After working in a crime lab, Dr. Burks returned to academia, teaching, and forensic science research. Her research team is focused on the development of colorimetric and luminescent sensing systems with integrated image and chemometric analysis for forensic applications. She is co-creator of the Digital Imaging and Vision Applications in Science (DIVAS) project which created a pedagogical and programmatic "onramp" that empowers natural science majors to engage in authentic computational problems as members of community of practice.
Beyond the bench, Dr. Burks is a popular science communicator appearing regularly on TV, radio, podcasts, and print outlets. Most recently, she was a series regular in the Smithsonian Channel show "The Curious Life and Death Of..." and writes a science-meets-true crime column called “Trace Analysis” for Chemistry World. In 2020, she was awarded the American Chemical Society's Grady - Stack Award for Interpreting Chemistry for the Public.
Central to Dr. Burks' research, teaching, and service is the central tenet that equitable, diverse, and inclusive spaces and practices both respect people and produce scientific outcomes of greater integrity. She is a member of several local, national, and international committees, task forces, and projects focused on social justice and STEM.
Fernando Pérez is an Associate Professor in Statistics at UC Berkeley and a Faculty Scientist in the Department of Data Science and Technology at Lawrence Berkeley National Laboratory. His research focuses on creating tools for computational research and data science across domain disciplines, with an emphasis on high-level languages, interactive and literate computing, and reproducible research. He is particularly interested in the intersection of physical models and machine learning in geoscience, to address critical problems such as climate change and environmental protection. He created IPython while a graduate student in 2001 and co-founded its successor, Project Jupyter. In 2021, IPython/Jupyter was named by Nature one of the "Ten computer codes that transformed science".
Pérez is a co-founder of the 2i2c.org initiative, the Berkeley Institute for Data Science and the NumFOCUS Foundation. He is a National Academy of Science Kavli Frontiers of Science Fellow and a member of the Python Software Foundation. He is a recipient of the 2017 ACM Software System Award and the 2012 FSF Award for the Advancement of Free Software. He holds a PhD in physics from the University of Colorado, Boulder, and did postdoctoral research in applied mathematics at the same institution.
Jet Propulsion Laboratory
Michael Starch has been a software engineer at NASA's Jet Propulsion Laboratory for over a decade. In that time he has designed and built cloud-scale data processing systems, engineered flight control software, and advocated open source software development. Most recently he was the Mars Helicopter Downlink and Tools lead for Ingenuity's first powered flight on another planet. He also functions as the cognizant engineer and community manager for the open source F′ embedded systems framework used on a number of spacecraft including Ingenuity itself. Underpinning all this is a love of automation and a healthy dose of Python.
Michael graduated with a Bachelors in Computer Engineering from the University of Michigan College of Engineering in Ann Arbor, Michigan. In his free time, Michael mentors students at a local high school, helps organize local technical organizations, and loves to talk anything tech.
Dr. Theresa Jean Tanenbaum (“Tess”) is a game designer, artist, activist, and professor in the Department of Informatics at UC Irvine where she is a founding member of the Transformative Play Lab (https://transformativeplay.ics.uci.edu/). Dr. Tanenbaum’s work is playful, provocative, and interdisciplinary, frequently straddling the line between art, design, and research. She is an expert in the design and study of digital storytelling systems that produce experiences of role-taking and identity shift by drawing on techniques from theater and the performing arts. Her work seeks to create possibilities for social and individual change by highlighting how the identities that we inhabit in the world are contingent and negotiated. These experiences of transformative theatrical play create possibility models that are emancipatory, allowing oppressed and marginalized people to inhabit new identities that create possibilities where there were none before and reclaim power and agency denied to them. She has been instrumental in helping create new, more inclusive, policies within the academic publishing world that make it possible for people to correct their names on previously published scholarship. She has worked with COPE, the ACM, SAGE, Springer, Taylor & Francis, Elsevier, and many other publishers to develop identity practices in publishing that safeguard the privacy of transgender authors seeking to update their scholarly records to reflect their correct names. She is the founder of the Name Change Policy Working Group (https://ncpwg.org) and serves as the VP for Publications for ACM SIGCHI, and on the board of the Association for Research Into Digital Interactive Narratives (ARDIN) as the director of EDI initiatives.
Jim Bednar is the Director of Technical Consulting 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 and about software development. Dr. Bednar manages the open source Python projects HoloViz, Panel, hvPlot, Datashader, HoloViews, GeoViews, Param, Lumen, and Colorcet. Before Anaconda, Dr. Bednar was a lecturer and researcher in Computational Neuroscience at the University of Edinburgh, Scotland, as well as a software and hardware engineer at National Instruments.
Jim 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 and about software development. Dr. Bednar manages the open source Python projects HoloViz, Panel, hvPlot, Datashader, HoloViews, GeoViews, Param, Lumen, and Colorcet. Before Anaconda, Dr. Bednar was a lecturer and researcher in Computational Neuroscience at the University of Edinburgh, Scotland, and a hardware engineer working on data acquisition at National Instruments.
Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science seamlessly. He has extensive experience as a data scientist, educator, evangelist, content marketer, and data strategy consultant at 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.
Daniel Chen is a PhD candidate at Virginia Tech studying data science education in the biomedical sciences.
He holds an MPH in Epidemiology and specializes in research design, analysis, and teaching scientific computing with an emphasis on R, Git, Python and Linux. Daniel is the author of Pandas for Everyone, the Python/Pandas complement to R for Everyone.
Minnesota State University Moorhead
I have been a Professor of Physics and Astronomy at Minnesota State University Moorhead since 1999. I began doing scientific programming around 1988 and jumped into Python in 2013. Since then, I've worked on astropy and some of its affiliated packages, ipywidgets and ipyevents, and helped with some of the early work on conda-forge. I attended my first SciPy in 2014 and was introduced to IPython widgets in one of the first tutorials I attended.
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.
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.
Miro Dudik is a Senior Principal Researcher in machine learning at Microsoft Research NYC, where he focuses on algorithmic fairness, reinforcement learning, and contextual bandits. He received his PhD from Princeton in 2007. He is a co-creator of the Fairlearn toolkit for assessing and improving the fairness of machine learning models and of the Maxent package for modeling species distributions, which is used by biologists around the world to design national parks, model impacts of climate change, and discover new species.
Australian National University
I'm an interdisciplinary researcher at ANU, currently working on a PhD in computer science - based on my open-source work on Hypothesis and property-based testing. I've previously spent several years working in cybernetics (research and education), and as a research software engineer in remote sensing and earth sciences, so it's super exciting to see so many scientists and maintainers adopting my testing tools!
When I'm not on the internet, you can probably find me either curled up with a good book and a lot of chocolate, or far out of contact along a deserted beach or deep in the Australian bush.
University of Washington
Lisa Ibanez is a Senior Research Scientist in the Department of Psychology at the University of Washington. She received her PhD in Applied Developmental Psychology in 2010. Over the last decade, she has focused on generating and evaluating solutions for healthcare transformation to reduce disparities and increase accessibility using human-centered design and machine learning. Her translational research has included validation studies and pragmatic randomized control trials (RCTs), which have evaluated the effectiveness of screening tools and treatments for pediatric mental health.
Adrin is a trained bioinformatician who now works in the algorithmic privacy and fairness team at Zalando and focuses on unwanted biases in algorithms. He's also a maintainer of scikit-learn and fairlearn.
Eric is a Principal Data Scientist at Moderna. Prior to Moderna, he was at the Novartis Institutes for Biomedical Research conducting biomedical data science research with a focus on using Bayesian statistical methods in the service of making medicines for patients. Prior to Novartis, he was an Insight Health Data Fellow in the summer of 2017 and defended his doctoral thesis in the Department of Biological Engineering at MIT in the spring of 2017.
Eric is also an open-source software developer and has led the development of pyjanitor, a clean API for cleaning data in Python, and nxviz, a visualization package for NetworkX. In addition, he gives back to the open-source community through code contributions to multiple projects.
His personal life motto is found in the Gospel of Luke 12:48.
Michael is a postdoctoral researcher at Microsoft Research working with the FATE (Fairness, Accountability, Transparency, and Ethics in AI) research group. He works at the intersection of human-computer interaction and AI/ML, focusing on enabling more fair and responsible AI through research with AI practitioners and stakeholders impacted by AI systems. Michael received his PhD in Human-Computer Interaction from Carnegie Mellon University.
Mariana is a software developer who deeply cares about the impacts that technology has in the world and tries her best to be the change she wants to see by contributing to open source projects that stand upon libre and diverse standards.
She currently works at QuantStack expanding the Jupyter ecosystem with new libraries and functionalities. She's the creator of SQL-kernels for Jupyter, the cytoscape Jupyter widget, Rhumba the fast R package-manager and a contributor for projects like Mamba and Memestra.
Manojit Nandi is a Senior Data Scientist at Microsoft Research NYC. He works on the Fairlearn library for assessing and improving fairness in machine learning models and aims to understand the challenges data scientists face when incorporating fairness practices into their workflow. He has spoken at multiple conferences about fairness, explainability, and responsible AI.
Ramon works as a data scientist and educator at Coder Academy (CA) in Sydney and, most recently, he was a python and statistics instructor at London Business School and a research associate at INSEAD. He currently spends most of his time developing the content for CA's data science bootcamp from scratch. In his previous data-related roles, he worked at the intersection of education, data science, development economics, consumer behavior, and research in the areas of entrepreneurship and strategy in both, professional and academic settings. In his spare time, he enjoys mountain biking, playing baseball, and exploring many of the outdoor wonders Australia has to offer.
Martin Renou is a Scientific Software Developer at QuantStack. Prior to joining QuantStack, Martin also worked as a Software Developer at Enthought. He studied at the French Aerospace Engineering School ISAE-Supaero, with a major in autonomous systems and programming.
As an open-source developer, Martin has worked on a variety of projects, such as ipygany (a 3-D mesh visualization library for the Jupyter Notebook) and ipympl (an interactive Matplotlib backend for Jupyter)
GESIS – Leibniz Institute for the Social Sciences
Currently working on the GESIS notebooks project, making it easier for researchers to share their work.
I am also interested in the development and maintenance of the open source data & science software ecosystem. I try to help around with some projects like NetworkX and Econ-ARK. I have also presented tutorials at various conferences to spread my love of python and network science.
In a previous life, I used to dabble in academic network science research at UCLouvain, Oxford, Sorbonne.
Chan Zuckerberg Initiative
Nicholas Sofroniew leads the Imaging Tech Team at the Chan Zuckerberg Initiative, a Science focused philanthropy. He also maintains napari, a Python image visualization and analysis platform.
Logan is a Scientific Software Developer & Python Trainer at Enthought. Prior to becoming a technical trainer, he worked as a data scientist and machine learning engineer in both the digital media and protective engineering industries. He holds a M.S. in mechanical engineering with a minor in statistics from the University of Florida and a B.S. in mathematics from Palm Beach Atlantic University.
Logan enjoys learning new things but is even more passionate about sharing his knowledge with others. Outside of work, he enjoys staying active, spending time with his family, and brewing a good cup of coffee.
Hanna Wallach is a Senior Principal Research Manager at Microsoft Research NYC, where she focuses on issues of fairness, accountability, transparency, and ethics as they relate to AI and machine learning. Hanna has 20 years of experience developing machine learning and natural language processing methods for analyzing social processes. She was named one of Glamour magazine’s “35 Women Under 35 Who Are Changing the Tech Industry” in 2014 and won the CRA-WP Borg Early Career Award in 2016. Hanna is committed to increasing diversity in computing and co-founded several initiatives to address the underrepresentation of women in machine learning and in free and open-source software development.
Eindhoven University of Technology
Hilde Weerts is a data scientist, researcher, and software engineer who is passionate about responsible data science. She has a background in industrial engineering and computer science. As an AI engineer, she works on research, tools, and education in the field of fair and explainable machine learning.
Conference Speakers & Authors
ETH Zurich's Center for Law & Economics
Elliott Ash is Assistant Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics, Switzerland. Prior to joining ETH, Elliott was Assistant Professor of Economics at University of Warwick, and before that a Postdoctoral Research Associate at Princeton University’s Center for the study of Democratic Politics. He received a Ph.D. in economics and J.D. from Columbia University, a B.A. in economics, government, and philosophy from University of Texas at Austin, and an LL.M. in international criminal law from University of Amsterdam.
Niels is a machine learning engineer and core maintainer of Flyte, an open source ML orchestration tool and author and maintainer of Pandera, a data testing tool for dataframes.
He has a Masters in Public Health with a specialization in sociomedical science and public health informatics, and prior to that a background in developmental biology and immunology.
His research interests include reinforcement learning, AutoML, creative machine learning, and fairness, accountability, and transparency in automated systems. He enjoys developing open source tools for improving data science and machine learning practice.
Genevieve Buckley is a scientist and programmer based in Melbourne Australia. She builds software tools for scientific discovery. Her interests include deep learning, automated analysis, and contributing to open source projects. She has a wealth of professional experience with image processing and analysis, spanning x-ray imaging, fluorescence microscopy, and electron beam microscopy. She is a maintainer for the dask-image project, and the napari image viewer.
Colin Carroll is a software engineer at Google Research. In this role he focuses Bayesian computation and research, and contributes to a number of open source libraries, including TensorFlow Probability, PyMC3, and ArviZ. He received his PhD in mathematics from Rice University, where he researched geometric measure theory.
Academy for Mathematics, Science, and Engineering
Thomas Chen is a student researcher whose primary interests lie in machine learning and computer vision. He serves on the U.S. Technology Policy Committee of the Association for Computing Machinery. As much of his work lies at the nexus of artificial intelligence and earth science, he is also an active early-career scientist member of the European Geosciences Union and the American Geophysical Union. He enjoys using the Python programming language to conduct research that has real-world impacts. Previously, Thomas has presented work at a number of conferences, workshops, and meetings, from NeurIPS workshops, to Applied Machine Learning Days, to the Open Data Science Conference, to Machine Learning Week Europe.
Brendan is an expert in data science and geospatial technology and is Co-founder and Principal at makepath, a spatial data science firm in Austin, Texas. With over 15 years of experience in GIS, he has worked with The Nature Conservancy, World Resources Institute, New Forests, World Wildlife Fund, NASA, and The Bill and Melinda Gates Foundation. Brendan is actively involved in the PyData community and is a core developer for several open source libraries including Datashader and Bokeh. He recently created the Xarray-Spatial and mapshader libraries for large scale spatial analysis.
Analog Devices Inc
Andrei Cozma is an engineering manager at ADI, supporting the design and development of system-level reference designs. He holds a B.S. degree in industrial automation and informatics and a Ph.D. in electronics and telecommunications. He has been involved in the design and development of projects from different industry fields such as motor control, industrial automation, software-defined radio and machine vision.
Imperial College London
Nataraj has 21 years of industry experience in developing the vision, strategy and execution of analytic capabilities in finance and pharmaceutical domains. Experience at IBM, UBS Investment Bank, UBS Wealth Management, Purdue Pharma, Philip Morris. He was the core architect of the RWE and Rx Data Analytics solution at Purdue Pharma which was spun out into a new startup, RxDataScience with over $ 3.5M in seed funding. He currently serves as the VP of Advanced Analytics at the firm.
Nataraj is a published author of multiple books on data science, journal articles and research papers in commercial market research, health outcomes, epidemiology and RWE Analytics. He has been a presenter, keynote speaker and Chairperson at over 20+ machine learning and AI-related healthcare conferences in US, Europe and Asia.
University of Michigan
Bradley Dice is a scientist and software developer researching nano-scale materials via computational simulations. He is currently a PhD candidate in Physics and Scientific Computing at the University of Michigan in Ann Arbor. He is a member of the Glotzer Group, a National Science Foundation Graduate Research Fellow, and a MolSSI Software Fellow.
University of Chicago
James Evans is Professor of Sociology, Director of Knowledge Lab, and Faculty Director of Computational Social Science at the University of Chicago. He is also an External Professor at the Santa Fe Institute. His research uses large-scale data, machine learning, and generative models to understand how collectives think and what they know. This involves inquiry into the emergence of ideas, shared patterns of reasoning, and processes of attention, communication, agreement, and certainty. He is especially interested in innovation—how new ideas and practices emerge—and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery.
Thomas J. Fan is a Senior Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library. He leads the scikit-learn project by designing the project’s roadmap, giving feedback to contributors, and implementing new features. Fan also is a maintainer for skorch, a scikit-learn compatible library that wraps PyTorch. He has a Masters in Mathematics from NYU and a Masters in Physics from Stony Brook University. During his academic studies, Fan researched quantum computation and condensed matter physics.
Ralf has been deeply involved in the SciPy and PyData communities for over a decade. He is a maintainer of NumPy, SciPy and data-apis.org, and has contributed widely throughout the SciPy ecosystem. Ralf is currently the SciPy Steering Council Chair, and he served on the NumFOCUS Board of Directors from 2012-2018.
Ralf co-directs Quansight Labs, which consists of developers, community managers, designers, and documentation writers who build open-source technology and grow open-source communities around data science and scientific computing projects. Previously Ralf has worked in industrial R&D, on topics as diverse as MRI, lithography and forestry.
University of Minnesota Duluth
I obtained my PhD in Mechanical Engineering from the University of Minnesota. After six years in industry working as a design engineer at Seagate Technology, I transitioned to academia and am currently an Associate Professor at the Duluth Campus of the University of Minnesota. Throughout my career, I've benefited from using the tools provided by the SciPy ecosystem and have taught my students these tools as well. More recently, I've made contributions to the SymPy, Pyodide, and CadQuery Python projects. My recent research efforts involve leveraging the scientific Python toolset in the browser using Pyodide to create powerful engineering tools that can be run anywhere.
Predrag is a system architect at Kensho in Boston, working on data infrastructure and developer productivity tools. As part of adopting Python type hints in one of Kensho's large open-source projects, Predrag built typing_copilot to address the pain points maintainers experienced in the process. In addition to high-performance systems, he also loves to experiment with new compiler and database technologies. Outside of work, Predrag enjoys playing ice hockey and board games.
University of Florida Genetics Institute
Genetics and genomics Ph.D. student in Eric T. Wang's lab at the University of Florida Genetics Institute. Interested in subcellular RNA localization and repeat expansion diseases. Developing quantitative methods for analysis of single molecule RNA imaging by fluorescence microscopy. Applying Markovian stochastic models to study motion of RNAs in diverse cellular environments.
Lauren Klein is an associate professor in the departments of English and Quantitative Theory & Methods at Emory University, where she also directs the Digital Humanities Lab. Before moving to Emory, she taught in the School of Literature, Media, and Communication at Georgia Tech. Klein works at the intersection of digital humanities, data science, and early American literature, with a research focus on issues of gender and race. She is the author of An Archive of Taste: Race and Eating in the Early United States (University of Minnesota Press, 2020) and, with Catherine D’Ignazio, Data Feminism (MIT Press, 2020). With Matthew K. Gold, she edits Debates in the Digital Humanities, a hybrid print-digital publication stream that explores debates in the field as they emerge. Her work has appeared in leading humanities journals including PMLA, American Literature, and American Quarterly; and at technical conferences including NACCL, EMNLP, and IEEE VIS. Her work has been supported by grants from the NEH and the Mellon Foundation.
Zoufiné Lauer-Baré obtained his PhD in applied mathematics in 2014 at the TU Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany.
Zoufiné Lauer-Baré worked two years in the data and workflow management in the Crash test simulation context in Ingolstadt, Germany.
Currently, he works as simulation engineer in the development of electro hydraulic valves at Hilite International, using analytical, numerical and machine learning techniques in Nürtingen, Germany.
Zoufiné is also an external lecturer at the Baden Württemberg Corporate State University, where he teaches programming with python and foundations of mathematics for data science students, as well as the Finite Element Method for engineering students.
Sean Law is a senior applied scientific researcher and lead data scientist currently working with a multi-talented R&D team at Charles Schwab. He has experience producing cutting edge methodologies, building high-performance predictive models, and developing rapid prototypes. Additionally, he is one of the co-organizers of PyData Ann Arbor and is also the creator and core maintainer of STUMPY, a powerful and scalable open source Python library that can be used for a variety of time series data mining tasks.
S. Shayan Mousavi M.
Shayan Mousavi is a Materials Science and Engineering Ph.D. student at McMaster University. He holds a B.Sc. degree in Materials Science and Engineering from the Sharif University of Technology with a secondary concentration in Theoretical Mathematics. In his research, he investigates the photonic and optoelectronic properties of materials using various electron microscopy and spectroscopy techniques conducted at the Canadian center for electron microscopy (CCEM) and the Canadian light sources synchrotron facility (CLS).
He implements a wide range of spectral analysis methods including machine learning- and deep learning-based techniques to refine, extract, and generate information from spectral data.
Analog Devices Inc.
Dan Nechita is a software engineer at ADI, working on a software library for interfacing with Time-of-Flight cameras. He holds a bachelor’s degree in electronics from Technical University of Cluj-Napoca.
Laura K. Nelson is a scholar of social movements and culture, computational sociologist, automated text analysis methodologist, and an open source, open science, reproducible research enthusiast. She is currently an assistant professor of sociology at Northeastern University, where she is a faculty affiliate at the Network Science Institute, she is core faculty at NULab for Texts, Maps, and Networks, and serves on the executive committee of the Women's, Gender, and Sexuality Studies Program. She has published or has work forthcoming in Sociological Methods and Research, Gender & Society, Mobilization, Poetics, and the American Journal of Sociology, among other outlets. She will soon be joining the sociology department at the University of British Columbia as an assistant professor.
Robert Nishihara is one of the creators of Ray, a distributed system for scaling Python and machine learning applications. He is one of the co-founders and CEO of Anyscale, which is the company behind Ray. He did his PhD in machine learning and distributed systems in the computer science department at UC Berkeley. Before that, he majored in math at Harvard.
Baudouin Raoult is currently Principal Software Architect. He joined the ECMWF in 1989. Since then, he has been responsible for the architecture and the development of ECMWF meteorological archive system, a multi-petabyte repository of meteorological data. He has also led several key ECMWF software developments, including the implementation of the Copernicus Climate Data Store, a cloud-based distributed climate data repository that also provides users with Python based analytics tools.
Dr Iza Romanowska
Fellow at the Aarhus Institute of Advances Studies - AIAS, Denmark
Formerly at the Barcelona Supercomputing Center, Spain
Iza Romanowska a complexity scientist working on the interface between social sciences and computer science having originally trained and worked as a prehistoric archaeologist before switching to computer-based research. She specialises in agent-based modelling - a simulation technique she uses for various research questions, from mobility in prehistoric cities, the first Out-of-Africa human dispersal, to large-scale economic interactions across the Roman Meditteranean and real-time pedestrian flows in modern sports venues.
Karla is a Senior Research Software Development Engineer in the Gray Systems Lab (GSL) at Microsoft. She finished her PhD in Computer Science at the University of Maryland, College Park in 2015.
After graduating, Karla spent 3 years as a research scientist at Intel Labs and joined Microsoft in 2018. Her research interests are broad, and generally enjoys scalability and performance challenges related to systems infrastructure.
Henry Schreiner is a Computational Physicist / Research Software Engineer in High
Energy Physics at Princeton University. He specializes in the interface between
high-performance compiled codes and interactive computation in Python, in
software distribution, and in interface design. He has previously worked on
computational cosmic-ray tomography for archaeology and high performance GPU
model fitting. He is currently a member of the IRIS-HEP project, developing
tools for the next era of the Large Hadron Collider (LHC).
He is a maintainer/core developer for pypa/build, scikit-build, cibuildwheel,
pybind11, and plubmum for Python. He is an admin of Scikit-HEP, and a lead
designer on boost-histogram, hist, UHI, vector, Particle, and DecayLanguage
packages there. He is also the lead author of the Scikit-HEP developer pages
and scikit-hep/cookie. He is the primary author of CLI11, a C++ library used by
Microsoft terminal and many others. He is also the lead web developer for
IRIS-HEP. He is also the author of Modern CMake and a variety of CMake, GPU,
and Python training courses and classes.
Argonne National Laboratory
Adam is located at Argonne National Laboratory and works as the instrument operations manager for the Department of Energy Atmospheric Radiation Measurement (ARM) user facility. He has a M.S. in Atmospheric Science from the University of North Dakota where his experience in computer programming first start, moving on to positions as a systems engineer and a data analyst with ARM for eight years with ARM's. Data Quality Office before coming into the current position. His past experience in programming languages run the gamut from Fortran and C to paid programming platforms before recently converting to Python and the open source process.
National Energy Research Scientific Computing Center
Rollin is a Data Architect and Computational Systems Engineer at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory in Berkeley, California. The last 5+ years he has helped hundreds of users on three different supercomputers use Python and Jupyter effectively to make discoveries in all areas of science supported by the US Department of Energy's Office of Science. He has led the NERSC Application Readiness Program for Data, and is the chief architect of NERSC's Jupyter service. He likes making supercomputing more accessible to scientists, and helping them succeed with Python.
University of Amsterdam
Melvin Wevers is an Assistant Professor in Digital History at the University of Amsterdam. His research interests include computational historical methods, visual culture, advertisements, and historical newspapers. He has published on gender bias in historical newspapers, conceptual history and word embeddings, computer vision for historical images, and more recently, on the agency of computer vision models.
Poster Presenters & Authors
I am a software engineer at NVIDIA, working on Clara -- a healthcare application framework for AI-powered imaging, genomics, and for the development and deployment of smart sensors, previously worked in the Camera team as a camera software engineer for Android/Linux camera module on NVIDIA Jetson/Shield platform.
Before joining NVIDIA in 2015, I obtained a Ph.D. from Korea Advanced Institute of Science and Technology (KAIST) with a specialization in software testing and program analysis, with emphasis on the application of automated test case generation techniques to GUI software and unit-level code, and on empirical studies.
Biological Magnetic Resoance data Bank(BMRB)
Kumaran Baskaran is a scientist at Biological Magnetic Resonance data Bank (BMRB https://bmrb.io). He earned his PhD form University of Regensburg, Germany. He is an expert in NMR spectroscopy and structural biology. He is currently working on improving data visualization tools for BMRB and NMR structure validation project in collaboration with world wide Protein Data Bank.
Corri por Aí
I am Marcel Pinheiro Caraciolo, living in Recife, Brazil. I am computer engineer with undergoing MBA on Technology for Business in Artificial Intelligence, Data Science and Big Data and Specialization in Agile Project Management. I am author of the runpandas scientific library for running activities data analysis.
My interests are product management, bioinformatics computing, data science, sports analytics and productivity. When I am not coding or managing, I often run (training hard to be a marathonist) and build and rebuild one of my collection of Lego Architecture sets.
Daniel Chen is a PhD candidate at Virginia Tech studying data science education in the biomedical sciences.
He holds an MPH in Epidemiology and specializes in research design, analysis, and teaching scientific computing with an emphasis on R, Git, Python and Linux. Daniel is the author of Pandas for Everyone, the Python/Pandas complement to R for Everyone.
Alan Chin is currently a Software Engineer with IBM's CODAIT team in San Francisco. He is currently exploring the links between data scientists and AI Ops and how to make pipeline prototyping and deployments easier. He has a wide range of extracurricular interests and hobbies but mostly focused on 3D modeling and printing right now. He holds a B.S. in Computer Science from San Jose State University.
KLS Gogte Institute of Technology, India
I hold Post Graduate Diploma in Cyber Laws and Cyber Forensics from the National Law School of India University Bangalore. I have presented talks at many conferences including PyData Global, JuliaCon 2018 and 2020, PyCon FR/HK/TW/ID/TZ/AU, COSCUP Taiwan, FOSDEM 2021, FOSSASIA 2021, PyCon Africa, BuzzConf, EuroPython, PiterPy Russia, SciPy India. Worked as a Reviewer and Program Committee member for reputed International conferences including SciPy USA, SciPy Japan, JuliaCon, JupyterCon, PyData Global, and PyCon India, and publishers include Manning USA and Oxford Univesity Press. I am also a GitHub Certified Campus Advisor. I lead the PyData Belagavi chapter and the OWASP Belagavi chapter. I am working as CFP Co-Chair for PyCon India 2021.
Senior year student, pursuing a Bachelor's in Technology, major in Electronics. I have been using Deep Learning for medical imaging for AI research. I have been publishing research articles in the Biomedical image processing domain, using segmentation and analysis. Speaker in PyCon Poland 2020.
Laura is SeMI's Community Solution Engineer. She works on everything UX & DX related to the open-source vector search engine Weaviate. For example, she is responsible for the GraphQL API design, and researches new machine learning features for Weaviate. She is in close contact with Weaviate's open-source community. Additionally, she likes to solve custom use cases with Weaviate, and introduces Weaviate to other people by means of Meetups, talks and presentations at conferences. In her free time she's active in sports, traveling, photography and likes to teach programming to kids.
Ex-cosmologist turned data scientist with over 10 years experience in machine learning, statistical inference, and passionate about data insights visualisations. Result driven and highly detail oriented I’m motivated by intellectual challenges and love analysing data to communicate insights for better decisions within organisations.
My claim for fame is paying rent in four different continents within a span of a decade, including three tennis Grand Slam cities (NY, Melbourne, London).
IBM Center for Open Source Data and AI Technologies (CODAIT)
Romeo Kienzler is CTO and Chief Data Scientist of the IBM Center for Open Source Data and AI Technologies (CODAIT) in San Fransisco.
He holds an M. Sc. (ETH) in Computer Science with specialisation in Information Systems, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology Zurich.
He works as Associate Professor for Artificial Intelligence at the Swiss University of Applied Sciences Berne and Adjunct Professor for Information Security at the Swiss University of Applied Sciences Northwestern Switzerland (FHNW). His current research focus is on cloud-scale machine learning and deep learning using open source technologies including TensorFlow, Keras, and the Apache Spark stack.
Recently he joined the Linux Foundation AI as lead for the Trusted AI technical workgroup with focus on Deep Learning Adversarial Robustness, Fairness and Explainability.
He also contributes to various open source projects. He regularly speaks at international conferences including significant publications in the area of data mining, machine learning and Blockchain technologies.
Romeo is lead instructor of the Advance Data Science specialisation on Coursera https://www.coursera.org/specializations/advanced-data-science-ibm with courses on Scalable Data Science, Advanced Machine Learning, Signal Processing and Applied AI with DeepLearning
He published a book on Mastering Apache Spark V2.X (http://amzn.to/2vUHkGl) which has been translated into Chinese (http://www.flag.com.tw/books/product/FT363).
Recently, he published a book on "What's new in TensorFlow 2.x" with O'Reilly (https://learning.oreilly.com/library/view/whats-new-in/9781492073727/)
Romeo Kienzler is a member of the IBM Technical Expert Council and the IBM Academy of Technology - IBM’s leading brain trusts. #ibmaot
Joongi is the main author of Backend.AI and CTO in Lablup. He received Ph.D in Computer Science from KAIST by developing a GPU-accelerated packet processing framework offering world-first 80 Gbps performance. His main interests are analysis and design of scalable backend systems. He is also an open-source enthusiast; has contributed to a number of open source projects, such as Python, iPuTTY, Textcube, aiodocker, aiohttp, pyzmq, DPDK, and more.
I'm currently the head of data science at DrFirst, a health tech company based outside of Washington, DC.
Outside of my work, I enjoy bicycling, climbing, photography, reading, cooking, basketball, music collecting, traveling, and volunteering. I love talking to people who are passionate about "ideas."
Greg is a scientific software engineer at Quansight with a background in medical imaging (magnetic resonance imaging). He is a maintainer for the open source scikit-image, SciPy, PyWavelets and pyFFTW libraries. He also has an interest in GPU computing and has been a frequent contributor to CuPy.
Marc Skov Madsen
Marc Skov Madsen is the lead developer of awesome-panel.org. Awesome Panel is a site to demo how awesome HoloViz Panel and the Panel community is.
With Panel you and your team can easily build and share internal interactive visualizations, tools, dashboards and applications in Python using the tools you know and love.
Marc works as a Lead Trading Analysts at Ørsted, bringing self service and proprietary data & analytics to the Traders of Ørsted.
Marc has been contributing to and leading mathematically complex projects across operation, engineering, finance and trading in the Utility and Financial sectors since 2004.
Marc, PhD, CFA®
University of Oklahoma Health Sciences Center
Blaine Mooers is an Associate Professor of Biochemistry and Molecular Biology University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. He is also a member of the Stephenson Cancer Center and serves as director of the Laboratory of Biomolecular Structure and Function. He earned a Ph.D. in Biochemistry and Biophysics with Professor P. Shing Ho at Oregon State University. He was a Howard Hughes Medical Institute fellow with Professor Brian Matthews at the University of Oregon. As a post-doc, he worked on problems in protein structure and design and RNA structure. His research interests include the role of RNA structure in the RNA editing system in trypanosomes. His lab uses X-ray crystallography, small-angle X-ray scattering, and molecular dynamics simulations. He also collaborates with colleagues on crystallographic studies to develop better anti-cancer compounds. He has been using Python and Jupyter to aid the analysis of his data for the past seven years. His research interests include developing software to improve computational workflows in structural biology. A Warren L. DeLano Memorial PyMOL Open-Source Fellowship funded some of this work. The National Institutes of Health currently funds his research. Contact him at email@example.com.
University Hospital of Basel
Background in computer science, business and finance.
Worked for financial institutions on software engineering tasks, risk modelling and business transformation.
Joined a health institution to be part of to the technological transformation of medical domain in Switzerland, contributing to AI modelling and putting MLOps into practise.
Contributed to publications in all of the above mentioned fields.
Big supporter of open-source projects, but only a small contributor.
Having high hopes the latter one will change for the better.
Shubham Sharma is serving as Senior Remote Sensing Scientist at Geospoc, Bengaluru, India.For the past five years ,he has been working on utilisation of Python for satellite image processing applications and has worked on related projects with a renowned space organisation ISRO.He has also presented at leading Python conferences such as Pycon India and has mentored at conferences such as Scipy. In addition, he has taken workshops related to Python programming for Satellite image processing.He enjoys python programming and takes a great interest in outreach of Python programming amongst the community and exploring the scientific frontiers through Python.
Linkedin : https://in.linkedin.com/in/shubham-sharma-54685780
Pycon India 2018 video : https://www.youtube.com/watch?v=Yon0aJ4GBFA
Jeongkyu is the founder and CEO in Lablup Inc. He is known as the lead developer of Textcube Project, popular open-source web publishing platform in South Korea, which is open-source codebase of tistory.com and part of blogger.com. As one of the Korea’s foremost experts on open-source projects, he has participated in many successful ventures for the past 19 years.
Jeongkyu Received Ph.D in Statistical physics from POSTECH with topics of complex system, agent-based model and neuroscience studies. He leads ML-based / open source projects with several companies and laboratories, especially text classification, entropy-related information compression (for security) and context retrieval. Now he is developing Backend.AI, open-source distributed machine learning management platform for deep learning frameworks and scientific computation since 2015.
Jyotika Singh is the Director of Data Science at ICX Media, where she manages and mentors her team as they work on NLP, feature engineering, machine learning, data analytics, research, distributed computing, social media data and audiences data using Python and Spark. She is an inventor of a patent on data science, classification and reclassification algorithms, processes and optimizations for media and audience marketing campaigns. Her efforts in driving data science and analytics at ICX along with introduction of new methods and processes have strongly contributed to reducing operating costs, securing new clients and achieving high double digit revenue growth in the past year with positive EBITDA.
She earned her Bachelor's in Engineering Hons. in Electronics and Communications Engineering from Birla Institute of Technology and Science, Pilani, Dubai (BITS) where she published multiple papers on her research in Signal Processing and Communications and earned a silver medal for the entire graduating class of 2014 and 1st rank for the Electrical Engineering class of 2014. She then earned her Master's in Science from the University of California, Los Angeles (UCLA) where she researched on signal and speech processing, developed novel approaches to remove noise from speech and worked on a variety of machine learning projects on image, text, user ratings, social media, entertainment and movies data. Outside her work, she enjoys working on a variety of problem solving techniques on text, audio and image data. She has opened multiple github open source projects, such as pyAudioProcessing, and has been a speaker at multiple conferences across the globe to share her findings and work with the Python and Data Science community. She is passionate about encouraging women in STEM and continues mentorship efforts to support the topic.
In her free time, she is big on spending time with family and friends, painting, sketching, art & decor, and trying out different sports.
Manasi Vartak is the founder and CEO of Verta, an MIT-spinoff building software to enable high-velocity machine learning. The Verta platform enables data scientists and ML engineers to robustly version ML models, collaborate and share ML knowledge, and when models are ready for graduation, to deploy and monitor models in production environments. Verta grew out of Manasi's Ph.D. work at MIT on ModelDB, the first open-source model management system deployed at Fortune-500 companies. Manasi previously worked on deep learning for content recommendation as part of the feed-ranking team at Twitter and dynamic ad-targeting at Google.
Tobias de Jong
Leiden Institute of Physics
I am a physicist/material scientist/microscopist and I investigate electronic superstructures in Van der Waals materials, such as moiré patterns in twisted bilayer graphene as well as Charge Density Waves in Transition Metal Dichalcogenides, e.g. TaS2.
The main measurement tool for my research is a Low Energy Electron Microscope (LEEM), with which we are always trying to push the boundaries of what information can be extracted on the atomic and electronic structure of materials.
This means medium-large, just out of memory image and spectroscopic datasets, with its own domain-specific properties. Tools with which I try to analyse these datasets include, amongst others, Dask, Xarray, plain SciPy, scikit-image and scikit-learn.