Participants

RAPIDS2 Leadership

Rob Ross is the director for RAPIDS. He is a Senior Computer Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. His interests include the design, implementation, and deployment of complex distributed systems in service of simulation, data, and learning applications in the sciences.

Lenny Oliker is the deputy director for RAPIDS. He is a Senior Computer Scientist in the Computational Research Division at Lawrence Berkeley National Laboratory. His research interests include optimization of scientific methods, performance evaluation, autotuning, domain-optimized computational architectures, and large-scale genome assembly.

Anshu Dubey is the Application Engagement lead for RAPIDS. She is a Computer Scientist at Argonne National Laboratory, and a Senior Scientist for CASE in the Computer Science Department, University of Chicago. Her research interests include multiphysics scientific software, adaptive mesh refinement and software sustainability for scientific software.

Sam Williams is the Application Engagement co-lead for RAPIDS. He is a Staff Scientist in the Computational Research Division at the Lawrence Berkeley National Laboratory. His research interests include performance optimization, performance modeling, computer architecture, and high-performance, scalable linear solvers.

Dmitriy Morozov is the Data Understanding lead for RAPIDS. He is a research scientist in the Data Analytics and Visualization group at Lawrence Berkeley National Laboratory. His research interests include computational geometry, topology, and high-performance computing.

David Pugmire is the Data Understanding co-lead for RAPIDS. He is a Senior R&D Staff Member and Visualization Team Lead in the Scientific Data Group at Oak Ridge National Laboratory. His research focuses on scalable in situ visualization and analysis on HPC systems.

Jeffrey Vetter is the Platform Readiness lead for RAPIDS. He is a ORNL Corporate Fellow and the group leader of the Future Technologies Group at Oak Ridge National Laboratory. His research interests are emerging architectures and programming systems to enable their use.

Paul Hovland is the Platform Readiness co-lead for RAPIDS. He is a Senior Computer Scientist and Deputy Division Director for the Mathematics and Computer Science Division at Argonne National Laboratory. His research focuses on software engineering for high performance scientific applications; areas of interest include automatic differentiation, domain specific languages, and performance engineering.

Scott Klasky is the ORNL PI, and the Scientific Data Management lead for RAPIDS. He is a Distinguished R&D Staff Member and the group leader of the Scientific Data Group at Oak Ridge National Laboratory. More information on his R&D contributions can be found on his google scholar page.

John Wu is the Scientific Data Management co-lead for RAPIDS. He works on a number of topics in data management, data analysis, and high-performance computing. More information is available at http://crd.lbl.gov/wu/

Prasanna Balaprakash is the AI/ML lead for RAPIDS. He is Computer Scientist at Argonne National Laboratory and a Fellow in the Computation Institute of the University of Chicago and in the Northwestern-Argonne Institute of Science and Engineering of the Northwestern University. His research interests span the areas of artificial intelligence, machine learning, optimization, and automatic application performance modeling and tuning. For more information see www.mcs.anl.gov/~pbalapra


Shinjae Yoo is the AI/ML co-lead for RAPIDS. He works on various aspects of AI/ML from a foundational theory to scalable machine learning models on scientific frontier applications. More information is available at http://sjyoo.com.


RAPIDS2 Participants


Eric Brugger is a computer scientist at the Lawrence Livermore National Laboratory. He is one of the founders of the VisIt Visualization and Analysis tool as well as the VisIt Project Leader. His research interests are scientific visualization, scientific data modeling and in situ visualization and analysis.


Aydin Buluc is a Computational Staff Scientist at the Lawrence Berkeley National Laboratory and an Adjunct Assistant Professor of Electrical Engineering and Computer Science at UC Berkeley. His research interests include parallel computing, combinatorial scientific computing, high performance graph analysis, sparse matrix computations, computational genomics and parallel machine learning. For more information, see http://graphblas.org/~aydin/

Bronis R. de Supinski is the Lawrence Livermore Point of Contact (POC) for RAPIDS. He is the Chief Technology Officer (CTO) for Livermore Computing (LC), a role in which he formulates LLNL's large-scale computing strategy and oversees its implementation. His research interests include all aspects of large scale systems including OpenMP, for which he is the Language Committee Chair. For more information: http://people.llnl.gov/desupinski1

Giorgis Georgakoudis is a Computer Scientist in the Center of Advanced Scientific Computing (CASC) of Lawrence Livermore National Laboratory. Giorgis researches optimizing the performance of parallel programs by improving system software infrastructure, including parallel runtimes, compilers and performance analysis tools. For more information visit https://people.llnl.gov/georgakoudis1

Kevin Harms is a Performance Engineer at the Argonne Leadership Computing Facility who works on Storage and I/O. His research interests are parallel I/O and object storage systems.

Kevin Huck is Research Faculty and Computer Scientist at the University of Oregon. His research interests include application and workflow performance measurement, analysis, aggregation and visualization as well as lightweight measurement, dynamic runtime optimization and feedback/control systems for asynchronous multitasking runtimes.

Rob Latham is a Principle Software Development Specialist at Argonne National Laboratory. He works on high performance IO libraries and tools for scientific applications.

Chunhua "Leo" Liao is a computer scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research focus has been on source-to-source compiler techniques to improve the performance, efficiency, and resilience of parallel programs. For more information, see https://people.llnl.gov/liao6

Wei-keng Liao is a Research Professor in the Department of Electrical Engineering and Computer Science at the Northwestern University. His research interests include parallel and distributed file I/O and storage system, high-performance data mining algorithm design, and software development for Parallel NetCDF.

Allen Malony is a Professor in the Department of Computer and Information Science at the University of Oregon. His research interests are in parallel computing, performance analysis, supercomputing, and scientific software environments. For more information, see https://cs.uoregon.edu/profile/malony

Wes Bethel has interests in all aspects of data science, especially scalable methods for HPC platforms, he is a Senior Scientist at Lawrence Berkeley National Laboratory, a Senior Fellow at the Berkeley Institute for Data Science, and an ACM Distinguished Scientist.


Boyana Norris is an Associate Professor in the Department of Computer and Information Science at University of Oregon. Her research in high-performance computing (HPC) focuses on methodologies and tools for performance reasoning and automated optimization of scientific applications, while ensuring continued or better usability of HPC tools and libraries and improving developer productivity. https://cs.uoregon.edu/profile/bnorris2

Manish Parashar Manish Parashar is Distinguished Professor and founding Director of the Rutgers Discovery Informatics Institute (RDI2). His research interests are broadly in Parallel and Distributed Computing. In the RAPIDS project, he is working on data-management for in-situ workflows. For more information please visit http://parashar.rutgers.edu/..

Tom Peterka is a computer scientist at Argonne National Laboratory, fellow at the Computation Institute of the University of Chicago, adjunct assistant professor at the University of Illinois at Chicago, and fellow at the Northwestern Argonne Institute for Science and Engineering. His research interests are in large-scale parallelism for in situ analysis of scientific data.

Seyong Lee is a Senior R&D Staff Member in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. His research interests include parallel programming and performance optimization in heterogeneous computing environments, program analysis, and optimizing compilers. See his website for more information: https://ft.ornl.gov/~lees2/.

Chad A. Steed is a Senior Research Staff Member and Director of the Visual Informatics for Science and Technology Advances (VISTA) Laboratory at Oak Ridge National Laboratory. His research spans the life cycle of data science including interactive data visualization, data analytics, visual perception, databases, and graphic design. More information is available at https://csteed.com.

Khaled Ibrahim is a Computer Scientist in the Computational Research Division at Lawrence Berkeley National Laboratory. His research interests include performance modeling, analysis and tuning. He also conducts research in communication runtime optimization.

Stephen Siegel is an associate professor of computer science and mathematics at the University of Delaware. He directs the Verified Software Laboratory, which is developing automated software verification tools for HPC programs. More information is available at https://vsl.cis.udel.edu.

Hanqi Guo is an Assistant Computer Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. His research interests include data analysis, visualization, and machine learning for scientific data.

Ankit Agrawal is a Research Professor in the Department of Electrical and Computer Engineering at Northwestern University. He specializes in interdisciplinary AI and big data analytics via high performance data mining. More information available here.

Sandeep Madireddy is an Assistant Computer Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. His research interests include theoretical and applied machine learning, probabilistic modeling and high performance computing. He has experience applying them to diverse problems in various domains, ranging from physical sciences to computer systems modeling and neuromorphic computing. For more information see http://www.mcs.anl.gov/~smadireddy/

James Kress is R&D Staff Member in the Scientific Data Group at Oak Ridge National Laboratory. His research focuses on predictable and scalable in situ visualization and analysis on HPC systems.

Pravallika (Pravi) Devineni is an R&D Staff Member in the Computational Data Analytics Group at Oak Ridge National Laboratory. Her research interests include applied machine learning, tensor decompositions and graph mining. In the RAPIDS-2 project, she is working on designing energy-efficient deep learning models and applying them to problems in diverse domains.

Xin Liang is a Computer/Data Scientist in the Scientific Data Group at Oak Ridge National Laboratory. His research interests include scientific data management, reduction/refactoring, and analytics.

Ben Whitney is a Postdoctoral Research Associate in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. His research interests include compression methods for scientific data, numerical methods, and scientific software development.

Jesus Pulido is a staff research scientist at the Los Alamos National Laboratory, as part of Data Science at Scale Team in the Applied Computer Science Group in CCS-7. His research interests are in data analysis, data reduction, visualization, high-performance computing, wavelets and multi-resolution methods.

Pascal Grosset is a scientist in the Data Science at Scale team at Los Alamos National Laboratory. His research interests include in situ analysis for large simulations, data management workflows and data reduction, and rendering in the context of scientific applications.

Romit Maulik is the Margaret Butler Postdoctoral Fellow at Argonne National Laboratory. His research interests include computational physics, scientific machine learning and high performance computing. For more information see here.

Berk Geveci leads the Scientific Computing team at Kitware Inc. He is one of the leading developers of the ParaView visualization application and the Visualization Toolkit (VTK). His research interests include large scale parallel computing, computational dynamics, finite elements & visualization algorithms.


Utkarsh Ayachit is a Distinguished Engineer at Kitware, Inc., and is the leading developer of the ParaView visualization application. His interests include design and development of high performance tools to aid in scientific data analysis & visualization, and frameworks to customize scientific workflows.


Chris Harris is a Principal Engineer at Kitware Inc. He works on high performance computational and experimental workflows especially in the area of chemistry and material science. He is the lead developer for the eSimMon simulation dashboard system.



Wei Xu is a Computer Scientist in Computational Science Initiative at Brookhaven National Laboratory, and a Research Assistant Professor in Computer Science Department at Stony Brook University. Her research interests are visual analytics and machine learning. More information is available at: https://sites.google.com/site/weixusbu2012

Xihaier Luo is a staff member at Brookhaven National Laboratory. His research interests lie at the intersection of machine learning and scientific computing. For more information see: https://xihaier.github.io/



Yihui (Ray) Ren is an Assistant Computational Scientist in the Computational Science Initiative at Brookhaven National Laboratory. His research interests include deep learning (DL) algorithms and their scientific applications, performance analysis and optimization of DL algorithms in high performance computing and edge environments, neuromorphic computing and privacy preserving AI.


Patrick Johnstone is a staff member at Brookhaven National Laboratory. His research interests are in mathematical optimization, distributed algorithms, and machine learning. See more at https://sites.google.com/site/proycejohnstone/home.