Elliott Ash is Assistant Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics, Switzerland. Prior to joining ETH, Elliott was Assistant Professor of Economics at University of Warwick, and before that a Postdoctoral Research Associate at Princeton University’s Center for the study of Democratic Politics. He received a Ph.D. in economics and J.D. from Columbia University, a B.A. in economics, government, and philosophy from University of Texas at Austin, and an LL.M. in international criminal law from University of Amsterdam.

Niels is a machine learning engineer and core maintainer of Flyte, an open source ML orchestration tool and author and maintainer of Pandera, a data testing tool for dataframes.

He has a Masters in Public Health with a specialization in sociomedical science and public health informatics, and prior to that a background in developmental biology and immunology.

His research interests include reinforcement learning, AutoML, creative machine learning, and fairness, accountability, and transparency in automated systems. He enjoys developing open source tools for improving data science and machine learning practice.

Genevieve Buckley is a scientist and programmer based in Melbourne Australia. She builds software tools for scientific discovery. Her interests include deep learning, automated analysis, and contributing to open source projects. She has a wealth of professional experience with image processing and analysis, spanning x-ray imaging, fluorescence microscopy, and electron beam microscopy. She is a maintainer for the dask-image project, and the napari image viewer.

Colin Carroll is a software engineer at Google Research. In this role he focuses Bayesian computation and research, and contributes to a number of open source libraries, including TensorFlow Probability, PyMC3, and ArviZ. He received his PhD in mathematics from Rice University, where he researched geometric measure theory.

Thomas Chen is a student researcher whose primary interests lie in machine learning and computer vision. He serves on the U.S. Technology Policy Committee of the Association for Computing Machinery. As much of his work lies at the nexus of artificial intelligence and earth science, he is also an active early-career scientist member of the European Geosciences Union and the American Geophysical Union. He enjoys using the Python programming language to conduct research that has real-world impacts. Previously, Thomas has presented work at a number of conferences, workshops, and meetings, from NeurIPS workshops, to Applied Machine Learning Days, to the Open Data Science Conference, to Machine Learning Week Europe.

Brendan is an expert in data science and geospatial technology and is Co-founder and Principal at makepath, a spatial data science firm in Austin, Texas. With over 15 years of experience in GIS, he has worked with The Nature Conservancy, World Resources Institute, New Forests, World Wildlife Fund, NASA, and The Bill and Melinda Gates Foundation. Brendan is actively involved in the PyData community and is a core developer for several open source libraries including Datashader and Bokeh. He recently created the Xarray-Spatial and mapshader libraries for large scale spatial analysis.

Andrei Cozma is an engineering manager at ADI, supporting the design and development of system-level reference designs. He holds a B.S. degree in industrial automation and informatics and a Ph.D. in electronics and telecommunications. He has been involved in the design and development of projects from different industry fields such as motor control, industrial automation, software-defined radio and machine vision.

Nataraj has 21 years of industry experience in developing the vision, strategy and execution of analytic capabilities in finance and pharmaceutical domains. Experience at IBM, UBS Investment Bank, UBS Wealth Management, Purdue Pharma, Philip Morris. He was the core architect of the RWE and Rx Data Analytics solution at Purdue Pharma which was spun out into a new startup, RxDataScience with over $ 3.5M in seed funding. He currently serves as the VP of Advanced Analytics at the firm.

Nataraj is a published author of multiple books on data science, journal articles and research papers in commercial market research, health outcomes, epidemiology and RWE Analytics. He has been a presenter, keynote speaker and Chairperson at over 20+ machine learning and AI-related healthcare conferences in US, Europe and Asia.

Bradley Dice is a scientist and software developer researching nano-scale materials via computational simulations. He is currently a PhD candidate in Physics and Scientific Computing at the University of Michigan in Ann Arbor. He is a member of the Glotzer Group, a National Science Foundation Graduate Research Fellow, and a MolSSI Software Fellow.

James Evans is Professor of Sociology, Director of Knowledge Lab, and Faculty Director of Computational Social Science at the University of Chicago. He is also an External Professor at the Santa Fe Institute. His research uses large-scale data, machine learning, and generative models to understand how collectives think and what they know. This involves inquiry into the emergence of ideas, shared patterns of reasoning, and processes of attention, communication, agreement, and certainty. He is especially interested in innovation—how new ideas and practices emerge—and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery.

Thomas J. Fan is a Senior Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library. He leads the scikit-learn project by designing the project’s roadmap, giving feedback to contributors, and implementing new features. Fan also is a maintainer for skorch, a scikit-learn compatible library that wraps PyTorch. He has a Masters in Mathematics from NYU and a Masters in Physics from Stony Brook University. During his academic studies, Fan researched quantum computation and condensed matter physics.

Ralf has been deeply involved in the SciPy and PyData communities for over a decade. He is a maintainer of NumPy, SciPy and data-apis.org, and has contributed widely throughout the SciPy ecosystem. Ralf is currently the SciPy Steering Council Chair, and he served on the NumFOCUS Board of Directors from 2012-2018.

Ralf co-directs Quansight Labs, which consists of developers, community managers, designers, and documentation writers who build open-source technology and grow open-source communities around data science and scientific computing projects. Previously Ralf has worked in industrial R&D, on topics as diverse as MRI, lithography and forestry.

I obtained my PhD in Mechanical Engineering from the University of Minnesota. After six years in industry working as a design engineer at Seagate Technology, I transitioned to academia and am currently an Associate Professor at the Duluth Campus of the University of Minnesota. Throughout my career, I've benefited from using the tools provided by the SciPy ecosystem and have taught my students these tools as well. More recently, I've made contributions to the SymPy, Pyodide, and CadQuery Python projects. My recent research efforts involve leveraging the scientific Python toolset in the browser using Pyodide to create powerful engineering tools that can be run anywhere.

Predrag is a system architect at Kensho in Boston, working on data infrastructure and developer productivity tools. As part of adopting Python type hints in one of Kensho's large open-source projects, Predrag built typing_copilot to address the pain points maintainers experienced in the process. In addition to high-performance systems, he also loves to experiment with new compiler and database technologies. Outside of work, Predrag enjoys playing ice hockey and board games.

Genetics and genomics Ph.D. student in Eric T. Wang's lab at the University of Florida Genetics Institute. Interested in subcellular RNA localization and repeat expansion diseases. Developing quantitative methods for analysis of single molecule RNA imaging by fluorescence microscopy. Applying Markovian stochastic models to study motion of RNAs in diverse cellular environments.

Lauren Klein is an associate professor in the departments of English and Quantitative Theory & Methods at Emory University, where she also directs the Digital Humanities Lab. Before moving to Emory, she taught in the School of Literature, Media, and Communication at Georgia Tech. Klein works at the intersection of digital humanities, data science, and early American literature, with a research focus on issues of gender and race. She is the author of An Archive of Taste: Race and Eating in the Early United States (University of Minnesota Press, 2020) and, with Catherine D’Ignazio, Data Feminism (MIT Press, 2020). With Matthew K. Gold, she edits Debates in the Digital Humanities, a hybrid print-digital publication stream that explores debates in the field as they emerge. Her work has appeared in leading humanities journals including PMLA, American Literature, and American Quarterly; and at technical conferences including NACCL, EMNLP, and IEEE VIS. Her work has been supported by grants from the NEH and the Mellon Foundation.

Zoufiné Lauer-Baré obtained his PhD in applied mathematics in 2014 at the TU Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany.
Zoufiné Lauer-Baré worked two years in the data and workflow management in the Crash test simulation context in Ingolstadt, Germany.
Currently, he works as simulation engineer in the development of electro hydraulic valves at Hilite International, using analytical, numerical and machine learning techniques in Nürtingen, Germany.
Zoufiné is also an external lecturer at the Baden Württemberg Corporate State University, where he teaches programming with python and foundations of mathematics for data science students, as well as the Finite Element Method for engineering students.

Sean Law is a senior applied scientific researcher and lead data scientist currently working with a multi-talented R&D team at Charles Schwab. He has experience producing cutting edge methodologies, building high-performance predictive models, and developing rapid prototypes. Additionally, he is one of the co-organizers of PyData Ann Arbor and is also the creator and core maintainer of STUMPY, a powerful and scalable open source Python library that can be used for a variety of time series data mining tasks.

Shayan Mousavi is a Materials Science and Engineering Ph.D. student at McMaster University. He holds a B.Sc. degree in Materials Science and Engineering from the Sharif University of Technology with a secondary concentration in Theoretical Mathematics. In his research, he investigates the photonic and optoelectronic properties of materials using various electron microscopy and spectroscopy techniques conducted at the Canadian center for electron microscopy (CCEM) and the Canadian light sources synchrotron facility (CLS).
He implements a wide range of spectral analysis methods including machine learning- and deep learning-based techniques to refine, extract, and generate information from spectral data.

Dan Nechita is a software engineer at ADI, working on a software library for interfacing with Time-of-Flight cameras. He holds a bachelor’s degree in electronics from Technical University of Cluj-Napoca.

Laura K. Nelson is a scholar of social movements and culture, computational sociologist, automated text analysis methodologist, and an open source, open science, reproducible research enthusiast. She is currently an assistant professor of sociology at Northeastern University, where she is a faculty affiliate at the Network Science Institute, she is core faculty at NULab for Texts, Maps, and Networks, and serves on the executive committee of the Women's, Gender, and Sexuality Studies Program. She has published or has work forthcoming in Sociological Methods and Research, Gender & Society, Mobilization, Poetics, and the American Journal of Sociology, among other outlets. She will soon be joining the sociology department at the University of British Columbia as an assistant professor.

Robert Nishihara is one of the creators of Ray, a distributed system for scaling Python and machine learning applications. He is one of the co-founders and CEO of Anyscale, which is the company behind Ray. He did his PhD in machine learning and distributed systems in the computer science department at UC Berkeley. Before that, he majored in math at Harvard.

Baudouin Raoult is currently Principal Software Architect. He joined the ECMWF in 1989. Since then, he has been responsible for the architecture and the development of ECMWF meteorological archive system, a multi-petabyte repository of meteorological data. He has also led several key ECMWF software developments, including the implementation of the Copernicus Climate Data Store, a cloud-based distributed climate data repository that also provides users with Python based analytics tools.

Dr Iza Romanowska
Fellow at the Aarhus Institute of Advances Studies - AIAS, Denmark
Formerly at the Barcelona Supercomputing Center, Spain

Iza Romanowska a complexity scientist working on the interface between social sciences and computer science having originally trained and worked as a prehistoric archaeologist before switching to computer-based research. She specialises in agent-based modelling - a simulation technique she uses for various research questions, from mobility in prehistoric cities, the first Out-of-Africa human dispersal, to large-scale economic interactions across the Roman Meditteranean and real-time pedestrian flows in modern sports venues.

Karla is a Senior Research Software Development Engineer in the Gray Systems Lab (GSL) at Microsoft. She finished her PhD in Computer Science at the University of Maryland, College Park in 2015.

After graduating, Karla spent 3 years as a research scientist at Intel Labs and joined Microsoft in 2018. Her research interests are broad, and generally enjoys scalability and performance challenges related to systems infrastructure.

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, and plubmum for Python. He is an admin of Scikit-HEP, and a lead
designer on boost-histogram, hist, UHI, vector, Particle, and DecayLanguage
packages there. He is also the lead author of the Scikit-HEP developer pages
and scikit-hep/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.

Adam is located at Argonne National Laboratory and works as the instrument operations manager for the Department of Energy Atmospheric Radiation Measurement (ARM) user facility. He has a M.S. in Atmospheric Science from the University of North Dakota where his experience in computer programming first start, moving on to positions as a systems engineer and a data analyst with ARM for eight years with ARM's. Data Quality Office before coming into the current position. His past experience in programming languages run the gamut from Fortran and C to paid programming platforms before recently converting to Python and the open source process.

Rollin is a Data Architect and Computational Systems Engineer at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory in Berkeley, California. The last 5+ years he has helped hundreds of users on three different supercomputers use Python and Jupyter effectively to make discoveries in all areas of science supported by the US Department of Energy's Office of Science. He has led the NERSC Application Readiness Program for Data, and is the chief architect of NERSC's Jupyter service. He likes making supercomputing more accessible to scientists, and helping them succeed with Python.

Melvin Wevers is an Assistant Professor in Digital History at the University of Amsterdam. His research interests include computational historical methods, visual culture, advertisements, and historical newspapers. He has published on gender bias in historical newspapers, conceptual history and word embeddings, computer vision for historical images, and more recently, on the agency of computer vision models.