I am a Ph.D. Candidate in the Department of Chemical Engineering at the University of Michigan, Ann Arbor. The research I conduct under Sharon C. Glotzer, is focused on data-driven design of novel soft matter materials.

As part of my research, I am the lead-developer of the open-source data management framework signac, supporting the scientific community in the application of best-practices in scientific computing. As a contributor to the general-purpose particle simulation toolkit HOOMD-Blue, I strive to make signac highly integrable with compute-intensive simulation workflows on leadership-class supercomputers.

Fruzsina is in her third year of a PhD in cosmology at the University of Cambridge. She obtained an MSci and BA from the University of Cambridge, studying experimental and theoretical physics at Gonville and Caius college. Her research focuses on developing computational methods for studying the evolution of the early universe, and on aspects of quantum field theory - the framework in which contemporary particle physics is formulated.
During her PhD studies she worked at the British Antarctic Survey for 6 months to develop code to forecast sea ice extent over the Arctic using convolutional networks. She has previously interned at the Institute of Astronomy, Cambridge, working on automating the subtraction of host galaxy contamination from images of active galactic nuclei (supermassive black holes), and at London-based start-up Kokoon Tech Ltd., classifying sleep stages from electroencephalogram signals using machine learning.

I'm a machine learning engineer at Talkspace, where I build NLP-powered tools that enhance asynchronous talk therapy. My current work at centers around early detection and continuous monitoring systems for acute mental health conditions and patient-therapist matching. I have 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. My research interests include reinforcement learning, automated machine learning, and fairness, accountability, and transparency in machine learning. I like developing open source tools for improving data science and machine learning practice.

I work on the design and implementation of high performance programming systems including languages, compilers, and runtimes. In particular, my focus is on building systems that enable both high performance and high productivity for large scale system architectures including both supercomputers and distributed systems. I also have considerable experience constructing tools capable of leveraging the power of GPUs for general purpose computing. I believe that high performance computing should not simply be the domain of experts, but should be a tool available to all via the intelligent implementation of programming systems. My design philosophy is that these systems should be built the right way and not with the ad-hoc tools available for users today. I endeavor to make this vision a reality by designing abstractions capable of facilitating the needs of end users with minimal cognitive load. My hope is one day the design of programming systems will be an approach leveraged by all programmers and not just experts. About Me I currently work as a research scientist at NVIDIA Research. In 2014 I completed my Ph.D. in Computer Science at Stanford University under the guidance of Professor Alex Aiken. Before that I graduated Magna Cum Laude from Duke University in 2008 with degrees in Electrical Computer Engineering (with honors), Mathematics (with high honors) , and Computer Science.

I am a Research Engineer working on the Econ-Ark suite of macroeconomic simulation tools. These open source tools for structural modeling with heterogeneous agents are fiscally sponsored by NumFOCUS.

Monica Bobra is a research scientist at Stanford University in the W. W. Hansen Experimental
Physics Laboratory, where she studies the Sun and space weather as a member of the NASA Solar Dynamics Observatory science team. Her research focuses on analyzing large data sets, on the scale of terabytes to petabytes, that describe the Sun and space weather. She also serves as Vice Chair of the advisory board for SunPy Project and an editor for the Journal of Open Source Software (JOSS).

Maarten Breddels is an entrepreneur and freelance developer/consultant/data scientist working mostly with Python, C++ and Javascript in the Jupyter ecosystem. Creator of ipyvolume and vaex, founder of vaex.io. His expertise ranges from fast numerical computation, API design, to 3d visualization. He has a Bachelor in ICT, a Master and PhD in Astronomy, likes to code and solve problems.

Brandon is a second year Chemical Engineering PhD candidate at the University of Michigan. His adviser is Prof. Sharon Glotzer. His research centers on developing analysis methods for particle based simulation using concepts from information theory and on the self-assembly of colloids. In addition to his research, Brandon contributes to multiple open source scientific Python packages such as HOOMD-blue and signac.

When not on his computer, Brandon enjoys spending time with family and friends, exercising, cooking, and exploring the outdoors.

Joe Eaton is the Principal System Engineer for Data and Graph Analytics at NVIDIA.
He has spent the last 7 years at NVIDIA working on applications of sparse linear algebra: CUDA Libraries, cuSOLVER, cuSPARSE, nvGRAPH, AmgX and now 100% on RAPIDS, developing Python APIs, cuML and cuGRAPH. RAPIDS is an end-to-end platform for data science, including IO, ETL, model training, inference and visualization.
Previously he spent 18 years in Oil & Gas reservoir simulation and HPC . He is a frequent speaker at SC, GTC, and directly interfaces with engineers and mathematicians across industries.

Matthew is a postdoctoral researcher at the University of Illinois at Urbana-Champaign with research expertise in experimental high energy physics. He works as a member of the ATLAS collaboration as part of the effort to discover physics beyond the standard model with experiments preformed at CERN's Large Hadron Collider (LHC) in Geneva, Switzerland. He is also a researcher at the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP). He previously did his Ph.D. (2019) research at Southern Methodist University, also on the ATLAS experiment, in searches for high momentum low mass resonances decaying to two b-quarks and produced in association with a jet.

Emmanuelle Gouillart is a scientific Python developer at Plotly, where she is a core developer of the plotly.py visualization library. She has a background in physics and materials science, and she has carried on scientific research and software development during the last years. She became a core contributor of Python’s popular image processing library scikit-image since a large part of her research relies on extracting quantitative data from image datasets. She has been a co-organizer of the Euroscipy conference during the last ten years, and she enjoys very much discussing with Python users about image processing and visualization at conferences.

Sean is interested in tectonic-climate interactions, the role of catastrophism in the geologic record and marine geophysical imaging at nested resolutions. His current projects include tectonic and climate interactions in the St. Elias Mountains and Surveyor submarine fan, geohazards and margin evolution of subduction and transform faulting in Alaska, Sumatra, and Japan, and the geologic processes and environmental effects of the Cretaceous-Paleogene Chicxulub meteor impact.

I am a scientist and technologist living in Austin, Texas. I characterize stars and exoplanets using high resolution spectroscopy and probabilistic inference. I recently joined the Exoplanet Atmospheres research group with UT Austin Professor Caroline Morley. We want to measure the atmospheres of exoplanets, with a long view of finding out which exoplanets are habitable. I specialize in quantifying astrophysical uncertainty in the atmospheric measurement process. Previously I worked for the K2 mission, which continues to revolutionize our understanding of exoplanets and stellar activity among other topics. I contribute to the scientific Python ecosystem including the lightkurve toolkit for time series data anlysis and the spectral inference framework Starfish. For my PhD I led metrology with the UT Austin Silicon Diffractive Optics Group, where I lithographically patterned immersion gratings. During a NASA GSRP fellowship at the JPL Microdevices Lab I improved the precision of these devices even further. Recently I’ve become excited about GPUs, autodiff, and other modern numerical innovations that stand to catalyze astronomical discovery.

Brendon holds a Ph.D. in mechanical engineering from the University of California Santa Barbara, and a B.Eng. in mechanical engineering as well as B.Sc. in computer science from Western University in Canada.

With more than 10 years of industry experience, Brendon is a recognized leader in machine learning who understands the science of oil and gas exploration. His experience as a computational geologist at ExxonMobil and as a research geophysicist at ION Geophysical add to his background in software development to enable cross-functional discussions that drive business value across oil and gas organizations.

I am in the last stretch of my PhD with Heidelberg University and I conduct my research in the Saalfeld lab at HHMI Janelia in the Washington DC metro area. I am interested in scalable neuron reconstruction from large 3D electron micrographs (EM) and high-quality tools for efficient visualization, generation of dense ground truth annotations, and proof reading connectomics in 3D EM. Previously, I worked on cell tracking in developing embryos and used C++/Python with the SciPy and NumPy frameworks for image processing. My current work builds on top of the ImageJ eco-system, most importantly the multi-dimensional image processing library ImgLib2. I created imglyb to create a bridge between NumPy and ImgLib2 data structures with shared memory access. Now, it is possible to combine those frameworks in a single-process workflow. For example, multi-dimensional NumPy arrays can be visualized in powerful Java tools like BigDataViewer or Paintera.

Michael holds a Ph.D. in polymer science from The University of Akron and a B.S. in materials science and engineering from the University of Illinois at Urbana-Champaign with expertise in polymers for optoelectronic applications.

Prior to joining Enthought, he worked as a postdoctoral researcher at several institutions, where he developed improved physical models for organic electronic devices that connect the complex materials microstructure to semiconductor device physics. This research was enabled by custom open source software tools for physics-based device simulations, automated experimental measurements, and advanced data analysis.

Eshin is a postdoctoral fellow at Dartmouth College where he also received a PhD in Cognitive Neuroscience. His research integrates approaches from behavioral economics, social psychology, functional MRI, machine learning, and computational modeling to understand the neural and behavioral bases of human social and emotional processing. His research methods prioritize experimental naturalism to maximize the generalizability and real-world application of his work. He also applies his expertise in analytics to help solve technology, business, and civic tech problems with several volunteer organizations including the Dartmouth Graduate Consulting Group and Code for America. In his free time, he loves developing and contributing to free and open-source software, primarily in Python and Javascript, to make scientific tools accessible to everyone while improving the reproducibility of empirical research.

I am a philosophy PhD turned data scientist who has worked on problems involving natural language at the University of California, as well as at IBM and now Levi Strauss. I have experience teaching as well as giving presentations. I have given academic presentations at various philosophical conferences (including the major American and Australian philosophy associatio0ns) as well as more technical, data science conferences (including DeveloperWeek and Dataiku events). I have also taught intensive courses in data science internally at IBM to new hires.

Ralph Kube joined the Princeton Plasma Physics Laboratory as a research physicist in 2019 and is a member of the US SciDAC Center for High-Fidelity Boundary Plasma Simulations (HBPS), led by Principal Investigator Choong-Seock Chang. His research interest lies in understanding the edge and scrape-off layer regions of magnetically confined plasmas. Ralph's current work includes applying machine learning methods to accelerate the full-f gyrokinetic particle-in-cell code XGC1. He also develops novel workflow tools tailored to automatically analyze big and fast data streams produced by fusion experiments and high-fidelity numerical simulations.

Ralph graduated with a B.Sc. in Physics from RWTH Aachen University in 2004. He received a M.Sc. and a PhD in Plasma Physics from UiT The Arctic University of Norway. After that he worked as a postdoctoral research fellow at the University of Innsbruck in Austria and at UiT in Norway where he investigated the statistical properties of intermittent fluctuations observed in scrape-off layer plasmas.

Kelvin Lee is a postdoctoral researcher at the Center for Astrophysics | Harvard & Smithsonian, where he applies physical chemistry and rotational spectroscopy to understand the physics and dynamics of astrophysical processes. His research involves using laboratory analogs of interstellar conditions to discover unobserved, highly reactive molecules that may be of interest to questions regarding star and planet formation and the origins of life.

Aside from astrochemistry and spectroscopy, his passions are in open source and reproducible (data) science, machine learning, and statistical inference.

Fritz Lekschas is a Ph.D. candidate in computer science at Harvard School of Engineering and Applied Sciences with a strong passion for scalable biomedical data visualization, bioinformatics, web development, and design. As part of his Ph.D., Fritz Lekschas joined Peter Kerpedjiev and Nezar Abdennur in building the HiGlass framework (https://higlass.io) and several related tools (http://genomevis.lekschas.de). For more information visit his personal website at https://lekschas.de or his Twitter feed at https://twitter.com/flekschas. Fritz always writes about himself in the third person because it sounds so regal!

Jon is a data scientist and software developer with the Air and Missile Defense Sector of the Johns Hopkins Applied Physics Laboratory. He has interest and experience across a range of technical domains, programming languages, data visualization techniques, and data science technologies. Jon holds bachelors’ degrees in Physics and Mathematics from Millersville University, a master’s degree in Computer Science from Johns Hopkins University, and is currently pursuing a Master of Information and Data Science degree from UC Berkeley.

I am a postdoctoral researcher and lecturer in the Statistics Department of Stanford University, USA. I work with Susan Holmes, Russell Poldrack and Xavier Pennec. My research aims to create computational representations of the human body, at different scales. At the macroscopic scale, I am interested in learning quantitative descriptions of organ shapes and functions, together with their normal and pathological variations in the population; for example, learning the manifold of the brain anatomy or the manifold of the brain activity during certain functional tasks. At the microscopic scale, I am interested in studying cells shapes and functions, and at the nanoscopic scale, molecular shapes and functions. My goal is to leverage these heterogeneous multiscale representations to implement new computer-assisted diagnosis methods and develop innovative treatments for diseases.

I'm a lecturer at the Data Science Institute at Columbia University and author of the O'Reilly book "Introduction to machine learning with Python", describing a practical approach to machine learning with python and scikit-learn. I am one of the core developers of the scikit-learn machine learning library, and I have been co-maintaining it for several years. I'm also a Software Carpentry instructor. In the past, I worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. My mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

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

Ardie Orden is a Geospatial Analyst at Thinking Machines Data Science, a Philippine data science consultancy that helps people make data-driven decisions. He has worked with UNICEF, Qatar Computing Research Institute, Humanitarian OpenStreetMap Team, and different companies in Southeast Asia in order to help solve research and business problems that involve geospatial data. His research interests include GIS, deep learning with satellite images, data science for social good, and AI for social good.

Matthew is an open source software developer in the numeric Python ecosystem. He maintains several PyData libraries, but today focuses mostly on Dask a library for scalable computing. Matthew worked for Anaconda Inc for several years, then built out the Dask team at NVIDIA for RAPIDS, and most recently founded Coiled Computing to improve Python's scalability with Dask for large organizations.

Matthew has given talks at a variety of technical, academic, and industry conferences. A list of talks and keynotes is available at matthewrocklin.com/talks.

Matthew holds a bachelors degree from UC Berkeley in physics and mathematics, and a PhD in computer science from the University of Chicago.

Website: https://matthewrocklin.com
Blog: https://matthewrocklin.com/blog
Dask: https://dask.org/

Jacob Schreiber is a post-doc and IGERT big data fellow in the Genome Science department at the University of Washington. Previously, he was a graduate student in the Computer Science and Engineering Department. His research focus is on the application of machine learning methods, primarily deep learning ones, to the massive amount of data being generated in the field of genome science. His research projects have involved predicting the three dimensional structure of the genome using convolutional neural networks and learning a latent representation of the human epigenome using deep tensor factorization. These projects typically involve hundreds of millions to billions of samples, making them markedly "big data" problems. He routinely contributes to the Python open source community as the core developer of pomegranate, a package for flexible probabilistic modeling, apricot, a package for data summarization for machine learning, and in the past as a core developer for the scikit-learn project. Future projects include one day being able to go outside again

Henry Schreiner is a Computational Physicist/Research Software engineer in High Energy Physics. 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 tomogrophy for archeology 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).

Stanley Seibert is the senior director of community innovation at Anaconda and also contributes to the Numba project. Prior to joining Anaconda, Stan was chief data scientist at Mobi, working on vehicle fleet tracking and route planning. He has more than a decade of experience using Python for data analysis and has been doing GPU computing since 2008. Stan received a Ph.D. in experimental high-energy physics from the University of Texas at Austin and performed research at Los Alamos National Laboratory, University of Pennsylvania, and the Sudbury Neutrino Observatory.

Brigitta Sipőcz is a DIRAC Fellow at the Data Intensive Research in Astrophysics and Cosmology Institute at the University of Washington. She is an astronomer by training, and occasionally still observes the night sky, but she currently spends most of her time developing and maintaining open source astronomy software. She has recently become the lead developer of astroML, and also maintains several packages in the Astropy Project. She also has a keen interest in finding ways to promote the open development model and make tools more sustainable. She is a fellow of the Software Sustainability Institute.

Christine Smit works at NASA's Goddard Earth Sciences (GES) Data and Information Services Center (DISC). She works on Giovanni, a tool designed to make the path between data and science simpler so you don't have to be an expert to use remote sensing data. Her current work involves migrating Giovanni to the cloud with Amazon Web Services. She enjoys the challenge of working with large, scientifically relevant datasets.

Adam Thompson is a Senior Solutions Architect at NVIDIA and the creator of cuSignal – a GPU accelerated signal processing Python library housed under the RAPIDS Open GPU Data Science project. Adam’s technical interests involve signal processing, applications of deep learning to radio frequency data, high performance computing, and data compression. He holds a Masters degree in Electrical and Computer Engineering from Georgia Tech and a Bachelors Degree from Clemson University. You can find him tweeting about signals, GPUs, indie music, and baking at @adamlikesai.

Horacio Vargas Guzman is a lead scientist at the Jozef Stefan Institute in Slovenia. He works on the theoretical development and implementation of analytical and computational methods for exploring new material properties, with special interest in physical phenomena of soft matter and biological inhomogeneous systems. One of his recent developments is a computational multiscale method for efficient calculations of free energy landscapes of virus capsids, as a function of the environment-mediated variables, like pH or the ionic strengths of the buffers where the viruses are located. Previously, he was appointed as a postdoctoral researcher at the Max Planck Institute of Polymer Research in Germany focused on the development and assessment of new multiscale algorithms for molecular simulation. In 2014 he obtained his Doctor's degree in Condensed Matter Physics and Nanotechnology from the University Autonoma of Madrid. The topic of his PhD was focused on the development of theoretical models for the peak interaction forces in Dynamic Atomic Force Microscopy techniques. He has presented his published work in several scientific conferences in Europe and America, mainly devoted to Soft Condensed Matter Physics and Nanotechnology, among other contributions and invitations to scientific computing workshops and tutorials related to physical systems simulations with HPC, C/C++, Fortran and Python. I am also a Software Carpentry Instructor and avid user of the scipythonic programming stack.

Jake VanderPlas was formerly both the Director of Open Software and the Director of Research in Physical Sciences for the eScience Institute; he now works at Google. He comes from a background of machine learning and data-intensive astronomy and astrophysics. Along with his astronomy and computing research, Jake is interested in encouraging open and reproducible research practices across scientific disciplines. To this end, he has been very active in the world of open source Python. He is a core maintainer or regular contributor to several well-known Python projects, including Scikit-learn, Scipy, Matplotlib, and IPython. He has authored several packages focused on promoting open and reproducible science, including astroML, scidbpy, mpld3, and others. He has been involved on the program and review committees for a number of well-known Python conferences, including the PyCon, SciPy, and PyData series.

He is a co-author of the Python-powered text Statistics, Data Mining, and Machine Learning in Astronomy, and has presented many tutorials on the subject. He occasionally blogs about Python, Machine Learning, Visualization, and related topics at http://jakevdp.github.io.

I am involved in a number of open source projects, the biggest are xtensor and xsimd. I also work a lot on technologies in the Project Jupyter space. I did my master studies in Robotics, Systems and Controls and still use ROS on a daily basis.

I am currently employed at a small & very fine software consultancy in Paris, called QuantStack where we are lucky to work on mostly open source software.