SciPy 2023

200 W Cesar Chavez
  • SciPy Welcome Reception
Aaron Meurer

Aaron Meurer is a software engineer at Quansight, where he works on important projects affecting the scientific Python ecosystem including the array API standard, NumPy, and PyTorch. He is also a core maintainer of the SymPy symbolic mathematics library.

  • SymPy Introductory Tutorial
  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Abby Mitchell

Abby joined IBM in 2019 as a full stack web developer before moving to IBM Quantum as a Developer Advocate in March 2021. She is currently working on Qiskit, an Open Source SDK for Quantum Computers. As a primarily self taught developer and quantum enthusiast she is passionate about encouraging people from any background to pursue their interest in technology.

  • Beyond Bits & Qubits: Effective Open Source Community Management in Quantum Computing
Adam Breindel

Adam Breindel is a member of the Anyscale training team and he consults and teaches on large-scale data engineering and AI/machine learning. He has served as technical reviewer for numerous O'Reilly titles covering Ray, Apache Spark, and other topics. Adam's 20 years of engineering experience include numerous startups and large enterprises with projects ranging from AI/ML systems and cluster management to web, mobile, and IoT apps. He holds a BA (Mathematics) from University of Chicago and a MA (Classics) from Brown University. Adam's interests include hiking, literature, and complex adaptive systems.

  • Scalable machine learning workloads with Ray AI Runtime
Alan Braz
  • Open Source Sprints [Kickoff in Room 204]
  • Open Source Sprints [Kickoff in Room 204]
Albert Steppi

Albert Steppi (@steppi) is a Senior Software Engineer at Quansight Labs. He earned a PhD in Statistics from Florida State University in 2018. Albert has been a maintainer of the SciPy library since 2021.

  • Resampling and Monte Carlo Methods in SciPy.stats
Alex Monahan

Hello, I'm Alex! I am a forward deployed software engineer at MotherDuck and I write blogs and docs for the DuckDB Foundation. My background is Industrial and Systems Engineering from Virginia Tech, but I've decided I prefer working in data! I recently joined MotherDuck 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. It was such a perfect fit that we quickly integrated it and I began using it in multiple projects. I also became one of DuckDB's biggest Twitter fans! I have been diving deeper into duck-themed databases ever since.

  • In-Process Analytical Data Management with DuckDB
Alexander Kaszynski

Alex Kaszynski, co-creator of PyVista and creator of the PyAnsys project.

Advocate for all things open source and has contributed to the creation of Ansys’s open source projects at Ansys and PyMAPDL. Enjoys presenting and demoing Python, especially 3D visualization but also its application to CAE and automation.

  • 3D Visualization with PyVista
Alexandre Passos

Currently working at openai, previously at google.

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Allen Downey

Allen Downey is a curriculum designer at the online learning company Brilliant and professor emeritus at Olin College. He is the author of several books related to computer science and data science, including Think Python, Think Stats, and Think Bayes. His blog, Probably Overthinking It, features articles about Bayesian statistics. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.

  • Taming Black Swans: Long-tailed distributions in the natural and engineered world
Amog Kamsetty

Amog is a Senior Software Engineer at Anyscale where works on the Ray open source project building solutions for distributed machine learning workloads including distributed model training and offline inference.

  • Modern compute stack for scaling large AI/ML workloads
Ana Comesana

Ana Comesana is a Scientific Engineering Associate at Lawrence Berkeley National Laboratory. She is a data scientist who conducts applied machine learning research to support projects in a variety of areas, including water treatment, energy management, and bio-jet fuel research. Ana received her B.S. in Mathematics from UC Berkeley.

  • Using Python to accelerate sustainable aviation fuel research and development
Anders Johansson

I am a PhD student in Applied Physics in the group of Boris Kozinsky at Harvard SEAS. My focus is on machine learning interatomic potentials for molecular dynamics simulations, in particular on how to make them fast on modern hardware architecture and large supercompters.

GitHub: @anjohan

  • Allegro and FLARE: Fast and accurate machine learning potentials for extreme-scale simulations
Anderson Banihirwe
  • Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
Andreas Mueller

Andreas Müller is a Principal Research SDE at Microsoft, where he works on the interface of the Data Science ecosystem and cloud infrastructure.
He previously held positions as Associate Research Scientist at the Columbia Data Science Institute and as a Research Engineer at the NYU Center for Data Science.
He is one of the core developers of the scikit-learn machine learning library, a member of the scikit-learn technical committee, and the author of the book "Introduction to machine learning with Python".
His work focuses on practical aspects of machine learning and the development of user-centric machine learning software.

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Andrei Paleyes

Andrei is currently pursuing PhD at the University of Cambridge. His research interests are somewhere between machine learning and software systems, leaning towards the latter. He also has keen interest in Bayesian optimization and is actively participating in several open source projects. Before jumping into the world of academia he has spent more than a decade as a software engineer, developing everything from small webapps to data center network software.

  • Emukit: Python toolkit for uncertainty quantification
Angela Pisco

Head of computational biology at insitro

  • Keynote - How Open Source Tools Power the Efforts of Biological Data Analysis and Drug Discovery
Anna Haensch
  • Small Town Police Accountability: A Data Science Toolkit
Anutosh Bhat

I am Anutosh Bhat, a 4rth year undegraduate student at IIT Madras . I'm persuing an interdisciplinary dual degree (B.Tech + M.Tech) in Biological Engineering and Data Science .I am an Open Source and Software Development enthusiast and have contributed to some influential libraries like SymPy, SageMath, Networkx, Kyverno and a couple others in the past . My main interests revolve around
domains like Symbolic and Numerical computations/algorithms and also some Cloud Native Computing based stuff.

  • SymPy Introductory Tutorial
Ariana Mendible

Ariana Mendible is an assistant professor at Seattle University, where she teaches and uses data science to approach social justice research problems.

  • Small Town Police Accountability: A Data Science Toolkit
Athan Reines
  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Axel Donath

I'm a Postdoc researcher at the Center for Astrophysics. My research interests include the Galactic X-Ray and Gamma-Ray source populations as well as statistical methods for analysis of low counts data in general. I'm also interested in methods to combine data from multiple instruments. I'm the lead developer of the open source software package Gammapy, sub-package maintainer of Astropy and a member of the CHASC astro-statistics collaboration. I'm also editor for the Astronomy and Astrophysics track of the Journal of Open Source Software JOSS.

  • Building better data structures, APIs and configuration systems for scientific software using Pydantic
  • Gammapy: a Python Package for Gamma-Ray Astronomy Version v1.0
Bane Sullivan

Bane Sullivan, co-creator of PyVista, is a Research Software Engineer working at the intersection of geoscience, visualization, and data science.

Bane is a geophysicist/hydrologist by training and has been working to grow PyVista's adoption within the subsurface geoscience communities.

  • 3D Visualization with PyVista
  • [BoF Room 103] Python Visualization and App Tools
Brigitta Sipőcz

I am an astronomer turned Research Software Engineer. I work at Caltech/IPAC to build and improve tools, e.g. Python libraries and Science Platforms to provide ways to access data in the NASA/IPAC Infrared Science Archive. Prior to joining IPAC, I was DiRAC Fellow in the data engineering team at the Institute for Data Intensive Research in Astrophysics and Cosmology in Seattle. I am a developer and maintainer of several open-source astronomy libraries and their infrastructure (e.g. astroML, astroquery, astropy) and I very much enjoy contributing to upstream projects as well in the wider Scientific Python ecosystem. I have a keen interest in finding ways to make tools more sustainable. I am a fellow of the Software Sustainability Institute.

  • Open Source Sprints [Kickoff in Room 204]
  • [BoF Room 103] SciPy 2023 Sprint Prep BoF
  • Open Source Sprints [Kickoff in Room 204]
Bryan Van de Ven
  • Interactive data visualization with Bokeh
C.A.M. Gerlach
  • [BoF Room 104] Beyond Notebooks: From reproducible to reusable research
  • [BoF Room 105] Scientific Python Packaging Summit
Caitlin Lewis

I am a current undergraduate student at the University of North Carolina at Chapel Hill studying Computer Science and Statistics. I currently work for the Hantman Lab in the UNC Neuroscience Center helping to develop tools to aid in the analysis and visualization of large calcium imaging datasets.

  • Ultra fast visualization of large datasets using modern graphics APIs in jupyter notebooks
Charles Blackmon-Luca
  • Advanced Dask Tutorial
Charles D Lindsey

Charles Lindsey is a Principal Data Scientist at Revionics. Charles earned a PhD in Statistics from Texas A&M in 2010, where he researched dimension reduction and classification. Charles then worked at StataCorp LLC. At StataCorp, Charles was the lead developer of the Extended Regression Model (ERM) commands, which allow causal inference on observational data with common complications like unobserved confounding variables and sample selection. At Revionics, Charles works on price optimization and sales forecasting using Bayesian methods and other machine learning techniques.

  • Bayesian Statistics with Python, No Resampling Necessary
Chen Qian

Chen Qian is a software engineer at Google. He is a maintainer of Keras and Tensorflow. In 2021, Chen co-founded the project KerasNLP with other Keras maintainers, and has since been working on building APIs for NLP developers. He is enthusiastic at languages, finding everything about language is charming, e.g., learning new languages, linguistics and NLP.

  • Explore generative models in AI with Keras
Cheuk Ting Ho

Before working in Developer Relations, Cheuk has been a Data Scientist in various companies which demands high numerical and programmatical skills, especially in Python. To follow her passion for the tech community, Cheuk is now working with the open-source community. Cheuk also contributes to multiple Open Source libraries like Hypothesis, Django and Pandas.

Besides her work, Cheuk enjoys talking about Python on personal streaming platforms and podcasts. Cheuk has also been a speaker at Universities and various conferences. Besides speaking at conferences, Cheuk also organises events for developers. Conferences that Cheuk has organized include EuroPython (which she is a board member), PyData Global and Pyjamas Conf. Believing in Tech Diversity and Inclusion, Cheuk constantly organizes workshops and mentored sprints for minority groups. In 2021, Cheuk has become a Python Software Foundation fellow.

  • Power up your work with compiling and profiling
Chris Havlin

Chris Havlin is a Research Scientist in the School of Information Sciences at the University of Illinois. His work focuses on open source scientific software development and computational geodynamics.

  • Introducing yt_xarray
Christopher Ariza

Christopher Ariza is Partner and Chief Technology Officer at Research Affiliates, a global leader in investment strategies and research. He is the creator and lead developer of StaticFrame, an alternative DataFrame library built on an immutable data model. Having worked in Python for over 20 years, he has developed tools in a variety of domains, including algorithmic music composition and computer-aided musicology, and has spoken at numerous conferences, including PyCon USA, PyData Global, PyData Los Angeles, and numerous other venues.

  • Out-Performing NumPy is Hard: When and How to Try with Your Own C-Extensions
Christopher Ostrouchov
  • Data of an Unusual Size: A practical guide to analysis and interactive visualization of massive datasets
Coleman Kendrick

Research Software Engineer in the Application Engineering group at Oak Ridge National Laboratory.

  • Using Numba for GPU acceleration of Neutron Beamline Digital Twins
David C. Folch
  • Using Linear Tracking Data to Estimate Backcountry Recreation Popularity
David Nicholson

Engineer with Embedded Intelligence, a research and development group in the DC area. Developer, maintainer of More at

  • vak: a neural network framework for researchers studying animal acoustic communication
David Zeber

David Zeber is a Staff Data Scientist at Mozilla who enjoys prototyping innovative approaches to improving the user search experience. While at Mozilla, he also led research into online tracking and privacy-preserving technologies for working with user data. He holds a PhD in applied probability from Cornell University.

  • Subpoenas Less Scary
Deepak Cherian
  • Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
  • Tidy Geospatial Cubes
Demitri Muna
  • [BoF Room 104] Funding Open Source Software
Dharhas Pothina
  • Community-first open source: An action plan!
  • Data of an Unusual Size: A practical guide to analysis and interactive visualization of massive datasets
Dimitri Yatsenko

Dimitri Yatsenko has a PhD in Neuroscience (Baylor College of Medicine) and Masters in Computational Engineering and Science (University of Utah). As CEO at DataJoint, he leads a team of scientists and engineers to develop tools for analyzing and managing neuroscience data for advanced collaborative projects. He serves as Principal Investigator on NIH grants to develop open-source software and a cloud platform supporting standardized data pipelines for common types of neuroscience experiments.

  • DataJoint: Bringing databases back into data science
Divyashree Shivakumar Sreepathihalli

Divya is a talented machine learning software engineer who is currently a part of the Keras team at Google. In this role, she specializes in developing Keras core modeling APIs and KerasCV to improve the functionality of the software.

Divya has an impressive track record of delivering successful conference talks, including the Southern Data Science Conference and the Women in ML Symposium. Prior to joining Google, Divya worked as a Deep Learning Scientist for Zazu Sensor, a startup group in Intel's Emerging Growth Incubation (EGI) group. Her work there focused on computer vision and deep learning algorithm development for object detection and tracking, resulting in significant advancements for the startup.

Before her time at Zazu Sensor, Divya worked as a Platform Architect at Intel's Client Computing Group, where she was responsible for developing proof of concepts for innovative solutions in anonymized computer vision applications. Her efforts resulted in several successful patents being filed, bringing substantial value to the organization.

Divya completed her Masters in Computer Engineering from Texas A & M University where she focused on Artificial intelligence in 2017.

  • Explore generative models in AI with Keras
Don Setiawan

Don Setiawan is a Senior Research Software Engineer at the University of Washington, eScience Institute, Scientific Software Engineering Center (SSEC). He has expertise in Python programming, web development, geospatial data analytics, and cloud-based data engineering. He is interested in building scalable, open software to facilitate scientific discovery across fields and enforce software best practices. He has been a power user of the Xarray ecosystem for several years across various projects with Ocean Observatory Initiative (OOI), U.S. Integrated Ocean Observing System (IOOS), National Oceanic and Atmospheric Administration (NOAA), and National Aeronautics and Space Administration (NASA). He is very excited to share his knowledge and help facilitate the Xarray tutorial as this is his first time at Scipy!

  • Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
Dr. Rumman Chowdhury

Dr. Rumman Chowdhury currently runs Parity Consulting, Parity Responsible Innovation Fund, and is a Responsible AI Fellow at the Berkman Klein Center for Internet & Society at Harvard University. She is also a Research Affiliate at the Minderoo Center for Democracy and Technology at Cambridge University and a visiting researcher at the NYU Tandon School of Engineering.

  • Keynote - Responsible AI in Practice: How far we've come and where we're going
Drew Camron

Scientific Python dev and educator @ UCAR/Unidata. MetPy, Siphon, Project Pythia.

  • Better (Open Source) Homes and Gardens with Project Pythia
Elena Romashkova

I'm an Associate Scientist 1 in the Oceanography Section of NCAR's Climate and Global Dynamics Lab.

  • Climate Model Evaluation Workflow Built on Jupyter Notebooks
Elliott Sales de Andrade
  • [BoF Room 103] Python Visualization and App Tools
Emma Marshall

I am a graduate student at the University of Utah in the Geography Department. My research uses remote sensing data and other tools to study recent variability of alpine glaciers in High Mountain Asia. I am excited to return for my second SciPy after attending for the first time in 2022!

  • Tidy Geospatial Cubes
Emmy Li

Emmy is a technical trainer at Anyscale Inc. She holds a B.Sc in Physics from Stanford University where she contributed toward computational astrophysics research at the Stanford Linear Accelerator Laboratory and NASA’s Jet Propulsion Laboratory. Emmy is passionate about creating high quality educational materials and sharing them with the broader Ray community.

  • Scalable machine learning workloads with Ray AI Runtime
Eric Sager Luxenberg

Eric Luxenberg is a PhD candidate in the Electrical Engineering department at Stanford University, advised by Stephen Boyd. His research interests include robust optimization and mathematical finance. He is a contributor to CVXPY, and has developed an open-source package for saddle optimization called DSP. He has also served as the primary instructor of Stanford’s convex optimization course.

  • Disciplined Saddle Programming
  • Controlling Self-Landing Rockets Using CVXPY
Erik Welch
  • GraphBLAS for Sparse Data and Graphs
Francesc Alted
  • Fast Exploration of the Milky Way (or any other n-dimensional dataset)
Fritz Lekschas

Fritz Lekschas is a computer scientist researching scalable visual exploration of biomedical data. As the Head of Visualization Research at Ozette Technologies, he is leading the development of web-based data visualization and exploration tools for analyzing high-dimensional single-cell data. Fritz earned his PhD in computer science from Harvard University, where he was advised by Hanspeter Pfister and Nils Gehlenborg. He has published more than twenty peer-reviewed papers and his work has been recognized with several awards.

In his free time, Fritz likes to work on open-source tools for visual data exploration.

  • Interactive Exploration of Large-Scale Datasets with Jupyter-Scatter
Gajendra Deshpande

I am Gajendra Deshpande and I am using Python since 2013 for academic research and development activities. I develop prototypes and applications in Natural Language Processing, Machine Learning, Cyber Security, and Web applications using Python and its ecosystem. I am working as a faculty of Computer Science and run a start-up in cyber security. I am an active member of the PyCon India community and served as program committee lead for PyCon India 2021. I have presented approximately 80 talks, 20 Workshops, and 15 posters across the globe at prestigious conferences like PyData Global, PyCon APAC, PyCon AU, EuroPython, DjangoCon US and Europe, SciPy India, SciPy USA, PyCon USA, JuliaCon, FOSDEM, and several other Python and FOSS conferences. I have helped Python and FOSS Conferences by reviewing the talk and tutorial proposals, mentoring first-time speakers, participating in the discussions, and organizing the events.

  • [BoF Room 104] Future of Python Programming Language in the Artificial Intelligence Era
Gil Forsyth

Gil Forsyth is a software engineer at Voltron Data. He followed the common career path of Japanese language specialist -> administrative assistant -> mechanical engineer -> computational fluid dynamicist -> data scientist -> software engineer -> machine learning engineer -> software engineer. Gil contributes to several projects in the PyData ecosystem and is a core maintainer of xonsh and Ibis. He served as the program chair for the Scientific Computing with Python (SciPy) conference from 2017 to 2020.

  • [BoF Room 103] SciPy 2023 Sprint Prep BoF
Guen Prawiroatmodjo

Guen Prawiroatmodjo is a physicist and software engineer at Microsoft Quantum. She studied Applied Physics at Delft Technical University and obtained a PhD in condensed matter physics experiment from the Niels Bohr Institute at the University of Copenhagen in Denmark. Her expertise is in quantum device characterization and control at cryogenic temperatures for developing quantum computing elements, and has broad experience with software engineering, data engineering and data science in the context of experimental data acquisition and analysis. A large part of her role is educating her fellow physicists and engineers to level up their Python development skills by adopting software best practices in their work. Guen has given introductory talks and workshops on Quantum Computing at various conferences, hackathons and events. Guen is a co-organizer of the SciPy conference and serves on the Program committee.

  • Meet your coding best friend: VS Code💖 - A hands-on tutorial on how to get the most out of the world’s most popular Python editor
Hadley Wickham
  • [BoF Room 103] Python Visualization and App Tools
Hannes Mühleisen

Prof. Dr. Hannes Mühleisen is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs, a consulting company providing services around DuckDB. He is also a senior researcher of the Database Architectures group at the Centrum Wiskunde & Informatica (CWI), the Dutch national research lab for Mathematics and Computer Science in Amsterdam. Hannes is also Professor of Data Engineering at Radboud Universiteit Nijmegen. His' main interest is analytical data management systems.

  • In-Process Analytical Data Management with DuckDB
Henry Schreiner III
  • [BoF Room 105] Scientific Python Packaging Summit
Hugo Bowne-Anderson

Hugo Bowne-Anderson is Head of Developer Relations at Outerbounds, a company committed to building infrastructure that provides a solid foundation for machine learning applications of all shapes and sizes. He is also host of the industry podcast Vanishing Gradients. Hugo is a data scientist, educator, evangelist, content marketer, and data strategy consultant, with extensive experience at Coiled, a company that makes it simple for organizations to scale their data science seamlessly, and DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 2 million learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open source software, both for individuals and the enterprise.

  • Full-stack Machine Learning for Data Scientists
Ian Thomas

Ian Thomas is a Senior Software Engineer at Anaconda. Originally an ocean modeller, Ian has many years' experience analysing and visualising data. Ian is an Open Source contributor and core maintainer of a number of libraries, most notably Bokeh, Datashader and fsspec. Ian is British and drinks a lot of tea.

  • Interactive data visualization with Bokeh
Inessa Pawson

Inessa is building bridges between people, open science, and open source software, advocating for diversification of contribution pathways to open source and supporting its social infrastructure. Passionate about the transformative power of collaboration out in the open, she has been organizing the Maintainers Summit at PyCon US since 2020 to foster best practices on how to maintain and develop sustainable open source projects and thriving communities. In her current role as NumPy Contributor Experience Lead, Inessa’s primary focus is on onboarding and supporting contributors, addressing gaps in the project governance, and developing programs to diversify pathways of contribution to the project.

  • [BoF Room 105] Open Source Project Code of Conduct Management and DEI Support
  • Contributor experience - Why it matters
Isabela Presedo-Floyd

Isabela Presedo-Floyd (she/her) is a question-asker and UX/UI and Accessibility Designer at Quansight Labs. She is an enthusiasm enthusiast who works on tools that support open, reproducible science.

  • Accessibility best practices for authoring Jupyter notebooks
Jack Ireland

I have worked in the field of solar physics since 1995. I am a co-founder of the SunPy and Helioviewer Projects. I am currently working as the Project Scientist for NASA's Solar Data Analysis Center, and US Project Scientist for the Solar and Heliospheric Observatory.

  • Seeing the Sun through the Clouds: Accelerating the SunPy Data Analysis Ecosystem with Dask
Jacob Schreiber

Jacob Schreiber is a post-doctoral researcher at Stanford University, where he studies human genomics using modern machine-learning tools. In his "free time," he contributes to the Python data science ecosystem in the form of pomegranate, a package for probabilistic modeling, and apricot, a package for submodular optimization for summarizing large data. In the past, he was a core developer for scikit-learn.

  • tfmodisco-lite: an attribution-based motif discovery algorithm
James A. Bednar

Jim Bednar is the Director of Custom 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 manages 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.

  • hvPlot and Panel: Visualize all your data easily, from notebooks to dashboards
  • [BoF Room 103] Python Visualization and App Tools
James Bourbeau
  • [BoF Room 103] PyArrow in pandas and Dask
James Bourbeau

James Bourbeau is a core maintainer of Dask, experienced educator, and has presented on Dask at various conferences and meetups such as SciPy, PyCon, and PyData Global. His most recent presentation was an introductory Dask tutorial at SciPy 2020, a recording of which can be found at

  • Advanced Dask Tutorial
Jarrod Millman
  • [BoF Room 105] Scientific Python Ecosystem Coordination
Jeff Wagner

Jeff Wagner is the Technical Lead at Open Force Field, a pre-competitive effort supported by a mix of industry partners and government funding. OpenFF aims to build extensible tools and datasets to advance the state-of-the-art in molecular modeling. He received his PhD in chemistry from UCSD in 2018 and is broadly interested in understanding out how sustainable organizations can exist at the interface of academia, industry, and open source.

  • Open Force Field: next-generation force fields with open data, open software, and open science
Jessica Scheick
  • Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
Jim Kitchen

I am a Senior Software Engineer at Anaconda, focused on graph analytics and sparse data. I am a member of the GraphBLAS C API committee and an author of the python-graphblas library.

  • GraphBLAS for Sparse Data and Graphs
Jim Pivarski

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). Then he worked as a data scientist for Open Data Group for 5 years before joining Princeton as a computational physicist in 2016. Now he develops software tools for data analysis in Python, leading the development of Awkward Array, and helps users with a wide range of data analysis problems.

  • Thinking in arrays
John Kirkham

Got my B.S. & M.S. in Physics. After graduating went to work at Howard Hughes Medical Institute for 5 years working on image processing problems particularly in neuroscience. Got more involved in open source during that work with particular interest in packaging, storage, and distributed array processing. Then joined the NVIDIA RAPIDS team where there has been good overlap with these past interests as well as new ones.

  • New CUDA Toolkit packages for Conda
  • Zarr: Community specification of large, cloud-optimised, N-dimensional, typed array storage
  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Jon Mease
  • [BoF Room 103] Python Visualization and App Tools
Jorge Martinez

Aerospace engineer and software developer interested in computational astrodynamics. In his free time, Jorge maintains various open source scientific projects including poliastro and the whole PyAnsys ecosystem.

Github account: jorgepiloto

  • What happens when the main maintainer of a project takes a step down?
Josh Moore

Josh is a research software engineer focusing on the standardization and storage of bioimaging data. Typically, that means finding ways of storing large binary with well-defined metadata in order to make them shareable. To that end, he is a maintainer of the Open Microscopy Environment (OME) as well as Zarr projects.

You can find out more under

  • Zarr: Community specification of large, cloud-optimised, N-dimensional, typed array storage
Josh Moore
  • [BoF Room 104] Where on Earth is my Pixel?
Juan Nunez-Iglesias
  • [BoF Room 104] Where on Earth is my Pixel?
  • [BoF Room 103] Python Visualization and App Tools
Juan Nunez-Iglesias

I'm a research scientist helping other scientists get insights from their image data using Python. I've been using Python since 2008, and the main scientific Python ecosystem (NumPy, SciPy, & co) since 2010. In 2012, on a whim, I went to my first SciPy (US) conference, and it changed my life! I realised that "open source" didn't mean just posting the code online. It meant actively collaborating on code with other scientists, across vast distances and at different times. Before you could say "import numpy as np", I had joined the scikit-image team, written a paper about it, written a whole book on SciPy (!), started new collaborative, open source libraries, and just generally been all-in on Scientific Python. I've been coming back to SciPy as often as I can to pay it forward for new folks in our community! 😊

  • View, annotate, and analyze multi-dimensional images in Python with napari
  • image analysis and visualization in Python with scikit-image, napari, and friends
Juanita Gomez

Juanita Gomez is passionate programmer, mathematician and open source advocate; former developer of Spyder IDE at Quansight. She has a BS in Pure Mathematics from Pontificia Universidad Javeriana in Colombia and is currently pursuing a Ph.D position in Computer Science at UC Santa Cruz. She is a community manager for the Scientific Python project, a community effort to better coordinate and support scientific Python libraries.

  • [BoF Room 104] Beyond Notebooks: From reproducible to reusable research
  • Scientific Python: from `__init__` to `__call__`
  • [BoF Room 105] Scientific Python Ecosystem Coordination
Jules S. Damji

Jules S. Damji is a lead developer advocate at Anyscale Inc, an MLflow contributor, and co-author of Learning Spark, 2nd Edition. He is a hands-on developer with over 25 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud, VeriSign, ProQuest, Hortonworks, and Databricks, building large-scale distributed systems. He holds a B.Sc and M.Sc in computer science (from Oregon State University and Cal State, Chico respectively), and an MA in political advocacy and communication (from Johns Hopkins University).

  • Modern compute stack for scaling large AI/ML workloads
Julia Signell
  • Advanced Dask Tutorial
Kevin Tyle

Mr. Tyle is the Manager of Departmental Computing for the Department of Atmospheric and Environmental Sciences at the University at Albany, at which he received his M.S. in Atmospheric Science in 1995. He also has a B.A. in Psychology, with emphases on Neuroscience and Cognitive Science, from the University of Rochester. His main interest is promoting the use of free- and open-source software packages, mostly using Python, for the analysis, visualization and sharing of geoscientific datasets.

  • Better (Open Source) Homes and Gardens with Project Pythia
Kira Evans
  • image analysis and visualization in Python with scikit-image, napari, and friends
Kushal Kolar

  • Ultra fast visualization of large datasets using modern graphics APIs in jupyter notebooks
  • [BoF Room 103] Python Visualization and App Tools
Kyle Sunden

Kyle is a Research Software Engineer working for Matplotlib with a focus on the data pipeline.
Kyle has a PhD in Chemistry from the University of Wisconsin where he made software to control laser spectroscopy instrumentation.

  • Mosaic Magic with Matplotlib
Lars Grüter
  • image analysis and visualization in Python with scikit-image, napari, and friends
Leah Silen

Leah is the Executive Director of NumFOCUS

  • [BoF Room 105] Open Source Project Code of Conduct Management and DEI Support
Leo Fang
  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Leopold Talirz

Leopold studied physics, and then spent a decade working as a computational materials scientist, solving nature’s riddles through atomistic simulations and writing software to make materials science more open, reproducible, and accessible. In 2021 he joined Microsoft Quantum to supercharge atomistic simulations via the cloud and, eventually, quantum computing. Leopold is a core contributor to the Python-based open-sourceAiiDA workflow manager as well as the Materials Cloud platform for seamless sharing of resources in computational materials science. He serves on the NumFOCUS committee for evaluating affiliated project applications and is co-chairing the chemistry & materials session at SciPy this year.

Besides talks at scientific conferences, Leopold organized AiiDA tutorials in Switzerland, the Netherlands, Norway, and China (sample video), including live hands-on lectures on how to code AiiDA plugins in Python.

  • Meet your coding best friend: VS Code💖 - A hands-on tutorial on how to get the most out of the world’s most popular Python editor
Lucas Hale

Dr. Lucas Hale is a materials research scientist at NIST where he is the content manager for the Interatomic Potentials Repository project. In support of the project, he has developed numerous Python packages for interacting with the repository data, designing atomistic simulations for investigating bulk crystal and crystalline defects, and developing and performing high throughput calculations.

  • Designing user-friendly APIs for the NIST Interatomic Potentials Repository
Ludovico Bianchi
  • How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev
  • [BoF Room 103] SciPy 2023 Sprint Prep BoF
  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Mark Raasveldt
  • In-Process Analytical Data Management with DuckDB
Matt Davis

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.

  • [BoF Room 103] SciPy 2023 Sprint Prep BoF
  • Introduction to Python and Programming
Matt Haberland

Matt Haberland (@mdhaber) is an Assistant Professor in the BioResource and Agricultural Engineering Department at Cal Poly. He earned his Ph.D. in Mechanical Engineering at MIT in 2014 for his thesis "Extracting Principles from Biology for Application to Running Robots", and previously created the Contact Sensor / Stabilizer for the rock drill of the Mars rover Curiosity. Matt has been attending the SciPy conference since 2019 as maintainer of the SciPy library.

  • Resampling and Monte Carlo Methods in SciPy.stats
Matt Harrison

Matt is a corporate trainer, author, and consultant on Python and Data Science. He has a CS degree from Stanford University. He is a best-selling author on Python and Data subjects. His books: Effective Pandas, Illustrated Guide to Learning Python 3, Intermediate Python, Learning the Pandas Library, and Effective PyCharm have all been best-selling books on Amazon. He just published Machine Learning Pocket Reference and Pandas Cookbook (Second Edition). He has taught courses at large companies (Netflix, NASA, Verizon, Adobe, HP, Exxon, and more), Universities (Stanford, University of Utah, BYU), as well as small companies. He has been using Python since 2000 and has taught thousands through live training both online and in person.

  • Idiomatic Pandas
  • [BoF Room 103] PyArrow in pandas and Dask
Matthew Barber

Software engineer @ Quansight working on Data APIs. Hypothesis maintainer.

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Meiirbek Islamov

Currently, I am pursuing a Ph.D. degree in Chemical Engineering at the University of Pittsburgh with an expected completion date of January 2024. My current research at Pitt focuses on understanding nanoscale thermal transport physics in Metal-Organic Frameworks (MOFs), a class of porous materials, which have been heralded as revolutionary materials for gas adsorption applications. In my research, I use high-performance computing, deep learning, and computational materials science/chemistry techniques.

  • A Graph-Neural Network-Based model for rapid prediction of Thermal Transport in Metal-Organic Frameworks
Melissa Weber Mendonça

Melissa is an applied mathematician and former university professor who fell in love with open source communities. She has been involved with the Python and PyData communities for some time, with a focus on outreach, education and DEI. She works at Quansight as a Senior Developer Experience Engineer, is a maintainer for NumPy and SciPy, and believes in the power of contributions beyond code.

  • Contributor experience - Why it matters
Michael Droettboom

Principal Software Engineering Manager at Microsoft

  • Keynote - Open Source Contributors in Space and Time
Michael Droettboom
  • [BoF Room 103] CPython performance
Nabil Freij

Nabil Freij is working as Research Software Engineer for Bay Area Environmental Research Institute supporting several NASA missions at Lockheed Martin Solar and Astrophysics Laboratory.

Before this, he was a Software Engineer at the The Institute for Environmental Analytics based at the University of Reading focused on providing customized weather and climate data to growers and farmers.

Before his pivot to Software Engineering, he was a research scientist at Universitat de les Illes Balears working on coronal heating and MHD waves.

  • Seeing the Sun through the Clouds: Accelerating the SunPy Data Analysis Ecosystem with Dask
Nathan Jessurun
  • [BoF Room 103] Python Visualization and App Tools
Naty Clementi

Naty is an Open Source Software Engineer at Coiled, Dask contributor, and an experienced educator. She has taught multiple Dask tutorials at conferences like Scipy, PyData, Women Who Code meetups, as well as periodic live tutorials. Her most recent presentations are Dask Tutorial Scipy 2022 ( and PyData NYC 2022. In her free time, she likes playing ultimate frisbee, going fly-fishing, and playing video games.

  • Advanced Dask Tutorial
Negin Sobhani

Negin Sobhani is a High Performance Computing consultant and computational atmospheric scientist working at the National Center for Atmospheric Research (NCAR). She has several years of experience developing and supporting open-source tools and infrastructure to improve the performance and accessibility of Earth System models and bridge the gap between data science, atmospheric science, and software engineering. She is interested in applying in adopting cutting-edge data science and computational technologies to improve our understanding of the environment.

  • Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
Nick Langellier

I am a senior machine learning engineer at VideaHealth, Inc. where I am currently developing AI models for automatic detection of dental diseases. My background is in astrophysics and I have 4 years experience as a teaching assistant at the University of Illinois at Urbana-Champaign and Harvard University. The coursework ranged from introductory to mid-level physics in both theoretical and laboratory settings. I have contributed to several conferences, most notably an invited talk at an exoplanet conference in Göttingen, Germany. There I presented my Ph. D work on improving exoplanet analysis pipelines through the use of machine learning.

  • Building better data structures, APIs and configuration systems for scientific software using Pydantic
Nicole Brewer

Nicole is PhD student in History and Philosphy of Science at Arizona State University, where she studies the intersection of science and software from many disciplinary perspectives. As a current Better Scientific Software Fellow and a former research software engineer, she is passionate about using computational notebooks and literate programming to make scientific software more accessible and reproducible. Check out Long Tales of Science - her interview podcast about women in high-performance computing.

  • How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev
Niels Bantilan

Niels is the Chief Machine Learning Engineer at, and core maintainer of Flyte, an open source workflow orchestration tool, author of UnionML, an MLOps framework for machine learning microservices, and creator of Pandera, a statistical typing and data testing tool for scientific data containers. His mission is to help data science and machine learning practitioners be more productive.

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.

  • A Hands-on Introduction to Production-grade Data Science Orchestration with Flyte
  • Pandera: Beyond Pandas Data Validation
Noa Tamir

Noa have been involved with the R and PyData communities for some time, with a focus on community building and DEI. They are a member of the NumFOCUS Board of Directors and DISC committee, PyLadies Organizer, and chaired the PyData Berlin 2022 conference. In addition, they are a Lead Data Science Coach at neue fische, contributing to pandas, and are currently developing the Contributor Experience Community and Handbook with Inessa Pawson and Melissa Mendonça.

  • [BoF Room 105] Open Source Project Code of Conduct Management and DEI Support
  • Contributor experience - Why it matters
Pablo Garcia-Nieto

Computational biologist at CZI focusing on providing access to all single-cell data hosted on CZ CELLxGENE ( Ph.D. on cellular and molecular biology from Stanford University, and BSc in genomics from the Autonomous National University of Mexico

  • An API for efficient and low-latency access to the largest standardized single-cell data repository by CZ CELLxGENE Discover.
Paige Martin
  • [BoF Room 104] Funding Open Source Software
Patrick Hoefler
  • [BoF Room 103] PyArrow in pandas and Dask

Pavithra is a Developer Advocate at Quansight, where she works to support the PyData community. She also contributes to the Bokeh and Dask projects; and has helped administrate Wikimedia’s outreach programs in the past. In her spare time, she enjoys a good book and hot coffee. :)

  • Community-first open source: An action plan!
  • Interactive data visualization with Bokeh
  • Data of an Unusual Size: A practical guide to analysis and interactive visualization of massive datasets
Philipp Schiele

Main instructor Philipp Schiele
Philipp Schiele's educational background is in finance and economics and he is currently pursuing a PhD in financial econometrics at the Ludwig Maximilian University of Munich, where he taught various courses in statistics. He is a CVXPY maintainer and has presented a tutorial at SciPy 2022. Generally, he is enthusiastic about finance, optimization, and technology, especially open-source projects.

  • Disciplined Saddle Programming
  • Controlling Self-Landing Rockets Using CVXPY
Qiusheng Wu

Qiusheng Wu is an Associate Professor in the Department of Geography & Sustainability at the University of Tennessee, Knoxville. He is also an Amazon Visiting Academic and a Google Developer Expert (GDE) for Earth Engine. His research focuses on Geographic Information Science, remote sensing, and open-source software development. Dr. Wu is an advocate of open science and reproducible research. He has developed several open-source packages that have been widely used by the geospatial community, such as geemap and leafmap. For more information about his research, visit

  • Interactive Analysis of Satellite Imagery with Earth Engine and Geemap
  • An Introduction to Cloud-Based Geospatial Analysis with Earth Engine and Geemap
Rajeev Jain

Rajeev Jain is a Principal Research Software Specialist at the Argonne National Laboratory, located in the suburbs of Chicago, with a focus on managing multi-disciplinary simulation, scalability and computation for applications-oriented problems.

He is a quick learner who loves to solve complex problems and readily adapts to new challenges. His work encompass a range of scientific domains, from simulating physical phenomena to developing deep-learning-enabled precision medicine for cancer and providing data analysis tools for the geoscience community.

To learn more about Rajeev Jain's work and research, you can visit his profile page on the Argonne website:


  • UXarray, a python library for unstructured climate and weather data
Ralf Gommers

Ralf has been deeply involved in the SciPy and PyData communities for over a decade. He is a maintainer of NumPy, SciPy and, 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.

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Rebecca BurWei

Staff Data Scientist at Mozilla. Prior lives include building a data department from scratch and getting a PhD in non-commutative algebra. Come talk to me about industry vs academia, careers in data science, under-rated places in Chicago, and who should have won the NBA playoffs.

  • Subpoenas Less Scary
Rick Ratzel

Rick Ratzel is a technical lead for RAPIDS cuGraph - a library of GPU-accelerated graph algorithms. Rick joined NVIDIA in January 2019, bringing several years of experience as a technical lead for teams in industries that include test and measurement, electronic design automation, and scientific computing. Rick’s focus for cuGraph, and throughout his career, has been on software architecture and API usability.

  • New CUDA Toolkit packages for Conda
Roni Kobrosly

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. During this time, I taught a couple undergraduate epidemiology courses, once of which involved a sizable introduction to causal thinking. I've also presented many one-off departmental presentations and at a few epidemiology conferences on causal inference in both cases.

Since leaving the academic world, I've been loving my second life in the tech industry as a data scientist, ML engineer, and more recently as the Head of Data Science at a medium-sized health tech company based in Washington DC. I love mentoring junior data folks and explaining the magic of data analysis and modeling to non-technical audience.

I also am a member of the open-source community, being the author and maintainer of the causal-curve python package. This package provides a set of tools for estimating the causal impact of continuous/non-binary treatments (e.g. estimating the causal impact of a neighborhood's income inequality on local crime, or understanding the causal effect of increasing a product's price on conversion rates).

  • Introduction to Causal Inference
Ryan May

Ryan May is a software engineer and deputy director for the Unidata program, part of the University Corporation for Atmospheric Research (UCAR) Community Programs, working on Python software and training for the atmospheric science community. Ryan began his meteorology career pursuing a B.S. in Meteorology at the University of Oklahoma in 1999. In 2014, Ryan started at Unidata, exchanging working on radar meteorology for working on open source tools for meteorology in Python. Currently, he is the Python team lead at Unidata and a core developer of the MetPy and Siphon Python packages, as well as a member of the steering committee for matplotlib and the core team for Conda Forge.

  • Building MetPy for the Long Term: Working to Keep an Open Source Project Sustainable

Dr. Kang completed his PhD in Geophysics at University of British Columbia, Canada, in 2018. His thesis work focused on electromagnetic imaging and its application to mining problems. Currently, he is a Postdoctoral Researcher in the Geophysics Department at Stanford. His research focus is on maximizing the value of sensor data for advancing groundwater science and management. He is a co-creator of an open-source geophysical software, SimPEG.

  • Accelerating the Use of Public Geophysical Data for Recharging California’s Groundwater
Sandhya Govindaraju

Sandhya is a Scientific Software Developer & Python Trainer at Enthought. Earlier, she supported CAD tools for microprocessor design at Sun Microsystems and Oracle. She holds a M.S in Electrical and Computer Engineering from University of Texas at Austin.

Sandhya enjoys learning new things and is passionate about sharing her knowledge and experience with others. Outside of work, she spends time with family and volunteers.

  • Introduction to Numerical Computing With NumPy
Sangyub Lee

I am a contributor of SymPy, and I have been using SymPy to develop math education solutions in Mathpresso Inc and TigerMilk.Education.

  • SymPy Introductory Tutorial
Sanket Verma

Sanket is a data scientist based out of New Delhi, India. He likes to build data science tools and products and has worked with startups, government and organisations. He loves building community and bringing everyone together and is Chair of PyData Delhi and PyData Global. Currently, he's taking care of the community and OSS at Zarr as their Community Manager.
When he’s not working, he likes to play the violin and computer games and sometimes thinks of saving the world!

  • Zarr: Community specification of large, cloud-optimised, N-dimensional, typed array storage
Sarah Kaiser

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.

  • Meet your coding best friend: VS Code💖 - A hands-on tutorial on how to get the most out of the world’s most popular Python editor
Saul shanabrook

Working on e-graphs in Python currently. Interested in cross library collaboration in the Python data science ecosystem.

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Savin Goyal

Savin is the co-founder and CTO of Outerbounds - where his team is building the modern ML stack to accelerate the impact of data science. Previously, he was at Netflix, where he built and open-sourced Metaflow, a full stack framework for data science.

  • Full-stack Machine Learning for Data Scientists
Scholz Garten, 1607 San Jacinto Blvd

  • SciPy Attendee Social Event hosted by Open Source Science (OSSci)
SciPy 2023 Committee
  • [BoF Room 105] SciPy 2024
Scott Henderson

Scott is research scientist in the University of Washington (UW) Department of Earth and Space Sciences and data science fellow at the eScience Institute. He works on numerous NASA-funded efforts to develop open Cloud computing solutions for data intensive research.

  • Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
  • Tidy Geospatial Cubes
Sebastian Raschka

Sebastian Raschka is a machine learning and AI researcher with a strong passion for education. As Lead AI Educator at Lightning AI, he is excited about making AI and deep learning more accessible and teaching people how to utilize these technologies at scale.
Sebastian was previously an Assistant Professor of Statistics at the University of Wisconsin-Madison. However, in 2023, he resigned from his position to devote himself fully to the Lightning AI startup company, which he had joined in 2022. While working at UW-Madison, Sebastian focused on researching deep learning and machine learning. To learn more about his research, you can visit his website at

Moreover, Sebastian loves open-source software and has been a passionate contributor for over a decade. Next to coding, he also loves writing and authored the bestselling Python Machine Learning book and Machine Learning with PyTorch and Scikit-learn.

If you like to find out more about Sebastian and what he is currently up to, please visit his personal website at You can also find Sebastian on Twitter (@rasbt), Mastodon (, and LinkedIn (

  • Modern Deep Learning with PyTorch
Shin-Rong Tsai

Shin-Rong Tsai is a research scientist at the University of Illinois Urbana-Champaign in School of Information Sciences. She received her master's and bachelor's degrees at National Taiwan University in physics department.
Her work now focuses on developing in situ analysis tool that enables ongoing simulations to use Python to analyze data. She also works on developing tools for analyzing and visualizing volumetric data.

  • libyt: a Tool for Parallel In Situ Analysis with yt
Sophia Vargas

Sophia Vargas is a Program Manager in the research and education team within Google’s Open Source Programs Office. In this role she leads efforts that span project health, contributor experience, and open source economics. She is also on the Governing Board and an active contributor to the CHAOSS community. Prior to Google, Sophia was an analyst at Forrester Research, covering data center infrastructure and cloud strategy.

  • Diversity Luncheon Keynote: How can we protect vulnerable groups while measuring representation in our communities?
Sophia Yang

Sophia Yang is a Senior Data Scientist and a Developer Advocate at Anaconda. She is passionate about the data science community and the Python open-source community. She is the author of multiple Python open-source libraries such as condastats, cranlogs, PyPowerUp, intake-stripe, and intake-salesforce. She serves on the Steering Committee and the Code of Conduct Committee of the Python open-source visualization system HoloViz. She also volunteers at NumFOCUS, PyData, and SciPy conferences. She holds an M.S. in Computer Science, an M.S. in Statistics, and a Ph.D. in Educational Psychology from The University of Texas at Austin.

  • hvPlot and Panel: Visualize all your data easily, from notebooks to dashboards
  • [BoF Room 103] Python Visualization and App Tools
Stephan Hoyer

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Stephannie Jimenez Gacha

I've been working in open source since 2019 as part of multiple projects involving scientific computing and IDE development. The last two years a lot of my work has been focused on providing a better UI/UX of multiple applications. I've given multiple talks about different topics, the two most recent are available in the following links:

  • PyData/Pycon Berlin 2022:
  • Scipy Latam 2021:
  • Accessibility best practices for authoring Jupyter notebooks
  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Steve Dutcher

Steve Dutcher is a Software Developer/Engineer at the University of Wisconsin, Space Science & Engineering Center with 20 years of experience applying his computer science degree to Atmospheric Science. He currently works as part of the NASA Atmosphere SIPS responsible for producing operational cloud and aerosol products from polar orbiting satellites. Additional projects include machine learning applications, producing a low latency fire product from direct broadcast, and supporting instrument field campaigns.

  • A Computer Vision (ML) Approach to Classifying Clouds and Aerosols from Satellite Observations
Steve Greenberg

Steve is passionate about using machine learning and remote sensing technology to tackle the climate and sustainability crises. He leads the Developer Relations team for Google Earth Engine. Earth Engine is a geospatial analysis platform advancing planetary sustainability and resilience to climate change. His team helps remote sensing professionals, data scientists and machine learning engineers analyze petabytes of satellite imagery to understand and protect the earth. Earth Engine is provided free-of-charge for noncommercial and research purposes.

From 2016 through 2021, Steve led Developer Relations for BigQuery, Vertex AI and other Machine Learning and Data Analytics products in Google Cloud Platform, where he focused on improving the experience for users of scikit-learn, XGBoost and TensorFlow.

Steve also co-leads Google's largest grassroots sustainability group - organizing Googlers to incubate new climate initiatives. Three of the climate areas he's worked on - wind energy prediction, real-time precipitation modeling and sustainable building design - have graduated into full-time projects at Google. Prior to joining Google in 2016, Steve led engineering at a Seattle startup helping governments be more accountable to their citizens with public data. Before 2012, Steve was a Program Manager working on various data efforts in Microsoft's Office team.

  • Interactive Analysis of Satellite Imagery with Earth Engine and Geemap
  • An Introduction to Cloud-Based Geospatial Analysis with Earth Engine and Geemap
Steven Diamond

Steven Diamond works on large scale battery optimization at Gridmatic. Steven received a PhD in Computer Science from Stanford University, where he studied optimization under Prof. Stephen Boyd. He is the original developer and BDFL of CVXPY.

  • Controlling Self-Landing Rockets Using CVXPY
Stuart Mumford

Stuart writes open source software for solar and astro physics. Is the the lead-developer of SunPy, contributes to Astropy, and spends most of his time working with the DKIST data center on data products and Python software for users of DKIST data.

  • Seeing the Sun through the Clouds: Accelerating the SunPy Data Analysis Ecosystem with Dask
Stéfan van der Walt
  • [BoF Room 105] Scientific Python Ecosystem Coordination
Tania Allard
  • Open Source Sprints [Kickoff in Room 204]
  • Open Source Sprints [Kickoff in Room 204]
Tetsuo Koyama

Hi! My name is Tetsuo Koyama. I'm CAE software engineer in Japan. I'm interested in scientific computing and visualization with computer graphics. I am a commiter of GetFEM and developer team of PyVista.

  • 3D Visualization with PyVista
Thomas J. Fan

Thomas J. Fan is a Staff Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library for Python. Previously, Thomas worked at Columbia University to improve interoperability between scikit-learn and AutoML systems. He is a maintainer for skorch, a neural network library that wraps PyTorch. Thomas has a Master's in Mathematics from NYU and a Master's in Physics from Stony Brook University.

  • Can There Be Too Much Parallelism?
  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Thomas Nicholas

Tom is a Research Software Engineer working in Ryan Abernathey's Ocean Transport Group at Lamont Doherty Earth Observatory, 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 is a member of the xarray core development team, and also works on xGCM, pint-xarray, and xarray-datatree.

  • Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
Thomson Comer

Thomson Comer has been writing GPU-accelerated libraries at NVIDIA since 2018. He contributes to RAPIDS cuDF, cuSpatial, and node-rapids, and collaborates with customers and curious developers about best practices for GPU acceleration. He earned an M.S. in computer science in 2009 with a concentration in machine learning, computer vision, and graphics. Before NVIDIA, Thomson worked for a decade at the startup accelerator and consulting firm Cardinal Peak.

  • New CUDA Toolkit packages for Conda

Timo is a technical writer and project manager at makepath. He started contributing to Bokeh in 2020 and loves to help others succeed in the world of Open Source.

  • Interactive data visualization with Bokeh
Tracy Teal

Tracy Teal is the Open Source Program Director at Posit. Previously, she was a co-founder of Data Carpentry and the Executive Director of The Carpentries. 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, EarthLab 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.

  • Scientific and technical publishing with Python and Quarto
Travis E Oliphant

Travis is a long-time participant in the SciPy ecosystem.

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Tyler Reddy

Staff Scientist at LANL

  • Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Valerio Maggio

Valerio Maggio is a Researcher, a Data scientist Advocate at Anaconda, and a casual "Magic: The Gathering" wizard. He is well versed in open science and research software, supporting the adoption of best software development practice (e.g. Code Review) in Data Science. Valerio is also an open-source contributor, and an active member of the Python community. Over the last twelve years he has contributed and volunteered to the organization of many international conferences and community meetups like PyCon Italy, PyData, EuroPython, and EuroSciPy. All his talks, workshop materials and random ramblings are publicly available on his Speaker Deck and GitHub profiles.

  • PPML: Machine Learning on data you cannot see
Victoria Adesoba

Victoria is the Director of Operations at makepath and has enjoyed contributing to Bokeh over the last year. She enjoys traveling and working on the go as well as mentoring youth in her community.

  • Interactive data visualization with Bokeh
Vincent Sutherland

Vince is a Data science grad student with a background in biology. He lives in a small mountain town where he works on a research team involved in estimating and quantifying the risks abandoned hard-rock mines pose to the population of Arizona.

  • Using Linear Tracking Data to Estimate Backcountry Recreation Popularity
Will Barnes

Research Term Faculty, American University, Washington, D.C., USA
Research Scientist, NASA Goddard Spaceflight Center, Greenbelt, MD, USA

  • Seeing the Sun through the Clouds: Accelerating the SunPy Data Analysis Ecosystem with Dask
Yarden Cohen

Researcher of living and artificial neural systems, behavior, memory, and computation. Assistant professor at the Weizmann Institute of Science, Israel.

  • vak: a neural network framework for researchers studying animal acoustic communication
Yuanqing Wang

Simons Center Fellow, NYU

  • From Espaloma to SAKE: To brew, distill, and mix force fields with balanced briskness, smoothness, and intricacy.
amanda casari

amanda casari is a developer relations engineer in the Open Source Programs Office at Google, where she is co-leading research and engineering to better understand risk and resilience in open source ecosystems. She was named an External Faculty member of the Vermont Complex Systems Center in 2021. amanda is persistently fascinated by the difference between the systems we aim to create and the ones that emerge, and pie.

  • Thar Be Dragons - Ethical, Legal, and Policy Challenges when Measuring Open Source
bonny p mcclain

Dr Bonny McClain is a geospatial analyst & self described human geographer and social anthropologist. Dr McClain applies advanced data analytics, including data engineering and geo-enrichment, to poverty, race, and gender discussions. Her research targets judgments about structural determinants, racial equity, and elements of intersectionality to illuminate the confluence of metrics contributing to poverty. Moving beyond ZIP codes to explore apportioned socioeconomic data based on underlying population data leads to discovering novel variables based on location to build more context to complex data questions.

Recent Talks:
Data Day Texas, Geospatial Keynote 2023
Open Source Solutions for Environmental Racism|Open Source Science Data Repositories Workshop| NASA, Langley Research Center|September 2022
Keynote Speaker | GIS DAY | Los Angeles County 2023
GIS Keynote Data Day Texas 2023
Formulating geospatial data questions to answer big problems | GeoPython 2022
SciPy 2022-2023 Diversity Committee Chair 2022
NC HIMSS Annual Conference: Closing Keynote: Location Intelligence: How Does Our Infrastructure Influence Change in Our Built Healthcare Environment?
SciPy Diversity Luncheon Keynote – July 2020--bias in algorithms

  • Python for answering geospatial questions: exploring social inequity in our communities