Anderson works as a software/data engineer at CarbonPlan, a non-profit working on data science and policy issues surrounding carbon removal and climate solutions. He is an open-source software advocate and contributor. He helps maintain the Xarray, dask-jobqueue, and a few other packages/libraries within the scientific Python data ecosystem.

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.

Youness Bennani is a full stack software developer at Bloomberg. He works on developing a financial data analysis tool that relies on Jupyter and leverages the Bloomberg Terminal and its data. Youness was previously working as a data analyst for the Berkeley National Lab, developing statistical models for energy efficiency policy decisions. Youness holds a Master's degree in energy and environmental engineering.

Eduardo Blancas is the Co-Founder and CEO of Ploomber, a Y Combinator-backed company developing tools to bridge the gap between interactive data work and production. Before that, he was a Data Scientist at Fidelity Investments, where he deployed the first customer-facing Machine Learning model for asset management. Eduardo holds an M.S. in Data Science from Columbia University and a B.S. in Mechatronics Engineering from Tecnológico de Monterrey.

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 500,000 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.

Alex is a Software Engineer and Jupyter Architect at the IBM Center for Open Source Data and AI Technologies (CODAIT), contributing to the Jupyter ecosystem. He serves on the JupyterLab Council and is a core committer on the Elyra project. He is also a PPMC for Apache Livy (Incubating).

Jason Chambless is a software developer at KBI Biopharma, Inc, a global biopharmaceutical manufacturing organization. He received his PhD in chemical and biological engineering in 2008 from Montana State University in Bozeman, MT. Jason has worked as a software developer since 2006, using languages from Fortran to C# to Python, to do everything from modeling industrial chemical reactors to building websites that track supply chain procurement.

I am a physical oceanographer and open-source scientific software developer. I help maintain Xarray, and other projects in the Xarray ecosystem (cf-xarray, flox, xgcm).

Naty Clementi is an Open Source Software Engineer at Coiled and is a contributor of Dask. Naty has presented versions of the Dask Tutorial in a local Women Who Code meetup (https://www.youtube.com/watch?v=ZKw7PdoS7YA&t=318s) and youtube streamings (https://www.youtube.com/watch?v=nHMcqEYZ5qY&t=488s).

Matt Craig is a Professor of Physics and Astronomy at Minnesota State University Moorhead, in western Minnesota. He started using python about six years ago to do data analysis on data from a small telescope the university owns. Jupyter notebooks seemed like a natural fit for new users; widgets make it even easier for them to get started. Since learning about widgets several years ago he has used them to connect programming novices with high-quality scientific packages. He has taught a Computational Physics course that has evolved into a scientific python course, with roughly equal emphasis on pythonic programming, computational methods, and a survey of existing packages. In addition, he leads a group of 5-10 undergraduates in doing astronomical research and coding in Python.

Things about myself that you can talk to me about if you feel like:

* I work full-time as a core developer of QuTiP for RIKEN.
* I help out with HPy, the new C API for Python.
* I founded PyConZA in 2012 and am still heavily involved.
* I've worked in data science, bioinformatics and radio astronomy.
* I've written some not terrible games for PyWeek.

I like hearing what others are excited enough about to devote a lot of their attention to.

Martha is a software engineer at the IBM Center for Open Source Data and AI Technologies. She is a distinguished contributor on [JupyterLab](https://github.com/jupyterlab/jupyterlab) and a committer on [Elyra](https://github.com/elyra-ai/elyra).

Itay Dafna is a Software Engineer in Bloomberg’s San Francisco Engineering office. He is developing open source and proprietary data visualization libraries and tooling to help users explore, understand, and communicate complex relationships within their data. Itay joined Bloomberg in 2013, where he has previously held the roles of Quantitative Analytics Team Leader and Financial Engineer. Itay earned his MSc in Management from the London School of Economics, and has been awarded the Certificate in Quantitative Finance from the CQF Institute.

Steven Diamond is a research scientist at Gridmatic, an energy startup. He holds a Ph.D. in computer science from Stanford, where his research focused on domain-specific languages (DSLs) for optimization. He is the original developer of CVXPY, a widely used Python-embedded DSL for convex optimization. He has served as the primary instructor of Stanford's convex optimization course, and has run tutorials on convex optimization across the world. He has given many talks on CVXPY, including at SciPy18.

I am currently a PhD student working on an open source interactive interface for cell segmentation and tracking optimisation. I work part time as a software engineer making contributions to open source software, and am a napari core developer.

I am passionate about scientific software, open source development and open research. I love sharing my knowledge with others and making software development accessible for all.

I am an Economist turned programmer and about to finish my PhD at University of Bonn. I love building complex models of human behavior to analyze policy relevant questions.

I have authored several open source Python packages. The most popular is estimagic, a library for numerical optimization and calibration of scientific models.

In my free time I build pizza ovens, bandsaws or furniture and enjoy long bike rides.

Jennifer Glick is a quantum applications researcher and technical lead of quantum prototypes at IBM Quantum where she researches, develops, and implements quantum algorithms and software that can address industry-relevant problems challenging to solve with today’s classical techniques. Jen is a contributor to the first demonstrations of Qiskit Runtime, a platform that enables execution of more complex quantum workloads. She is regularly involved in teaching quantum computing at community and industry events, including programming sessions at IBM THINK conferences, the Qiskit Global Summer School on Quantum Machine learning, and a tutorial on Qiskit Runtime at IEEE Quantum Week 2021. In 2020, she was selected for MIT Technology Review’s global 35 under 35 Innovators for her work on developing applications of quantum computing. Jen received her Ph.D. in physics from Michigan State University in 2017 and her B.S. in engineering physics from Colorado School of Mines in 2011.

Perry Greenfield got his Ph.D. in Physics from M.I.T. in Radio Astronomy studying the first discovered gravitational lens with the Very Large Array. He has been at the Space Telescope Science Institute for 36 years and is currently a Consulting Science Software Engineer. He and his group started using Python in 1998 and pioneered the use of Python in astronomy, being involved in key developments such as PyRAF, pyfits, numarray, matplotlib, and Astropy. Python plays a central role in the science software for the Hubble and James Webb Space Telescopes, and the future Nancy Grace Roman Space Telescope.

Zac is the lead maintainer of the Hypothesis project, a PSF Fellow, and generally enjoys contributing to open-source communities. Earlier this year he moved from Australia to SF, where he works as a research engineer at https://anthropic.com, helping to build reliable, interpretable, and steerable AI systems.

When away from a computer - it happens! - Zac can often be found in the great outdoors, or relaxing with a good book and a block of dark chocolate.

Scott is a research scientist at the University of Washington eScience Institute and Department of Earth and Space Sciences. He works on a number of NASA-funded projects to support open source software such as Xarray. Scott is excited about open science initiatives, scalable computing with large public satellite data archives, and living in Seattle, WA.

Alex Kaszynski

Co-Creator of PyVista, incessant coder, and leader within Ansys to embrace open-source development and the Python ecosystem.

Eli Knaap is a research scientist and Associate Director of the Center for Geospatial Sciences at the University of California-Riverside, with interests in spatial data science, urban dynamics, and neighborhood effects. He is a core developer for the Python Spatial Analysis Library (PySAL) and QuantEcon

I am a former epidemiology researcher who has spent about 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.

Since leaving the academic world, I've been loving my second life in the tech industry as a 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 and modeling to a non-technical audience.

I'm also a contributor in the open-source community, being the author and maintainer of the causal-curve python package.

I make a very good scrambled eggs and hash brown breakfast. I mean really, really good. Really. 🍳

Interested in scientific computing and visualization with computer graphics.
Developer team member of PyVista.
Past experience as a speaker:
- [PyConJP 2019 speaker "Introduction to FEM Analysis with Python"](https://youtu.be/6JuB1GiDLQQ)
- [PyConJP 2020 speaker "How to plot unstructured mesh file on Jupyter Notebook"](https://youtu.be/X3Z54Kw4I6Y)
- [SciPy Japan 2020 speaker "Translation Project of Mayavi2 documents"](https://youtu.be/epxm9SjLMS0)
- [PyConJP 2021 speaker "Visualize 3D scientific data in a Pythonic way like matplotlib"](https://youtu.be/ru-nENLgleo)

Kevin is a Canadian astronomer, tiny satellite enthusiast, and software engineer at Planet, where he develops tools to empower fellow scientists and developers to explore the Earth. Having worked in both academia and tech, Kevin has over 7 years of public speaking experience at conferences around the world and has published 25 peer-reviewed papers. He has a wide variety of teaching experience as a university teaching assistant, college mathematics educator, and planetarium presenter. His most recent experiences include speaking at the Canadian Astronomical Society Conference 2019, and teaching a hands-on Python workshop at L3Harris Engineers Week 2021. Portfolio website: https://www.lacaille.dev/

Dr. Haw-minn Lu is currently a Principal Data Scientist for Data Science/Machine Learning at West Health Institute in La Jolla, a nonprofit medical research organization. Dr. Lu earned his PhD in 1998 from the Electrical and Computer Engineering Department at the University of California, San Diego after receiving SM and SB degrees from the Massachusetts Institute of Technology.

Dr. Lu has been doing machine learning for over 25 years. His interests include machine learning, interactive visualization, data imputation/anonymization, and computing infrastructure.

Prior to joining West Health, Dr. Lu was involved in several startups using Python as the core infrastructure for applications such as ecommerce, network communications, and digital animation.

Dr. Lu is an instructor of machine learning at the University of San Diego

I'm a software engineer at Anaconda, a Jupyter distinguished contributor, a member of the Jupyter Widgets steering council and the NumFOCUS DISC committee.

I'm a person driven by meaning and I find a lot of significance and love in libre software and the internet and I love to work with it. The concept of liberating knowledge and nurturing cultural and scientific development deeply resonates with many of my personal goals and working towards making the digital world more accessible and comfortable for all is a very fulfilling mission to me.

I'm a PhD candidate in economics at the University of Bonn, currently working on topics related to computational econometrics. My projects range from contributing to optimization libraries to implementing statistical methods or models of human behavior. In all cases, I try to develop software that is easy to use and extend. Besides that, I'm a big advocate for reproducibility and the open-source philosophy, which I try to support by being an active member of the Open Source Economics initiative.

Ido Michael co-founded Ploomber to help data scientists build faster. He'd been working at AWS leading data engineering/science teams. Single handedly he built 100’s of data pipelines during those customer engagements together with his team. He's a MS graduate from Columbia University in NY. He focused on building Ploomber after he constantly found that projects dedicated about 30% of their time just to refactor the dev work (prototype) into a production pipeline.

Riley holds a PhD in Computing and Mathematical Sciences from Caltech. His thesis work on convex relaxation methods for nonconvex optimization received Caltech's 2021 Amori Doctoral Prize. He is currently a postdoc at UC Berkeley, where he leads the development of BLAS- and LAPACK-like libraries for randomized numerical linear algebra. He started contributing to CVXPY in 2018 and now serves as one of CVXPY's four project maintainers.

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.

Jim Pivarski was trained as a particle physicist with a Ph.D. from Cornell and helped commission the CMS experiment at the Large Hadron Collider (LHC). He then worked as a data scientist at Open Data Group for 5 years. In 2016, he joined Princeton as a computational physicist, where he develops software tools for data analysis, including Awkward Array, which manipulates arrays of data structures.

Prabhu Ramachandran is a faculty member at the department of aerospace
engineering, IIT Bombay. His research interests include particle methods for
computational fluid dynamics, scientific computing, parallel and
high-performance computing, and applied scientific data visualization.

He has been actively involved with the FOSS and Python communities for the
past two decades. He is an active member of the SciPy community and develops
many Python-based open source packages that are useful in the context of
scientific computing. He is the creator of the Mayavi Python package for 3D
visualization (https://github.com/enthought/mayavi). He is a PI of the FOSSEE
project (https://fossee.in) through which he has led the development of tools
and material to spread the adoption of Python. He has been a nominated fellow
of the Python Software Foundation since 2010. He was awarded the Kenneth
Gonsalves award by the Python Software Society of India and PSF in 2014. For
more information see https://www.aero.iitb.ac.in/~prabhu

Jonathan Rocher is principal software architect at KBI Biopharma Inc. He earned a PhD in Theoretical Physics from the university of Paris, and currently leads, designs and/or builds scientific applications and data science tools to streamline and improve the drug manufacturing processes at KBI. In all, he has been building mid to large size scientific applications using the Enthought Tool Suite for more than 10 years at KBI and before that at Enthought Inc for mid to large size companies in biotech, oil and gas, and other industries. He is also a passionate supporter, teacher and contributor to open-source scientific Python, including a regular SciPy conference contributor.

Dr. Jessica Scheick is a glaciologist, open-source software developer, remote sensor, and lifelong lover of snow, winter, and the outdoors. She is currently a Research Assistant Professor at the University of New Hampshire, where she works on glaciological research, research-enabling open-source software projects, and climate justice and diversity, equity, and inclusion initiatives. She is the lead developer and maintainer for the icepyx library, an Xarray user and contributor, and regularly runs Hackweeks with the University of Washington's eScience Institute. When she's not working, you can usually find Jessica outside.

My educational background is in finance and economics and I'm currently pursuing a Ph.D. in financial econometrics at the Ludwig Maximilian University of Munich. My research mostly focuses on portfolio optimization, time series analysis, and deep learning. As part of my Ph.D., I am currently a visiting student researcher at Stanford University. I am also working as a Financial Engineer at Scalable Capital, a German robo-advisor. Generally, I am enthusiastic about finance, optimization, and technology.

I am currently working on the NetworkX open source project (work funded through a grant from Chan Zuckerberg Initiative!) Also collaborating with folks from the Scientific Python project (Berkeley Institute of Data Science), Anaconda Inc and GESIS, Germany. Before this I used to work on the GESIS notebooks and gesis.mybinder.org.
I am also interested in the development and maintenance of the open source data & science software ecosystem. I try to help around with the Scientific Open Source ecosystem wherever possible. To share my love of Python and Network Science, I have presented workshops at multiple conferences like PyCon US, SciPy US, PyData London and many more!

Mansi Shah is a data scientist, flutist, and environmentalist whose work explores the spaces where artistic rigor and technical curiosity collide. She believes data (like art!) imitates life, and is interested in the profoundly human stories behind the numbers and notes. Mansi has over 10 years of experience as an artist and performer, connecting to diverse audiences through words and music. In addition to her work as a flutist, Mansi has performed in plays, directed ensembles, hosted concerts, and given lectures on topics ranging from creative applications of cloud native geospatial technologies to celebrating diversity in the arts. Her recent project Counterpoints premiered in early 2022 at the Data Through Design exhibition in NYC, and brings together her geospatial and musical perspectives. Mansi works for Planet Labs as a software engineer, helping make the power of daily global satellite imagery more accessible to all.
Website: https://www.iammansi.com/

Julia is the Head of Open Source at Saturn Cloud, a data science platform that enables simple and customizable access to Jupyter and Dask on AWS. Julia has worked in the scientific Python ecosystem as an environmental researcher, an open source contributor, and on both the frontend and backend data science platform product teams. In her current role at Saturn Cloud, Julia works on the engineering team and contributes to the maintenance of Dask. Before joining Saturn, Julia worked on the HoloViz team at Anaconda and as a hydrometeorology researcher at Princeton University.

Dr. Adam Symington is a geospatial data scientist, leading the data science team at Geollect Ltd in the United Kingdom. Over the last two years he has developed exceptional geospatial data science skills and worked on projects with a variety of data sources across multiple sectors. Adam regularly publishes geospatial data science tutorials on towards data science 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. Adam has published 17 peer reviewed research papers, including 3 open-source software papers. During Adams time in academia, he taught Python programming to undergraduates and postgraduate students and wrote his own Python programming course which he delivered to show students how to apply Python to their data analysis.

Logan is a Senior Digital Transformation (DTX) Services Consultant and Instructor at Enthought. In the past, he has worked as a data scientist and machine learning engineer in both the digital media and protective engineering industries. His experience includes discovering relationships in large datasets, synthesizing data to influence decision making, and creating/deploying machine learning models. Logan enjoys learning new things and sharing his knowledge with others. Outside of work, he enjoys staying active, spending time with his family, and brewing a good cup of coffee.

Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source chatbot automation framework Opsdroid in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.

Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source chatbot automation framework Opsdroid in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.

Siddhant is a Scientific Software Developer at Enthought where he writes software that empowers scientists. He holds a Ph.D. in Computational Science from the University of Texas at Austin, and a B.Tech. in Mechanical Engineering from Indian Institute of Technology, Kharagpur.

Currently, I am a postdoctoral researcher, advised by Dagmar Iber in the Department of Biosystems Science and Engineering at the ETH Zürich. I am writing software for microscopy analysis in support of the lab’s goal of understanding the molecular mechanisms that underpin morphogenesis. Additionally, I am a core developer of napari.

Jessie Yu is a senior software enginner and prolific inventor at IBM. Prior to working on quantum computing, Jessie’s career was mainly in the area of IBM mainframe kernel development and analytics software. Her experience in IBM Quantum began in 2018 where she first worked on systems and infrastructure support and later took over as maintainer for qiskit-ibmq-provider, a framework that provides access to IBM Quantum devices and services. She is now the product owner of the quantum backend software. Throughout her career she has presented at various conferences, workshops, and customer engagements.