Participants

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.

 Khaled Ibrahim is the Application Engagement co-lead for RAPIDS.  He 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. 

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 Distinguished 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.

Kevin Huck is the Platform Readiness co-lead for RAPIDS. He is a 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.

Sam Williams is the Platform Readiness lead for RAPIDS. He is a Senior 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. 

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.

Rob Latham is the Scientific Data Management co-lead for RAPIDS. He is Principle Software Development Specialist at Argonne National Laboratory and works on high performance IO libraries and tools for scientific applications.

Prasanna Balaprakash is the AI/ML lead for RAPIDS. He is the Director of AI Programs and Distinguished R&D Scientist at Oak Ridge National Laboratory. His research interests span artificial intelligence, machine learning, optimization, and high-performance computing. His research interests span the areas of artificial intelligence, machine learning, optimization, and automatic application performance modeling and tuning. For more information see https://pbalapra.github.io/ 

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.

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.

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.

John Wu 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/

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.

Fred Suter is a computer scientist at Oak Ridge National Laboratory, and was previously at IN2P3 computer center in France. His research spans the life cycle of data science and includes workflow automation systems.

Pradeep Subedi is a Research Associate at the Rutgers Discovery Informatics Institute at Rutgers University. His research interests include data-staging infrastructure for in-situ workflows, autonomic extreme-scale data management, machine learning techniques, and scalable middleware for scientific workflows.

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://seyonglee.github.io. 

Kshitij Mehta is a computer scientist in the Workflow Systems Group at Oak Ridge National Laboratory. His research interests include scalable workflow automation and management of large scientific data.

Paul Hovland is a Senior Computer Scientist in 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. 

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 a 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/ 

Qian Gong is a  R&D Staff Member in the Workflow System Group at Oak Ridge National Laboratory. She has been leading the development of the MGARD reduction framework.Her research focuses on scalable in situ and file system I/O.

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 an Assistant Computational Scientist at the MCS division in Argonne National Laboratory, and a Research Assistant Professor at IIT-Chicago. 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.   

Krishnan Raghavan is a Postdoctoral Fellow at Argonne National Laboratory. His current research interests include continual learning, reinforcement learning, graph theory, spatio-temporal statistics and control.

Xingfu Wu is a staff scientist at Argonne National Laboratory and a CASE senior scientist at the University of Chicago. His research interests include performance analysis and modeling, performane optimization, autotuning, energy efficient computing, power modeling, and machine learning.  

James Ahrens is a senior scientist at Los Alamos National Laboratory. His primary research interests are visualization, computer graphics, data science and parallel systems. Dr. Ahrens is the author of over 100 peer-reviewed papers that have been cited almost 6000 times. He is the founder/design lead of ParaView, an open-source visualization tool designed to handle extremely large data.

Arnur Nigmetov is a postdoctoral researcher at Lawrence Berkeley National Laboratory. His research interests include topological data analysis with applications in machine learning and parallel and distributed algorithms. 

John Patchett is a senior scientist at Los Alamos National Laboratory. His primary research interests are visualization, parallel systems, and data science. Dr.-Ing. Patchett is the ASC Production Visualization lead and the Deputy Data Science at Scale Team Lead in the Computer, Computational, and Statistical Sciences group at LANL.

Kenneth Moreland is a Senior Research Scientist at Oak Ridge National Laboratory for the Visualization group in the Computer Science and Mathematics Division. His research interests include large-scale visualization and visualization algorithms for multi-core, many-core, and future-generation computer hardware. More information is available at https://www.kennethmoreland.com/. 

Suren Byna is a Staff Scientist in the Scientific Data Management (SDM) Group at Lawrence Berkeley National Lab. His research interests are in scalable scientific data management. He works on optimizing parallel I/O and on developing systems for managing scientific data.

Tom Scogland is a Computer Scientist at Lawrence Livermore National Laboratory. His focus is heterogeneous programming models and system software. He is the chair of the OpenMP accelerator subcommittee and LLNL representative to the C++ committee.

Zhe Bai is a postdoctoral researcher at Lawrence Berkeley National Laboratory. Her interests include reduced order modeling, scientific machine learning, and dynamic system control.

Norbert Podhorszki is a Distinguished R&D Staff Member in the Workflow System Group at Oak Ridge National Laboratory. He has been leading the development of the ADIOS I/O framework His research focuses on scalable in situ and file system I/O.

Jian Huang is a professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His research focuses on designing novel systems for large data visualization. His recent work centers around Visualization as a Service (VaaS).

Tushar Athawale is a Computer Scientist in Visualization Group at Oak Ridge National Laboratory. His research interests are in uncertainty visualization and topological data analysis for scientific data discovery.

Ana Gainaru is a Computer Scientist in the Workflow group at Oak Ridge National Laboratory. Her research interest includes workflow and data management for large-scale complex applications that combine traditional HPC with AI/ML.

Viktor Reshniak is a staff mathematician in the Data Analysis and Machine Learning group at Oak Ridge National Laboratory. His research interests include the design of robust machine learning algorithms, image processing algorithms, and data compression algorithms.