SciPy 2025

Dr. Adam Symington is a geospatial data scientist, currently working for Synmax. Adam has published a book on geospatial data visualisation, “PythonMaps, Geospatial Visualization with Python” and runs the PythonMaps project, a project designed to spread the love of geospatial data through eye-catching visualisation. Prior to his career in data science Adam was a computational materials scientist at the University of Bath in England where he used machine learning and other statistical techniques to predict the properties of materials. During Adams time in academia, he taught Python programming, developed a Python programming course and published open source software.

Akshay Agrawal is currently building marimo, a new kind of reactive notebook for Python that is reproducible, git-friendly (stored as Python files), executable as a script, and deployable as an app.

He is both a researcher, focusing on machine learning and optimization, and an engineer, having contributed to several open source projects, including TensorFlow during his time at Google, and CVXPY, of which he is a maintainer. He holds a PhD from Stanford University, where he was advised by Stephen Boyd, as well as a BS and MS in computer science from Stanford.

Albert Steppi (@steppi) is a Senior Software Engineer at Quansight Labs and a maintainer of the SciPy library. Previously he worked as a Machine Learning Scientist at Lendbuzz and before that as a Scientific Software Developer in the Labroratory of Systems Pharmacology at Harvard Medical School. He earned a PhD in Statistics from Florida State University in 2018 with research focusing on network aware bioinformatics analysis and biomedical text mining. He is broadly interested in numerical mathematics and scientific and statistical computing.

I am an engineer and software developer focused on methods and analysis tools for neuroscience research, especially for extracellular electrophysiology. I am passionate about science, software, and engineering, and my mission is to support neuroscientists and facilitate their research efforts by providing state-of-the-art analysis methods and software tools. Among these, I am the core developer of several open-source scientific tools, including SpikeInterface, a widely used software framework to unify and simplify the analysis of extracellular electrophysiology data.

In March 2022, I joined the Allen Institute for Neural Dynamics team as an electrophysiology pipeline development engineer consultant, with the goal of building open-source and computationally efficient processing pipelines to analyze large amounts of electrophysiological data. Since July 2020, I have been working part-time at CatalystNeuro, a consulting company with the mission of facilitating collaborations in neuroscience and standardizing data analysis and data storage solutions.

Previously, I was a Postdoctoral Fellow at the Bio Engineering Lab at ETH, working on multimodal approaches to probe neural activity and to construct detailed biophysical models. Before that I was at the Center for Integrated Neuroplasticity CINPLA, at the University of Oslo, where I received my PhD.

Hello, I'm Alex! I am a customer software engineer at MotherDuck and I blog for DuckDB Labs. My background is Industrial and Systems Engineering from Virginia Tech, but I've decided I prefer working with data!

I joined MotherDuck 2 years ago after 9 years at Intel. I started at Intel as an industrial engineer, later became a technical analyst, and then jumped into a data scientist role. Back in 2020 I discovered DuckDB while building an internal self service analytics platform and became one of DuckDB's biggest Twitter fans! I have been diving deeper into Duck-themed databases ever since.

Hello! My name is Alex Kaszynski and I'm a
software developer and aerospace engineer, as well as the co-creator of
PyVista and the creator of
PyAnsys.

I'm an American living in the United States working for AFRL and Pasteur Labs & ISI.

Besides coding, I also enjoys presenting and demoing Python libraries, especially 3D
visualization, particularly in its application to
CAE and automation. You can also find some blog articles I've written at
DEV.to.

Alex is an Associate Professor in Geoinformatics and a Distributed Spatial Systems Researcher with many years of experience in geospatial data management and web- and cloud-based geoprocessing with a particular focus on land use, soils, hydrology, hydrogeology and water quality data. His interests include DIscrete Global Grid Systems (DGGS), OGC standards and web-services for environmental and geo-scientific data sharing, modelling workflows and interactive geo-scientific visualisation. He is also the European co-chair of the OGC DGGS working group.

Allison is a Developer Advocate for GPU-accelerated AI APIs, libraries, and tools at NVIDIA, with a specialization in advanced data science techniques and large language models (LLMs) . She brings over eight years of hands-on experience as a data scientist, focusing on managing and delivering end-to-end data science solutions. Her academic background includes a strong emphasis on data science, natural language processing (NLP), and generative AI. Allison holds a master’s degree in Applied Statistics from Cornell University and a master’s degree in Computer Science from San Francisco Bay University.

As a Scientific Engineering Associate at Lawrence Berkeley National Laboratory, Ana conducts multidisciplinary research focused on the development of innovative solutions, including tools to accelerate jet fuel research or autonomously design semantic models and data infrastructure for buildings. Ana enjoys using machine learning and data science to discover complex patterns and ultimately advance scientific research.

Angus Hollands is an Open Source Applications Engineer at 2i2c. He was previously a post-doctoral researcher in the Computational High Energy Physics group at Princeton University. He has a long-standing history of working collaboratively in open source projects, such as Executable Books, Jupyter, scikit-hep, and Blender. He is motivated by open-source, open-science, and the FAIR principles to build a more accessible, empowering future for scientific research and publication. His scientific background is in nuclear structure, in which he studied a PhD at the University of Birmingham.

Hi/Him.

Research Investigator and Data Scientist at Celanese Corporation (former DuPont)
PhD, Chemical Engineering, Graduate Minor in Statistical Machine Learning

Axel Sirota is an experienced AI leader, engineer, educator and consultant for global technology organizations.

For close to 15 years, Axel has been on the forefront of AI. He founded and grew a successful training consultancy, teaching AI, GenAI, and Python to Fortune 500 companies such as Intuit,
Salesforce, Barclays, Netflix, Apple, and Yahoo.
Axel has been a keynote speaker throughout South America and is known as one of the leading voices on AI Safety and AI technologies. He is eager to work with and support leaders around the world make thoughtful decisions around AI policy and how it impacts society.

Axel’s passion for education is evident in his 50+ published online courses across Pluralsight, O’Reilly Media, and LinkedIn Learning.

Axel received his Master’s Degree in Mathematical Sciences - Probability and Statistics from Universidad de Buenos Aires, Buenos Aires.

I did my undergrad at Cal Poly San Luis Obispo, where I sought out a broad range of coursework in theoretical and applied disciplines and left with a Bachelors of Science degree in both physics and mathematics. I also worked for years teaching these subjects working both for university-run learning centers and as a private tutor. As a professional, I have honed my skills as a scientific python developer, building software tools to enable more rapid scientific research in Machine Learning, Quantum Sensing, and more recently Quantum Circuit Simulation.

I am the lead developer for the the Rydberg Interactive Quantum Module (RydIQule), an open-source tool for research labs to model a broad range of Quantum Sensing experiments. Currently, my interests include quantum sensing and information (including understanding its limitations), software tools for scientific modelling, and science education.

Brodie Vidrine is a software engineer working for NOAA's National Center for Environmental Information. Before working to modernize government software, Brodie spent 17 years writing code for Ascend Math, an online Math tutorial program.

Bryce Adelstein Lelbach has spent over a decade developing programming languages, compilers, and software libraries. He is a Principal Architect at NVIDIA, where he leads programming language efforts and drives the technical roadmap for NVIDIA's compute compilers and libraries. Bryce is one of the leaders of the C++ community. He has served as chair of INCITS/PL22, the US standards committee for programming languages and the Standard C++ Library Evolution group. Bryce served as the program chair for the C++Now and CppCon conferences for many years. On the C++ Committee, he has personally worked on concurrency primitives, parallel algorithms, executors, and multidimensional arrays. He is one of the founding developers of the HPX parallel runtime system. Outside of work, Bryce is passionate about airplanes and watches.

Python and Spyder core developer, specializing in docs, infra, and UI. Python Docs Team and PEP Editor. Star✦Fleet Commander. Former NASA-funded ML researcher.

I’m a data scientist and AI engineer with 10+ years of experience across academic research and industry, building GenAI and machine learning solutions for research labs, startups, and Fortune 500 companies. I’m also a passionate educator, contributing to data training programs as a professor and consultant, and an active open-source contributor and speaker at conferences like PyData.

I got involved with Spyder in 2010 as a volunteer, and became its maintainer in 2013. I worked for Anaconda from 2015 to 2017. The first two years there I created conda packages for Qt, VTK, Boost, Pandoc, Graphviz, CMake, etc (most of my recipes were used by the Conda-forge team when the project started). In my final year I led a team of three developers working on Spyder. I joined Quansight in 2018 and left in 2022. There I managed a team of five developers (hired by Quansight), also in charge of maintaining Spyder. Since then I've been working on Spyder full time thanks to a CZI grant awarded to the project at the end of 2022.

Carol Willing is a three-time Python Steering Council member, a Python Core Developer, PSF Fellow, and a Project Jupyter core contributor. In 2019, she was awarded the Frank Willison Award for technical and community contributions to Python. As part of the Jupyter core team, Carol was awarded the 2017 ACM Software System Award for Project Jupyter's lasting influence. She's also a leader in open science and open-source governance serving on Quansight Labs Advisory Board and the CZI Open Science Advisory Board. She's driven to make open science accessible through open tools and learning materials. She recently served as Noteable's VP of Engineering,

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 previously worked in Air Quality, where he produced tools to analyse air pollution data, and has a PhD in Oceanography.

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

Associate Research Scientist in the MILES (Machine Integration and Learning for Earth Systems) group at NSF NCAR.

Charlotte Wickham is a Developer Educator at Posit, where she focuses on Quarto. Before Posit, she taught Statistics and Data Science at Oregon State University.

Chirag Shah is an Environmental Data Science/ full-stack software engineer with a keen interest in climate science and cutting-edge technologies. As an integral part of both the Atmospheric Radiation Measurement Facility (ARM) Data Center and the U.S. Geological Survey (USGS) Core Science Systems group, he works closely with product owners, scientists, and researchers to design and develop next-generation tools for managing, analyzing, and visualizing scientific data. These tools are aimed at advancing research in Earth, Climate, and Environmental sciences.

Chirag has a keen interest in various areas of software development, such as data management, data analytics, artificial intelligence, machine learning, the Internet of Things (IoT), and machine-to-machine communication. Chirag aims to harness technology's power to solve real-world challenges and is committed to staying at the forefront of technological advancements.

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.

Daniel Alejandro Coll Tejeda is a Software Engineer and Cloud Researcher at the Cloud and Distributed Systems Lab (URV), with extensive experience in developing and researching solutions across major cloud platforms including AWS, IBM Cloud, and GCP. His work has involved leveraging powerful tools like Lithops for serverless computing and Kubernetes for container orchestration, focusing on their practical application to solve complex scientific and data processing challenges. Daniel is passionate about pushing the boundaries of cloud technology and holds a degree in Computer Engineering from Universitat Rovira i Virgili.

Building on this deep cloud expertise, Daniel is currently one of the main creators of PyRun, a platform designed to democratize scalable cloud computing for Python users. PyRun simplifies running Python workloads—from data processing to AI—on your own cloud account by automating infrastructure management, runtime configuration, and seamlessly integrating frameworks like Lithops and Dask.

Debarshi Datta, PhD is an Assistant Professor in Data Science at the Christine E. Lynn College of Nursing, Florida Atlantic University. He completed PhD in Experimental Psychology at the Charles E. Schmidt College of Science, Florida Atlantic University. Dr. Datta has experience developing AI-driven decision support systems in healthcare data, including understanding problem statements, handling disputes, exploratory data analysis, building models, data visualization, and data storytelling. Dr. Datta’s current research focuses on data-driven domains like AI/ML to understand a population-based disease prognosis. His primary research contribution has been finding the severity of the disease, decision-making for developing a model that comprehends the most significant features predicting mortality, and severity of disease utilizing traditional AI/ML techniques such as decision trees, random forest classifier, XGB boost, and deep learning. In other research, he is building a model to identify early prediction of Dementia. Dr. Datta received many intramural grants, including Early Prediction of Alzheimer's Disease and Related Dementias on Preclinical Assessment Data using Machine Learning tools, Seed Funding from Smart Health for COVID-19 research, NSF I-Corps Customer Discovery Funding, ALL of US Institutional Champion Award, among many others.

Deepak Cherian is an Xarray maintainer and Forward Engineer at Earthmover. Previously he was an oceanographer at the National Center for Atmospheric Research. He helps build and maintain many parts of the scientific Python ecosystem, includinh Xarray, dask, zarr and related projects.

Deepyaman is a software engineer at Dagster Labs. He joined from Voltron Data, where he was a Senior Staff Software Engineer on the Ibis team. Before their acquisition by Voltron Data, he was a Founding Machine Learning Engineer at Claypot AI, working on their real-time feature engineering platform. Prior to that, he led data engineering teams and asset development across a range of industries at QuantumBlack, AI by McKinsey.

Deepyaman is passionate about building and contributing to the broader open-source data ecosystem. Outside of his day job, he helps maintain Kedro, an open-source Python framework for building production-ready data science pipelines.

Denis Leshchev is Senior Application Engineer, NVIDIA. Dr. Leshchev joined NVIDIA in 2024 and works as an application engineer for computational instruments where he focuses on customer adoption of hardware and software platforms targeting real-time AI, autonomous instruments, and tying high speed sensor I/O to GPU-accelerated compute. Dr. Leshchev has an extensive background in building scientific instrumentation, data acquisition and control systems, as well as data processing pipelines. He holds a PhD in Physics from Universite Grenoble Alpes (Grenoble, France).

I'm currently a PhD student working on timelapse microscopy data analysis, and I've been learning and working with Python for almost a decade now! I love the open-source community and everything it has to offer the world, and I've been lucky enough to make my own contributions to the community as a core developer for napari - an n-dimensional image viewer written entirely in Python. I'm passionate about making coding more accessible for scientists who want to make their own lives easier, and I love teaching everything from the fundamentals to the nitty gritty.

Elliot Marx is one of the co-founders of Chalk. He started his career at Affirm, where he built the early risk and credit data infrastructure system (the inspiration for Chalk). He then co-founded Haven Money, which Credit Karma acquired to power its banking products. He holds a B.S. and M.S. in Computer Science from Stanford University.

Emily Dorne is a lead data scientist at DrivenData where she develops machine learning models for social impact. Her expertise lies in classifying animals in camera trap videos to support conservationists, identifying harmful algal blooms to support water quality managers, and helping data scientists consider the ethical implications of their work. She is passionate about using data for social good and has previously worked at the Gates Foundation, Stanford Center for International Development, and the Brookings Institution.

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.

Erik Welch is a senior system software engineer on the RAPIDS cuGraph team at NVIDIA. He has 20 years' experience using Python as a scientist, engineer, and open-source developer on a wide range of data and high-performance computing problems. He primarily works on nx-cugraph, an accelerated backend to NetworkX, and is a primary maintainer of the popular toolz library.

PhD in Computer Science focused in bioimage understanding through computational intelligence methods.

I currently work as Systems Analyst in the Research IT department of the The Jackson Laboratory, where my main role is assisting people with integration of machine learning methods in their image analysis pipelines.

Postdoctoral fellow at Stanford / SLAC National Laboratory with expertise in ab-initio computational modeling, surface science simulations, and machine learning techniques. Passionate about advancing interdisciplinary approaches to materials science and energy conversion, leveraging principles from conventional algorithms and Artificial Intelligence frameworks. Excited about developing new quantum chemistry methods to model materials properties and validate them through collaborative experimental work.

I am a physics PhD student at University of Illinois Chicago (UIC) and part of the CMS group there. My areas of research are developing intuitive python tools for accelerating high energy physics analyses and performing measuring entanglement of top quark systems at the LHC.

Gajendra Deshpande is a distinguished professional with an M.Tech. in Computer Science and Engineering from Visvesvaraya Technological University, Belagavi, along with a PG Diploma in Cyber Law and Cyber Forensics from the National Law School of India University, Bengaluru. He founded and currently manages Theta Dynamics Private Limited in Belagavi.

Deshpande is renowned for his extensive contribution to the tech community, having delivered over 100 talks and conducted more than 25 workshops at various esteemed international conferences, including JuliaCon 2023 at MIT, USA, EuroPython Ireland, PyCon MEA Dubai, PyCon APAC Japan, PyData Global, and many more across Europe, Asia and the USA. His expertise has guided teams to victory in the Smart India Hackathon and National Security Hackathon five times.

As an active member of PyCon India, Deshpande has played crucial roles, such as leading the Program Committee in 2021 and serving as the Mentorship Lead in 2023. He has been instrumental in organizing FOSSCon India 2019 and BelPy conferences. His commitment extends to various professional bodies, serving as the Founding chair of Belagavi ACM chapter, Served as Vice Chair of the IEEE Young Professionals Affinity Group, Bangalore Section, and an Execom Member of IEEE Bangalore section. He is a Fellow Member of the Royal Statistical Society UK and maintains memberships with OWASP, the British Computer Society, and Senior member of ACM. Deshpande has significantly contributed to Python, Julia, and FOSS Conferences by reviewing proposals, mentoring speakers, engaging in discussions, and organizing events.

Guen is a software engineer at MotherDuck on the Ecosystems team. Guen has broad experience with software engineering, data engineering and data science with Python in the context of scientific data acquisition, analysis and computation for experimental physics. She has given introductory talks and workshops on SQL and DuckDB at various conferences, hackathons and events.

Heberto Mayorquin
Heberto Mayorquin holds a BSc in Physics, an MSc in Complexity Science, and a PhD in Computational Neuroscience. After a brief stint in the private sector optimizing SQL queries, he returned to science by joining CatalystNeuro. At CatalystNeuro, he helps neuroscience labs standardize their data—from extracting information buried in proprietary binary formats to streamlining metadata documentation and optimizing data layouts for long-term cloud storage. Within the organization, he serves as the lead maintainer of NeuroConv and is also a maintainer of SpikeInterface. His focus is on developing open-source tools and workflows that make it easier for researchers to share and reuse their own data, as he believes that open collaboration is a catalyst for scientific progress.

Henry Schreiner is a Computational Physicist / Research Software Engineer in High Energy Physics at Princeton University. He specializes in the interface between high-performance compiled codes and interactive computation in Python, in software distribution, and in interface design. He has previously worked on computational cosmic-ray tomography for archaeology and high performance GPU model fitting. He is currently a member of the IRIS-HEP project, developing tools for the next era of the Large Hadron Collider (LHC).

He is a maintainer/core developer for pypa/build, scikit-build, cibuildwheel, pybind11, meson-python, nox, and plumbum for Python. He is an admin of Scikit-HEP, and a lead designer on boost-histogram, hist, UHI, vector, uproot-browser, Particle, and DecayLanguage packages there. He is also the lead author of the Scientific-Python Development guide and Scientific-Python/cookie. He is the primary author of CLI11, a C++ library used by Microsoft terminal and many others. He is also the lead web developer for IRIS-HEP. He is also the author of Modern CMake and a variety of CMake, GPU, and Python training courses and classes.

I recently completed my PhD, during which I built software to manage the acquisition of combined epifluorescence and single-cell Raman spectroscopy time-lapse data. I extensively used Xarray and Zarr in both the data acquisition and analysis of this project. I have also presented multiple workshops on using Python for scientific data analysis and on using the SciPy stack (including Xarray) for microscopy data.

During graduate school, I discovered a passion for contributing to open-source scientific projects, which led me to my current role as a Xarray Community Developer at Earthmover. In this role, I am focused on improving Xarray for use cases in biological research.

Inessa is building bridges between people, open science, and open source software. She is passionate about making Python accessible for learners at all levels and has led numerous newcomer sprints, study groups, and tutorials. Inessa currently serves on the NumPy Steering Council and PyOpenSci Advisory Board. In her role as Open Source Program Manager at OpenTeams, Inessa has launched and actively supports several educational initiatives focused on widening the open source contributor pipeline. She is perpetually fascinated by incentive design, collaborative intelligence, and jazz.

Irina Demeshko is a senior software engineer at NVIDIA working on cuNumeric and Legate projects. Before NVIDIA, Irina was a research scientist and team leader of the Co-Design team at the Los Alamos National Laboratory. Her work and research interests are in the area of new HPC technologies and programming models.

Jacob Tomlinson is a senior software engineer at NVIDIA. His work involves maintaining open source projects including RAPIDS and Dask. He also tinkers with kr8s in his spare time. He lives in Exeter, UK.

Jay Qi is a lead data scientist at DrivenData where he helps mission-driven organizations and institutions leverage machine learning, data science, and data engineering for social impact. He has worked on applying machine learning to a wide range of scientific contexts, including hydrological modeling, spacecraft dynamics, and wildlife conservation. Before DrivenData, Jay modeled failures of industrial machines using sensor data at Uptake, and he has a background in aerospace engineering and computational fluid dynamics. Jay is also an active open source software maintainer and contributor, working on projects including cookiecutter-data-science, cloudpathlib, and erdantic.

I am a postdoctoral researcher at the University of Wisconsin-Madison, affiliated with the Data Science Institute and the Department of Physics. My research focuses on the intersection of modern statistical tools and high-energy physics.

Senior research engineer at CNRS, specialize in data analysis across astrophysics and oceanography. With extensive expertise in AI, scattering transform, and statistical modeling, I contribute to cutting-edge projects such as Planck’s SRoll algorithm and the development of the FOSCAT library on PyPi. I currently lead projects integrating deep learning and dimensionality reduction techniques for Earth and space science applications, including sea ice analysis, galaxy mapping and turbulence modeling.

Jim Kitchen is the lead engineer on the team that built the Anaconda Toolbox and Anaconda Code add-ins for Excel

Joe Cheng is CTO and first employee at Posit (formerly known as RStudio) and the creator of Shiny, a reactive web framework for creating data and AI applications using Python or R. He has been writing and maintaining open source software at the intersection of data analysis and the web for over 15 years.

Physicist at CERN with a dedication focus on machine learning, statistical tools and software engineering.

Dr. Katrina Riehl is a Principal Technical Product Manager at NVIDIA supporting CUDA and Python educational initiatives. 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 science 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, serving as a NumFOCUS Board member 2018-2024 and President 2021-2024. She continues to serve the NumFOCUS community on the NumFOCUS Advisory Council.

I am a process engineer and data scientist with over six years of experience in statistical data analysis, engineering, and machine learning. I’m a Microsoft-certified data scientist and a Databricks-certified data engineer. I work in the Equipment Intelligence group at Lam Research, independently research LLM-based applications and machine learning for environmental monitoring.

I built and maintain Fast Dash, an open-source Python library that transforms Python functions into interactive web applications.

Kevin Lee is a senior technical content developer on the Deep Learning Institute Team at NVIDIA. Kevin’s work focuses on raising awareness and driving adoption for GPU-accelerated technologies by creating developer focused hands-on training with an emphasis on Data Science, Computer Vision, and Large Language Models. Prior to NVIDIA, Kevin led a risk analytics team at Morgan Stanley and taught Data Science and Machine Learning at the University of California, Berkeley.

Leland McInnes is a researcher at the Tutte Institute for Mathematics and Computing. He is the author of a number of open source tools for data analysis and machine learning, including UMAP, HDBSCAN, PyNNDescent, EVōC, DataMapPlot and Toponymy.

Lorena A. Barba is professor of mechanical and aerospace engineering at the George Washington University in Washington, DC. Her research interests include computational fluid dynamics, high-performance computing, and computational biophysics. An international leader in computational science and engineering, she is also a long-standing advocate of open source software for science and education, and is well known for her courses and open educational resources. Barba served (2014–2021) in the Board of Directors for NumFOCUS, a US public charity that supports and promotes world-class open-source scientific software. She is an expert in research reproducibility, and was a member of the National Academies study committee on Reproducibility and Replicability in Science. She served as Reproducibility Chair for the SC19 (Supercomputing) Conference, is Editor-in-Chief of IEEE Computing in Science & Engineering, was founding editor and Associate EiC (2016–2021) for the Journal of Open Source Software, and is EiC of The Journal of Open Source Education. She was General Chair of the global JupyterCon 2020 and was named Jupyter Distinguished Contributor in 2020.

Luigi Cruz is a computer engineer working as a staff engineer at the SETI Institute. He created the CUDA-accelerated digital signal processing backend called BLADE currently in use at the Allen Telescope Array (ATA) and Very Large Array (VLA) for beam forming and high-spectral resolution observations. Luigi is also the maintainer of multiple open-source projects like the PiSDR, an SDR-specialized Raspberry Pi image, CyberEther, a heterogenous accelerated signal visualization library, and Radio Core, a Python library for demodulating SDR signals using the GPU with the help of CuPy.

Mark is a beamline scientist in the spectroscopy group at the Advanced Photon Source, collaborating closely with visiting researchers to execute cutting edge scientific experiments across a variety of disciplines. His research background emphasizes in-situ measurements, where chemical states are measured inside an operating battery in order to better understand the otherwise inaccessible dynamic processes. Combining high-resolution imaging, spectroscopy, and diffraction provides insights into the local interactions that drive the energy storage in cutting edge battery technologies. This research provides a foundation for future technological development that will deliver faster, more efficient, and safer energy storage solutions.

Mark completed his PhD in the chemistry department at the University of Illinois Chicago with Jordi Cabana, where he studied particle-level dynamics for layered cathodes. During this time, Mark spent a year as a visiting graduate student at the Advanced Photon Source through the U.S. Department of Energy’s Science Graduate Research Program. The result of this collaboration was a new cell for three-dimensional imaging of operating Li-ion batteries. He built upon his graduate research as a postdoctoral appointee in Interfacial Processes group working closely with Tim Fister to include additional 3D imaging in working cells, and high-temperature preparation of cutting edge battery materials. Throughout his work, Mark has written many scientific software packages to aid in data analysis and visualization.

Matt Haberland is an Associate Professor at Cal Poly, San Luis Obispo, and a maintainer of SciPy and NumPy.

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.

Matthew has served on the SciPy Organizing Committee since 2020, with roles as co-chair of the Physics and Astronomy specialized track and co-chair of the Program Committee.

I'm a data science tool builder at Posit, where I work on open source tools for data analysis (like siuba).

Previously, I worked as a consultant building out a data team for Caltrans (and love all things GTFS).

I received a Ph.D. in Cognitive Psychology from Princeton University, and am interested in what drives expert data science performance. This led me to build DataCamp Signal, adaptive tests of data science skill.

PhD in Statistics from the University of Chicago

Previously:
- Head of Data Science at Uptake
- Technical Staff at SentiLink

Currently:
- AI and Data Consultant at AlixPartners

Supply chain security @ Google OSS Security Team

Mihai Maruseac is a member of Google Open Source Security team (GOSST), working on Supply Chain Security, mainly on GUAC. Before joining GOSST, Mihai created the TensorFlow Security team after joining Google, moving from a startup to incorporate Differential Privacy (DP) withing Machine Learning (ML) algorithms. Mihai has a PhD in Differential Privacy from UMass Boston.

Negin Sobhani is a High Performance Computing consultant and computational atmospheric scientist at the National Center for Atmospheric Research (NCAR). She has extensive experience developing and supporting open-source tools and infrastructure that improve the performance and accessibility of Earth System models, bridging the gap between data science, atmospheric science, and software engineering. Her broader work encompasses the development of large-scale distributed training, optimization of resource utilization, and data pipelines across advanced computing environments for geoscience applications.

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 software infrastructure to improve genomic and multi-omic data science, especially in the scientific Python ecosystem.

Noor Aftab is the Global Program Lead at Amazon Web Services (AWS), where she oversees the Strategic Customer Program for Amazon S3, managing some of the largest cloud, data, and analytics workloads globally. With a foundation as a data scientist and extensive experience in cloud technologies, Noor combines technical expertise with strategic leadership to help organizations optimize their data infrastructure and deploy AI-driven solutions at scale.

An advocate for diversity, innovation, and inclusivity in technology, Noor has spoken at 13 global locations, sharing her insights on AI adoption, advancing innovation, and creating equitable pathways for underrepresented groups in tech. Her talks highlight how data and AI can transform industries, empower communities, and foster inclusion across the tech ecosystem.

Noor is the founder of the International Women Economic Council (IWEC), a global platform empowering women through mentorship, networking, and professional development opportunities. By connecting women with industry leaders and fostering career growth, IWEC is dedicated to breaking barriers and promoting gender equality in the workplace.

She actively contributes to the advancement of open science through her involvement with NumFOCUS, PyData, and community-driven initiatives, where she fosters collaboration, inclusivity, and accessibility in data science and AI. Noor also leads initiatives like the IEEE Hour of Power training program, equipping professionals with practical AI and data science skills.

Her contributions to business leadership and women’s empowerment have earned her numerous accolades, including the Australia Alumni Excellence Award and the Asia Pacific HRM Congress Award. Noor’s work has been featured in prominent media outlets such as the BBC, Martha Vineyard Times, and Hindustan Times, highlighting her global impact in advancing diversity and innovation in technology.

GitHub: aftabn81 | Website: www.nooraftab.com

Oleksandr is a PhD student at the University of Illinois Urbana-Champaign working on numerical methods for time-dependent neutron transport.

Patrick is a tech lead on ML for protein optimization at Cradle.bio, and founded much of the open-source scientific JAX ecosystem. He has previously worked as an ML researcher at Google X, held a visiting appointment at Imperial College London, and received a PhD from Oxford on neural differential equations.

Pedro Garcia Lopez is professor of the Computer Engineering and Mathematics Department at the University Rovira i Virgili (Spain).
He leads he “Cloud and Distributed Systems Lab” research group and coordinates large research european projects.
In particular, he leads CloudStars (2023-2027), NearData (2023-2025), CloudSkin (2023-2025),
and he participates as partner in EXTRACT (2023-2025). He also coordinated FP7 CloudSpaces (2013-1015), H2020 IOStack (2015-2017)
and H2020 CloudButton (2019-2022). Pedro Garcia Lopez is one of the main architects and leaders of the Lithops project that was created
in collaboration with IBM in the Cloudbutton.eu project. Pedro is the main author of the "Serverless End Game" and "Dataplug" papers and co-author
of the paper on "Transparent serverless execution of Python multiprocessing applications".

Peter is a napari core developer and a Systems Analyst in the Imaging Applications and Machine Learning team in Research IT at The Jackson Laboratory. In this day job, Peter supports users of open source imaging applications and workflows, both on local devices and on HPC. He also runs workshops to help users ease into the various applications. As a napari core developer, he focuses on user-facing bugs and issues, UI/UX, documentation, etc.

Rowan is the CEO and founder of Curvenote (https://curvenote.com), where we build tools to free science from static PDF documents such that the scientific community can share more interactive, reproducible, and richly-linked scientific content. Curvenote provides an all-in-one publishing platform for researchers, societies and institutes, with a focus on computational research.

Rowan is also on the steering-council for JupyterBook and MyST Markdown, which is part of Project Jupyter and provides widely used open-source tools for authoring and sharing scientific content. Rowan has a Ph.D. in computational geophysics from the University of British Columbia (UBC). While at UBC, Rowan helped start SimPEG (https://simpeg.xyz), a large-scale simulation and parameter estimation package for geophysical processes (electromagnetics, fluid-flow, gravity, etc.), which is used in industry, national labs, and universities globally.

Rowan has won multiple awards for innovative dissemination of research and open-educational resources, including a geoscience modeling application, Visible Geology, that has been used by more than a million geoscience students to interactively explore conceptual geologic models. In his previous role as the VP of Cloud Architecture at Seequent, Rowan ran a large software team working on computational software platforms, visualization tools, and version control systems for geoscientists.

Former robotics engineer now solving package management, so others don't have to experience what I had to go through. I'm a core maintainer of pixi and love sharing our work through talks, podcasts, or videos.

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.
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.

Sanjiban is a Doctoral Student at CERN, affiliated to the University of Manchester. He is researching on optimization strategies for efficient Machine Learning Inference for the High-Luminosity phase of the Large Hadron Collider at CERN within the Next-Gen Triggers Project. Previously, he was a Summer Student at CERN in 2022, and also contributed at CERN-HSF via the Google Summer of Code Program in 2021. In the development of SOFIE, he was particularly involved in the development of the Keras and PyTorch Parser, storage functionalities, machine learning operators based on ONNX standard, Graph Neural Networks support, etc. Moreover, he volunteered as a Mentor for the contributors of Google Summer of Code 2022, and again in 2023, 2024 and 2025, and the CERN Summer Students of 2023 working on CERN’s ROOT Data Analysis Project.

Previously, Sanjiban spoke at PyCon India 2023 about Python interfaces for Meta’s Velox Engine. He also presented a talk on the Velox architecture at PyCon Thailand 2023. He has been contributing to open-source projects on data science and engineering that includes ROOT, Apache Arrow, Substrait, etc.

I’m a software engineer deeply invested in the open-source scientific ecosystem. I love working with OS communities and contributing to various projects. I wear multiple hats in the open-source software world—writing code, creating processes and workflows, steering various committees, organising technical meet-ups and global conferences, and many more.

When I’m not working, you can find me trekking in the Himalayas. Mountains are my second favourite thing, right after computers. I also play video games, watch movies, play football and run marathons.

Sarah has spent most of her career developing technology in the lab, from virtual reality hardware to satellites. She got her PhD in Physics by starting plasma fires with lasers, Python, and Jupyter Notebooks. She has also written tech books for folks of all ages, including ABCs of Engineering and Learn Quantum Computing with Python and Q#. As a Cloud Developer Advocate for Python at Microsoft and a Python Software Foundation Fellow, she finds all kinds of new ways to build and break OSS tools for data science and machine learning. When not at her split ergo keyboard, she loves boating in the Seattle area, laser cutting everything, and playing with her German Shepard, Chewie.

Meteorologist and Software Engineer with a passion for leveraging technology to enhance environmental data analysis and decision-making.

Sean Freeman is an Assistant Professor of Atmospheric and Earth Science at The University of Alabama in Huntsville (UAH), having started that appointment in Spring 2023. Before coming to UAH, he received undergraduate degrees in Computer Science and Meteorology from Florida State University and MS and PhD degrees in Atmospheric Science from Colorado State University. Sean's research interests are primarily in clouds and storms, in particular, understanding the kinds of environments that support cloud development and severe weather. He uses numerical weather modeling and advanced data science tools such as cloud tracking to uncover the basic building blocks of convection from models and observations, as well as new measurements of convective inflows and outflows with drones. He has served as a tobac lead developer since 2021 and has chaired the tobac steering committee since 2023. Outside of work, he enjoys photography, hiking, traveling, baking, curling, and watching college football.

I am a scientific software developer at Brookhaven National Laboratory. I contribute to Bluesky code stack which is a library for experiment control and collection of scientific data and metadata.

Shaurya Agarwal has been tinkering with Data, Cloud technologies, Machine Learning, Data Science and now GenAI for over 21 years. Now a Director with PwC India, Shaurya brings his expertise and experience to solve critical problems across a very wide set of domains.

Data Scientist at Capital One

Stefanie Molin is a software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also a core developer of numpydoc and the author of “Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization,” which is currently in its second edition and has been translated into Korean and Chinese. She holds a bachelor’s of science degree in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

A scientific software developer experienced in Python, modern web development and cloud technology and computing, I've spent many years developing tool, research code and products for scientists and researchers in various fields including earth science, semiconductors and bio-medical research.

I am a Senior Manager in Data Science at Capital One, specializing in machine learning, generative AI, and Retrieval-Augmented Generation (RAG). I am passionate about leveraging AI to solve complex business challenges and drive innovation.

Interested in scientific computing and visualization with computer graphics.
Developer team member of PyVista.
Experience as a speaker:
- PyConJP 2019 speaker "Introduction to FEM Analysis with Python"
- PyConJP 2020 speaker "How to plot unstructured mesh file on Jupyter Notebook"
- SciPy Japan 2020 speaker "Translation Project of Mayavi2 documents"
- PyConJP 2021 speaker "Visualize 3D scientific data in a Pythonic way like Matplotlib"

Thomas J. Fan is a senior machine learning engineer at Union.ai and a maintainer of scikit-learn, an open-source machine learning library for Python. At scikit-learn, he led the development of DataFrame interoperability and GPU support through PyTorch. Previously, Thomas was a Columbia University researcher who worked on improving interoperability between machine learning frameworks and AutoML systems.

I am a postdoctoral researcher at the University of Minnesota studying brain development in the Department of Pediatrics. When I'm not at the lab bench, I spend most of my time supporting other biologists doing my passion, microscopy and bio-image analysis. I learned Python to unify image processing and data analysis and have fallen in love with the open-source community. To this end, I have begun to contribute to the n-dimensional image viewer napari and its plugin ecosystem with my own tool, napari-ndev, intended to (batch) process bioimages from start to finish with no coding necessary. I hope to bring accessible, high-quality, reproducible science to all, regardless of their experience with programming.

Tina Todaka is a research engineer at IFREMER, working in the UMR-LOPS (Laboratoire d’Océanographie Physique et Spatiale). She leads the IAOCEA project, focusing on the hybridization of model, satellite, and in-situ data for oceanography using scattering transform techniques. She also leads the Pangeo-Fish project, a software package leveraging the Pangeo environment to help biologists efficiently compute fish tracks from biologging in-situ data and Earth science datasets.

Her research interests include optimizing scientific computing workflows in oceanography, from high-performance and cloud-based computing to their practical applications in policy decision-making. She actively contributes to open-source geospatial science, developing scalable and reproducible tools for large-scale oceanographic data analysis.

Tom Nicholas is a core developer of Xarray, and the original author of xarray.DataTree. He has made numerous contributions throughout the Pangeo stack, including to VirtualiZarr, Cubed, xGCM, and pint-xarray. He currently works on the open-source Pangeo stack full-time at Earthmover. Prior to that he worked at a non-profit on open-source tools for monitoring carbon dioxide removal, and as a Research Software Engineer in Ryan Abernathey's Climate Data Science Lab at Columbia University. He first started using the open-source scientific python stack during his PhD, when he was studying plasma turbulence in nuclear fusion reactors. He has delivered many Xarray tutorials, including at SciPy 2022, 2023, and 2024.

Tom White is an independent software engineer. His long-term professional interest centres around large-scale distributed storage and processing. Over the last few years he has focused on big data infrastructure for scientists, including GATK, Scanpy, sgkit, and most recently Cubed. In a previous life Tom wrote “Hadoop: the Definitive Guide” published by O’Reilly. He lives in the Brecon Beacons in Wales with his family.

Software Engineer at Oak Ridge National Laboratory, specializing in Generative AI, and machine
learning applications for scientific data processing. Dedicated to designing scalable AI architectures,
collaborating with research teams, and integrating AI-driven solutions to enhance data workflows.

Vyas Ramasubramani is an experienced scientist and open-source developer. He has presented scientific software numerous times, both at SciPy and other academic conferences. He has also presented scientific results at various national conferences. He has been a developer for the cuDF project for four years and currently leads its development along with its various subprojects including cudf.pandas and cudf-polars.

Data Scientist at Capital One

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry Vanishing Gradients, where he explores cutting-edge developments in data science and artificial intelligence.
As a data scientist, educator, evangelist, content marketer, and strategist, Hugo has worked with leading companies in the field. His past roles include Head of Developer Relations at Outerbounds, a company committed to building infrastructure for machine learning applications, and positions at Coiled and DataCamp, where he focused on scaling data science and online education respectively.
Hugo's teaching experience spans from institutions like Yale University and Cold Spring Harbor Laboratory to conferences such as SciPy, PyCon, and ODSC. He has also worked with organizations like Data Carpentry to promote data literacy.
His impact on data science education is significant, having developed over 30 courses on the DataCamp platform that have reached more than 3 million learners worldwide. Hugo also created and hosted the popular weekly data industry podcast DataFramed for two years.
Committed to democratizing data skills and access to data science tools, Hugo advocates for open source software both for individuals and enterprises.

Nate is a Member of Technical Staff at Unitary Foundation working to make quantum computers useful, usable, and accessible. He mostly works on quantum error mitigation tooling, and is passionate about open source and the benefits it provides the scientific and technology ecosystem. In his spare time nate enjoys rock climbing, running, and building community.