files. I'm happy to wax poetic about metadata, interoperability, and well designed colorbars.

Dr. Cliff Kerr is a Senior Software Engineer at the Institute for Disease Modeling, part of the Gates Foundation, where he works on HIV, STIs, tuberculosis, and family planning. Previously, he completed a B.Sc. in neuroscience and a Ph.D. in physics, was a lecturer in scientific computing at the University of Sydney, co-founded two startups (on data analytics and health economics), worked on a DARPA project teaching robots to pick up balls, and developed an algorithm that composes music in real time based on brain activity recordings. He lives in New York.

Cynthia is a Machine Learning Engineer, where she serves as head architect and developer for a DARPA-funded ML analysis platform. The platform analyzes LLM embeddings using topology as the underlying mathematical framework, enabling researchers to study language model behavior at scale.

Beyond the core platform, she builds production APIs for vision-language models and text-to-speech systems, bringing cutting-edge AI research into operational deployment.

With an MS in Applied & Computational Mathematics from Johns Hopkins and prior experience at General Atomics-CCRi and Arity (geospatial ML at scale), she combines deep mathematical foundations with practical engineering expertise in PyTorch, AWS, and distributed systems.

She has mentored over 30 students through programs at The Coding School, Masterschool, and Springboard, helping them transition into data and ML roles. She also writes about ML systems on her blog: cynscode.com

Daina Bouquin is Senior Developer Relations Engineer at Anaconda with over 12 years of experience spanning astrophysics, library science, and software development. She previously served as Head Librarian at the Harvard-Smithsonian Center for Astrophysics, where she led projects on software citation, preservation, and recovering the contributions of early women in computing. This work gave her deep familiarity with historical computing collections in addition to experience supporting scientists doing computational research. At Anaconda, she creates educational content and strengthens connections between engineering teams and the broader open source community. She believes documentation isn't just about clarity, it's about building communities where people want to participate.

Daniel Chen is a data science educator working in developer relations at Posit, PBC, and a lecturer at the University of British Columbia. He specializes in teaching and advocating for modern data science tools and workflows.

I am a developer of open-source scientific software, and a scientist trained in acoustic phonetics, speech perception, and auditory neuroscience. My scientific interest broadly centers on the perception and representation of speech sounds. I'm (probably) most known for my work on MNE-Python.

Dario is the founder and director of the Open Source for Science Fund, a multi-donor fund by Renaissance Philanthropy launched in 2026 aiming to sustain and evolve the open source stack that powers scientific discovery. He previously led a portfolio of philanthropic investments in open science and open source at the Chan Zuckerberg Initiative, including the Essential Open Source Software for Science (EOSS) program, which supported for six years multiple libraries and communities in the scientific python ecosystem.

David Williams-Young is a Principal Quantum Software Architect at Microsoft Quantum, where he serves as software lead for quantum applications. His work focuses on quantum computing applications in chemistry and materials science, including quantum algorithms, classical simulation methods, and the development of tools that bridge quantum computing and computational many-body theory. Prior to joining Microsoft, he was as a Scientist in the Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory, where he developed exascale electronic structure methods and software for DOE Leadership Computing Facilities. He received his Ph.D. in Chemistry from the University of Washington, specializing in relativistic electronic structure theory. He is the author of numerous open-source computational chemistry libraries and has served as a major contributor numerous quantum chemistry software packages.

Eni is a scientific software developer at NASA Goddard’s Earth Science and Information Services Center (GESDISC) and Xarray core developer. At GESDISC she uses open-source tools to create services in support of NASA’s vast earth science catalog and contributes to several enterprise NASA ESDIS tools. She is interested in using computational science to improve our understanding of the natural world.

Research Scientist at the Institute for Learning and Brain Sciences, University of Washington, Seattle, WA.

As Senior Principal Data Scientist at Moderna Eric leads the Data Science and Artificial Intelligence (Research) team to accelerate science to the speed of thought. Prior to Moderna, he was at the Novartis Institutes for Biomedical Research conducting biomedical data science research with a focus on using Bayesian statistical methods in the service of discovering medicines for patients. Prior to Novartis, he was an Insight Health Data Fellow in the summer of 2017 and defended his doctoral thesis in the Department of Biological Engineering at MIT in the spring of 2017.

Eric is also an open-source software developer and has led the development of pyjanitor, a clean API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He is also on the core developer team of NetworkX and PyMC. In addition, he gives back to the community through code contributions, blogging, teaching, and writing.

His personal life motto is found in the Gospel of Luke 12:48.

Mathematician, Physicist, and Computational Chemist in love with ab initio modeling, surface science, and catalysis. Developer of improved quantum chemistry methods spanning Density Functional Theory (DFT), Density Functional Tight Binding (DFTB), and Machine Learning Interatomic Potentials (MLIPs). Advocate and early-stage contributor to open-source projects for transparent and collaborative science.

Georg is a Senior data expert at Magenta and a ML-ops engineer at ASCII. He is solving challenges with data. His interests include geospatial graphs and time series. Georg transitions the data platform of Magenta to the cloud and is handling large scale multi-modal ML-ops challenges at ASCII.

Co-chair of SciPy 2026

Gwyn Uttmark serves at Colorado State University and has more than a decade of expertise in open-source scientific software development. Gwyn currently works with the Cooperative Institute for Research in the Atmosphere to make the US Navy-born GeoIPS an accessible and effective platform for open-source development communities and academic researchers.

Hadley is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr)and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science. Learn more on his website, http://hadley.nz.

Hannah Ferriby is an Environmental Data Scientist at Tetra Tech based out of the Lansing, MI area. She received her BSE in Environmental Engineering from the University of Michigan and her MS in Biosystems Engineering from Michigan State University. She specializes in geospatial and remote sensing analysis with a focus on water quality applications. Her work ranges from cyanobacteria harmful algal bloom forecasting to numeric nutrient water quality criteria development to biodiversity metric creation. Hannah is newer to coding in Python but has extensive background in R and JavaScript (Google Earth Engine).

Hernan Picatto is a Data Engineer at the Supply Chain Intelligence Institute Austria (ASCII) and a PhD student in Informatics at TU Wien. His work bridges the gap between modern data orchestration and High-Performance Computing (HPC), focusing on reproducible workflows for web-scale NLP. Hernan is a core contributor to dagster-slurm and currently manages pipelines that process petabytes of Common Crawl data to reconstruct global supply chain networks. Before returning to academia, he worked as a Senior Software Engineer at JPMorgan Chase and an Algorithm Engineer at ZhiShou Technology in Beijing.

Hongsup Shin is a Senior AI & LLM Engineer at NVIDIA's Silicon Co-design Group, building agentic systems and automation for silicon engineering workflows. His work spans production RAG infrastructure, multi-agent systems, learning-to-rank for hardware verification, and human-in-the-loop system design. He has published at IEEE SOCC (2022, 2024) and previously presented at SciPy 2019 on ML applications for hardware failure detection. He currently serves as SciPy Conference Proceedings Chair, founded the Austin ML Journal Club, and has volunteered with the Texas Justice Initiative since 2019, where he authored TJI's first data analytics report on officer-involved shootings in Texas. He holds a Ph.D. in Neuroscience from Baylor College of Medicine.

I am working as an Xarray community developer at Earthmover. In this role I am focused on improving Xarray’s support and documentation for the biology/biomedical community. Prior to this I completed my PhD in which I extensively used Xarray, zarr and the Pydata stack to implement custom microscope control software and analyze multimodal timelapse single cell microscopy data. I loved the open source scientific software so much that now I get to work full time improving it and sharing it with others.

Research Software Engineer, Princeton University
Ianna Osborne is an open-science advocate and research software engineer specializing in particle physics and high-performance computing. She builds scalable, open-source tools for scientific discovery, maintains the Awkward Array project, and leads international efforts that foster collaboration and sustainable research software.

I'm a PhD student in the Department of Physics and Astronomy at Rice University, conducting research in high-energy physics as a member of the CMS experiment at the Large Hadron Collider at CERN. My work focuses on studying Higgs boson decays into two photons, analyzing data collected by the CMS detector, and contributing to software development for large-scale scientific analyses. I'm passionate about scientific computing and open-source tools that enable reproducible and efficient research. I’m maintainer of Awkward Array, an array library for nested, variable-sized data, using NumPy-like idioms, and an author and maintainer of Coffea, a toolkit designed to simplify data analysis in particle physics. With deep experience in the scientific Python ecosystem, I enjoy building tools that drive insight and accelerate scientific discovery.

Inessa is building bridges between people, open source software, and open science. Over the years, she has launched and continues to support several educational initiatives focused on widening the open source contributor pipeline. Inessa is Director of Open Source Program Office at OpenTeams and guest faculty at University of Connecticut. She also serves on the NumPy Steering Council, Scientific Python Ecosystem Coordination Steering Committee, and the pyOpenSci Advisory Council. Inessa is perpetually fascinated by incentive design, collaborative intelligence, and jazz.

Jake Diamond Reivich is an Executive Council member of Project Jupyter. He is also the CEO of Mito, an open source company that builds on top of the Jupyter ecosystem. As an Executive Council member, he is elected by the Jupyter community to help steward Project Jupyter through the new age of AI tooling. One of his focuses is sharing Jupyter AI with the larger open source developer and researc community.

Jim Bednar is the Senior Director of Professional Services at Anaconda, Inc. Dr. Bednar holds a Ph.D. in Computer Science from the University of Texas, along with degrees in Electrical Engineering and Philosophy. He has published more than 50 papers and books about the visual system, software development, and reproducible science. Dr. Bednar founded the HoloViz project, a collection of open-source Python tools that includes Panel, hvPlot, Datashader, HoloViews, GeoViews, Param, Lumen, and Colorcet. Dr. Bednar was a Lecturer and Reader in Computational Neuroscience at the University of Edinburgh from 2004-2015, and previously worked in hardware engineering and data acquisition at National Instruments.

Jarrod Millman is the Executive Director for Berkeley's Open Source Program Office (OSPO). With a background in computer science, mathematics, and statistics, and degrees from Cornell and Berkeley, Millman is a founding member of the scientific Python ecosystem. His primary focus is on developing and sustaining open-source, community-owned scientific software tools. Millman serves on the steering council of NetworkX, is a core developer of scikit-image, and was an early contributor to NumPy, SciPy, and scikit-learn. He has co-founded several influential initiatives to advance open and reproducible research, including the Scientific Python project, the nonprofit NumFOCUS, and the Neuroimaging in Python project.

Jasmine Omeke is a senior software engineer at Airbnb, where she focuses on data infrastructure. Before joining Airbnb, she worked as a software engineer at Netflix and PayPal, specializing in distributed systems and large-scale data processing, and building backend services that handled petabytes of data. She has experience creating eLearning content and authored a Python testing course on LinkedIn Learning. Jasmine also mentors aspiring computer science students through CodePath’s technical interview preparation program. A former Gates Millennium Scholar, she is excited to give back to the community that supported her early academic journey. Outside of work, Jasmine enjoys swimming and sewing. She holds a Bachelor of Arts from Harvard University and a Master of Computer Science from DePaul University.

Jaya Venkatesh is a Software Engineer at NVIDIA, working on the RAPIDS ecosystem to streamline the deployment of GPU-accelerated data science workflows across cloud and distributed systems. Previously, he was a Machine Learning Engineer at Pixxel Space, where he developed large-scale, real-time data processing and inference pipelines for Earth observation using GPU-accelerated Python libraries.

Jim was trained as a particle physicist with a Ph.D. from Cornell and helped commission the CMS experiment at the Large Hadron Collider (LHC). He has worked as a data scientist (at Open Data Group) and a software developer (at Princeton), and was the founder of the Awkward Array project. Jim is now at the University of Chicago's Data Science Institute, where he solves data analysis problems for nonprofit organizations.

Jocelyn Graf is an Entrepreneur in Residence at the University of California Riverside and advises small businesses through the Los Angeles Small Business Development Centers (SBDCs). Her expertise includes helping technical and non-technical people communicate and collaborate. She also helps academics and entrepreneurs apply for federal Small Business Innovation Research (SBIR) grants to commercialize their discoveries. In South Korea, she worked at Samsung and then founded, grew, and sold a biomedical & engineering research editing & translation company. In LA, she has also been a college STEM Director and founded a workforce training company that helped early-career engineers get fun product development experience by designing and building hardware for escape rooms and gaming conventions. Jocelyn works remote from Seattle, is an avid cyclist, and volunteers helping community organizations migrate from big tech platforms to open source alternatives. She is currently working on learning to code in a more “pythonic” style.

John Hoehner is an Instrumentation Engineer at Nominal.io.

Dr. Joseph H. Kennedy is a Staff Scientist for the Alaska Satellite Facility (ASF) and Geophysical Institute at the University of Alaska Fairbanks. He is a computational glaciologist by training but has since transitioned primarly into PB-scale remote sensing/Earth observing data processing and is best know for is work on the ITS_LIVE global glacier velocity project and the development of ASF's on-demand processing system, HyP3. He specializes in bridging the gap between scientists and software engineers, building ground-up Cloud and HPC data processing/analysis platforms for users, managing global-scale processing campaigns, and generating analysis-ready EarthObserving/Modeling data.

Josh is a sixth (and last!) year PhD student in the MIT Visualization Group. He builds new theories and libraries for charts and diagrams and is obsessed with how a well-designed picture can create a new insight. In his free time (any time really), Josh may be seen doing contact improv, singing, or playing guitar.

Hi there! I did my Bachelor in Bioengineering at McGill University (2019-2023), after which I did my Master's thesis in Synthetic Biology & Systems Biology in the Ignea Lab at McGill University (2023-2025) where I studied transcriptomics and metabolomics in Tacca plant species. I fell in love with foundational protein language models and modeling enzymes functions using structural and sequence information. After participating in the Align Bio 2025 PETase protein engineering tournament in Fall 2025 with my teammates, we developed a protein engineering framework using our methodology which we are presenting at the SciPy 2026 conference!

Katie Wetstone is a data scientist with a passion for leveraging machine learning tools to promote sustainable, ethical, and just change. At DrivenData, she works to implement open-source machine learning competitions and direct consulting projects that support mission-driven organizations. Her projects have spanned a variety of issues including public health, conservation, and education. She holds a BA in chemistry from Harvard University, and a Masters of Development Practice from the University of California, Berkeley.

Dr. Katrina Riehl is a Principal Technical Product Manager at NVIDIA leading the CUDA Education program. For over two decades, Katrina has worked extensively in the fields of scientific computing, machine learning, data science, and visualization. Most notably, she has helped lead data initiatives at the University of Texas Austin Applied Research Laboratory, Anaconda, Apple, Expedia Group, Cloudflare, and Snowflake. She is an active volunteer in the Python open-source scientific software community and currently serves on the Advisory Council for NumFOCUS.

Kristijan Armeni is a research scientist with doctoral and postdoctoral training in computational neuroscience, investigating language processing in the human brain (EEG/MEG) and in artificial cognitive systems (language models). He is an advocate of open science and maintains an interest in public interest technology and civic tech. He currently helps building the CIB Mango Tree project, an interactive open source tool for analyses of social media datasets.

Co-chair of SciPy 2026

Mahima Arora is a Senior Data Scientist on the Data & AI team at Red Hat, specializing in Generative AI applications. She develops AI-powered solutions that enhance efficiency and effectiveness, leading initiatives to optimize AI systems for greater impact. Passionate about open source, Mahima actively explores emerging tools and technologies to drive innovation and knowledge sharing, and has presented her work at PyData Amsterdam 2025 and PyCon India 2025.

Matt has been using Python to work with data in science and at startups since 2008, after getting degrees in Astronomy and Aerospace Engineering. He maintains some moderately popular open-source Python libraries, including SnakeViz and Palettable. Today Matt is the lead software engineer at Populus, a startup helping city governments manage various aspects of transportation.

I am a research software engineer who helps scientists perform computational image analysis for reproducible research.

Matthew is a research scientist in experimental high energy physics and data science at the University of Wisconsin-Madison Data Science Institute (a “data physicist”). He works as a member of the ATLAS collaboration on searches for physics beyond the standard model with experiments performed at CERN's Large Hadron Collider (LHC) in Geneva, Switzerland. He also serves on the executive board of the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) where he is a researcher and the Analysis Systems Area lead. He is also a topical editor for physics and data science for the Journal of Open Source Software. He previously did his Ph.D. (2019) research at Southern Methodist University, also on the ATLAS experiment, and was a postdoc at the University of Illinois at Urbana-Champaign, and the University of Wisconsin-Madison.

Associate Professor at the University of Minnesota Duluth in the Mechanical and Industrial Engineering Department and developer of the EngineeringPaper.xyz engineering calculation web app.

Dr. Michael Zargham is the Chief Engineer at BlockScience, a systems engineering firm that operationalizes emerging technology for high reliability organizations. His work focuses on digital infrastructures supporting ecosystems which span many geographies and jurisdictions. He serves roles in various non-profit organizations: advisor to Humane Intelligence, Board Member & Research Director at Metagov, Trustee for the Superset Data Trust, and is an advisor to NumFocus. Dr. Zargham received his PhD in Electrical and Systems engineering from the University of Pennsylvania with focus on optimal control for dynamic resource allocation in networks.

Nathan Martindale is a data scientist in the Nuclear Nonproliferation Division at Oak Ridge National Laboratory. Nathan completed both his B.S. (2018) and M.S. (2020) degree in computer science at Tennessee Tech University, with his graduate studies focusing on machine learning. His recent research interests have included visual analytics, system dynamics, and knowledge management, and he has released several open source libraries supporting research experiment management, system dynamics model creation and analysis, and interactive machine learning.

Naty Clementi is a senior software engineer at NVIDIA. She is a former academic with a Masters in Physics and PhD in Mechanical and Aerospace Engineering to her name. Her work involves contributing to RAPIDS, and in the past she has also contributed and maintained other open source projects such as Ibis and Dask. She is an active member of PyLadies and an active volunteer and organizer of Women and Gender Expansive Coders DC meetups.

I am an Assistant Professor in the Department of Genomics and Computational Biology and the Department of Systems Biology at UMass Chan Medical School.

I lead a computational research group (https://abdenlab.org) with a dual mandate. My group's biological research focuses on the 3D organization of the genome (3C/Hi-C technologies), its relationship to the epigenome, and the resulting manifold influences on cellular fate, differentiation, aging, and disease. My group's open-source interests are in supporting foundational infrastructure to improve AI and data science for genomics and multi-omics, especially in the scientific Python ecosystem.

Nicholas Geneva is a Senior Software Engineer in HPC/AI at NVIDIA, specializing in the development of platforms that integrate deep learning with physics and climate science. With over a decade of experience in scientific software development, he currently focuses on developing NVIDIA’s software for Earth-2 to enable AI driven weather / climate prediction for everyone. Nicholas has been a core developer of NVIDIA’s PhysicsNeMo Python packages for the past four years.

Nick Hodgskin is a Research Software Engineer and Xarray maintainer working at Utrecht University, primarily on Parcels - a Lagrangian simulation framework used in physical oceanography. Here he has been leading a rewrite of Parcels to use Xarray as a core data structure, along the way improving Parcels interoperability with the Pangeo ecosystem of packages. A self proclaimed "Pangeo evangelist", Nick loves communicating the power of the Scientific Python stack with Xarray for geospatial analysis - which he does by organising fortnightly talks at his institute, as well as by giving tutorials. When he isn't coding, you can find Nick playing Ultimate frisbee, hiking in nature, reading, or learning languages.

• GitHub

I am a data scientist and software engineer specializing in Python+Rust integration. I currently work at the Rose Center for Earth and Space optimizing astrophysical simulations and writing high-performance library code.

Nikolina “Niko” Šarčević is a cosmologist at Duke University working on cosmological inference and large-scale structure. She is a member of the LSST Dark Energy Science Collaboration (DESC) and the NASA Roman Space Telescope science collaborations. Her research focuses on statistical methods, astrophysical systematics, and scientific software for cosmology. Previously, she worked on dark matter searches as part of the XENON experiment.

Patrick Deziel is a machine learning engineer and Python and Go programmer. Patrick has extensive experience building machine learning powered applications and contributing to open source projects such as Yellowbrick, an ML visualization library written for Python. He currently works at Rotational Labs where he builds software to support prototyping and evaluation of AI/ML powered solutions.

Paul Ivanov's been proudly coming to SciPy since 2009. He got his start in the community in Matplotlib, and went on to also gain the commit bit to IPython and Jupyter projects. Away from keyboard, he likes biking and beekeeping.

About My Background:
I'm a Senior Software Engineer with 9+ years of experience in software engineering and AI/ML research. I pursued MS in Applied Computer Science and am also pursuing PMBA currently. My research focuses on practical applications of machine learning, optimization techniques, and generative AI models across various domains.

Published Work:
My recent publications include:
Planogram Synthesis using Diffusion Models - Published by Springer (constraint-aware generative models for spatial optimization)
LSTM Compression Techniques - Accepted at IEEE ICUIS 2025 (neural network optimization for resource-constrained deployment)
Generative AI for MES Optimization: LLM-Driven Digital Manufacturing Configuration Recommendation- Published in International Journal of Applied Mathematics (LLM-based optimization for manufacturing systems)
Comparative Analysis of Optimized GCD and Hybrid LLM-GCD Approaches for Retail Shelf Space Allocation - Published in European Journal of Information Technologies and Computer Science (hybrid approaches combining LLMs with classical optimization)
Cost-Performance Analysis of Cloud-Based Retail Point-of-Sale Systems: A Comparative Study of Google Cloud Platform and Microsoft Azure
ResearchGate: https://www.researchgate.net/profile/Ravi-Teja-Pagidoju/research

Peer review Experience:
I have reviewed papers at IEEE Transactions on Industrial Informatics Journal(Q1).
I have judged multiple hackathons, DECA startup pitches, business intelligence awards,.
I’m also a mentor at Fuel accelerator (
https://www.fuelaccelerator.com) , an active member in Retail AI Council.

My Speaking Experience:
Presented at Generative AI Expo 2026
Presented at NWA Tech Fest
Presented Keynote at SCRS ConferenceSCRS Conference
Regular knowledge sharing within engineering teams

Email: Pagidojuraviteja1@gmail.com

Rebecca Ely has been involved with Bloomberg’s adoption of open source technologies since 2016. In 2022, Ely joined both the Jupyter Frontends (formerly JupyterLab) Council and the Jupyter Accessibility Council. Ely’s 2015 career transition into tech was preceded by roles as a federal acquisition consultant, math and science teacher, and service monkey trainer. Ely holds a bachelor's degree in peace and justice studies from Wellesley College.

I am a former epidemiology researcher who has spent approximately a decade employing causal modeling and inference. The bulk of my academic career was spent conducting data analyses to estimate the population-level effects of harmful environment exposures, when traditional randomized experiments were infeasible or unethical.

Since leaving the academic world, I've been loving my second life in the tech industry as a data scientist, AI/ML engineer, and more recently as a Director of Data Science Observability at Capital One. I love mentoring junior data folks and explaining the magic of data analysis and modeling to non-technical audience. I am also a proud member of the open-source community!

Relevant links:

• https://ronikobrosly.github.io/
• https://www.linkedin.com/in/ronikobrosly/

Ruben is part of the Prefix.dev core team, builing Pixi and other tools in the package management space. Originally he's a Robotics engineer working on industrial robots, but quickly figuring out that solving development and deployment problems were one of the bigger issues that robotics developers had to deal with. Joining Prefix.dev allowed him to focus on improving the UX/DX of a large group of software engineers. Over the years he's been doing multiple talks and workshops on how to properly manage software and development workflows.

Ryan C. Cooper is an Associate Professor-in-Residence at the University of Connecticut. His background is in mechanics and materials science with an emphasis on numerical simulations and engineering education. He has been using Jupyter and GitHub to enhance the classroom experience for over six years. Prof. Cooper has developed and free open source materials for computational work in engineering and volunteered with the NumPy documentation team SciPy track chair. Ryan is an integral part of the AI in the School of Engineering committee. He has a Ph.D. from Columbia University and spent two and a half years at Oak Ridge National Laboratory as a Postdoctoral researcher.

Sai Sanjana Prakash is an R&D scientist with training in Computer Science and Biomedical Engineering from Georgia Tech. She is passionate about tackling challenges at the intersection of computation, science, and engineering. Her work and interests span the foundations of intelligence, protein design and engineering, and the development of computational tools to advance scientific discovery. Driven by a deep curiosity for complex systems, Sanju focuses on translating insights into innovative, practical solutions.

Sara Altman is a developer advocate on the AI Core team at Posit, where she focuses on how AI can be effectively and thoughtfully used for data science. She co-authors the Posit AI newsletter with Simon Couch. Previously, she helped build Posit Academy and taught data science and R at Stanford.

Sarah Tabor is a Data Scientist and Data Engineer at BETA Technologies, a Vermont based aerospace company designing and building the future of electric flight.

Sarah designs and builds cloud-native data pipelines from source to analysis using Python and open-source tools, all in service of a "Data For All" philosophy that democratizes access to data and insights for anyone at BETA, from engineers to executives.

Sarah holds BBAs in Economics, Finance and Business Analytics from the University of Iowa, and an MS in Complex Systems and Data Science from the University of Vermont.

Shawn Crawley is an Associate Software Engineer at Lynker where he primarily supports the Geospatial Intelligence Division of the National Water Center under NOAA's Office of Water Prediction. His expertise includes automating and optimizing GIS-data-driven workflows based in SQL, Python and JavaScript. He received his Master's Degree in Civil and Environmental Engineering from Brigham Young University with a focus on GIS and Hydroinformatics.

Shruti Sapre has been a software engineer at Bloomberg since 2022, where she works on a team that builds and maintains JupyterLab extensions for data analysis in BQuant. Before joining Bloomberg, she worked on static analysis tools for MATLAB. She holds a master's degree in computer science from the University of Southern California, and a bachelor’s degree in computer engineering from the University of Pune.

Shubham Sharma is a Senior Data Scientist with more than ten years of experience at the convergence of Remote Sensing, Image Processing, Computer Vision, and Deep Learning. He has been an active contributor to the open-source scientific computing ecosystem, engaging with communities through conferences such as SciPy and as a past speaker at PyCon. His work includes significant experience in Synthetic Aperture Radar (SAR) image processing, and he is deeply committed to advancing knowledge of satellite image analysis using Python within the open source community.

Simon Couch builds tools that make the work of data science more joyful and effective. As an engineer on the AI Core Team at Posit, his work spans coding assistants, model evaluations, and next-edit-suggestion systems. Drawing on his background in statistics, Simon spent several years authoring and maintaining core packages in the open-source tidymodels framework—like stacks, broom, and infer—before shifting his focus to LLMs. He blogs about his work at simonpcouch.com.

Smit Lunagariya is a Machine Learning Engineer at Google and an active contributor to the scientific Python and open-source ecosystems. He holds an Integrated Dual Degree (Bachelor's and Master's) in Mathematics and Computing Engineering from the Indian Institute of Technology (BHU), Varanasi.

His involvement in open source began with Google Summer of Code in 2020 and has since expanded to include contributions to several scientific computing projects, including SciPy, SymPy, LPython, LFortran, QuantEcon, and Aesara.

I am a full-time maintainer and community manager of napari, an interactive multi-dimensional Python image and data viewer, and its plugin ecosystem. I work to extend the plugin ecosystem and help scientists achieve their goals with image analysis.

Tomasz Kalinowski is a scientist turned software engineer at Posit, building open-source tools for data and scientific computing across Python and R. His work focuses on cross-language interoperability, machine learning workflows, and performance. He maintains reticulate and the tensorflow/keras R interfaces, coauthored Deep Learning with R, and helps lead Posit’s open-source PyData team (Great Tables, Plotnine, pointblank).

Dr. Tracy K. Teal has been the Open Source Program Director at RStudio/Posit and Nixtla, Executive Director of Dryad, and a co-founder and Executive Director of The Carpentries and is now the CEO at openRxiv. She developed open source bioinformatics software as an assistant professor at Michigan State University and holds a PhD in Computation and Neural Systems from California Institute of Technology. Tracy is involved in the open source software and reproducible research communities, including serving on advisory committees for NumFOCUS, pyOpenSci, R Consortium, and CarbonPlan, and has been working with open source communities, developing curriculum, and teaching people how to work with data and code as a developer, instructor and project leader throughout her career.

Trevor is a researcher and software engineer working on interactive computing tools in Python. He completed his PhD in the HIDIVE Lab at Harvard Medical School, where he developed interactive visualization and analysis tools for biological and AI applications. He created anywidget and contributes to open-source data tooling. Trevor now works at marimo, building a reactive notebook environment for Python. He lives in Brooklyn, NY with his two cats, Laird and Minnow, whom he’s very fond of.

Utkarsh Mahai is a full-stack software engineer at the Department of Energy's Atmospheric Radiation Measurement (ARM) User Facility Data Center. He works on building software, tools, and applications that help scientists and researchers access data, streamline workflows, and focus more on advancing their science.

His work spans the full software development lifecycle, from designing user experiences and building web applications to developing backend services and integrating emerging technologies where they can provide meaningful value. More recently, he has been involved in building agentic systems, modernizing user interfaces in the age of AI, and improving the ways information and context flow through applications.

Outside of work, Utkarsh is interested in conversations around AI ethics, governance, and the broader impact of emerging technologies. He enjoys continuous learning and exploring new ideas, tools, and approaches to solving problems.

Before joining the ARM Data Center, he worked on software products and business processes in the financial technology (fintech) and entertainment industries.

Yuan is a Senior Principal Software Engineer at Red Hat AI. Previously, he has led AI infrastructure and platform teams at various companies. He holds leadership positions in open source communities, including Argo, Kubeflow, KServe, Kubernetes, and CNCF. He's also a maintainer and author of many popular open source projects. In addition, Yuan authored three technical books as well as numerous papers and patents. He's a frequent conference speaker, technical advisor, leader, and mentor at various organizations.