SCHEDULE

Paul is a senior software engineer at NVIDIA where he works for the CUDA Math Library team. He received his Ph.D. in computer science at RWTH Aachen University in Germany in the field of high-performance tensor operations with a special focus on GEMM-like tensor contractions.

Kaumudi is a Strategic Program Manager in the Vertical Industry team with Oracle Cloud Infrastructure. She comes with varied industry experience ranging from Sales and Marketing to Supply Chain and Operations with strong technical background as a silicon Memory designer and technical marketing engineer. Kaumudi graduated with a masters in Electrical Engineering from University of Colorado. Kaumudi has thrived on working in various capacities on cutting edge differentiating cloud and enterprise technology solutions.

Preston Smith is the Director of Research Computing Services at Purdue University. Supporting over 190 HPC faculty, and 550 labs using research data systems, Purdue's Community Cluster program is a pioneering program for delivering "condo-style" HPC. At Purdue, his organization designs, builds, and operates compute systems, and delivers advanced research support to the campus community. Preston holds undergraduate and master’s degrees from Purdue’s College of Technology.

Manuel Castro is a software engineer at the SeungLab, a neuroscience laboratory at Princeton University. He works on building software to empower connectomics research, such as high performance tools for scientific analysis and visualization. He received his BS in computer science from the University of Chicago.

Travis Johnston earned his Ph.D. in mathematics from the University of South Carolina in 2014. Before joining the Computational Data Analytics group at Oak Ridge National Lab in 2016 he was a postdoctoral researcher in Michela Taufer's Global Computing Lab at the University of Delaware. His research interests include the mathematics of machine learning/deep learning and leveraging high performance computing to push the boundaries of what's possible with these technologies.

Dr. Melissa C. Smith received her B.S. (1993) and M.S. (1994) degrees in Electrical Engineering from Florida State University and a Ph.D. (2003) in Electrical Engineering from the University of Tennessee. She is currently an Associate Professor of Electrical and Computer Engineering at Clemson University. She has over 25 years of experience developing and implementing scientific workloads and machine learning applications across multiple domains, including 12 years as a research associate at Oak Ridge National Laboratory. Her current research focuses on the performance analysis and optimization of emerging heterogeneous computing architectures (GPGPU- and FPGA-based systems) for various application domains including machine learning, high-performance or real-time embedded applications, and medical and image processing. Her lab collaborates with researchers in other fields to develop new approaches to the application/architecture interface providing interdisciplinary solutions that enable new scientific advancements and/or capabilities. She is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).

Ben Shealy received a B.S. in Computer Engineering from Clemson University in 2017, and is currently pursuing a Ph.D. in Computer Engineering, also at Clemson University. He has several years of experience developing high-performance applications for computer vision, bioinformatics, and computational materials science. His current research focuses on creating big data pipelines that utilize emerging computing technologies such as GPUs, cluster computing, and cloud computing. He is also interested in applying machine learning to big data pipelines, both to improve their execution at scale and to assist in the analysis of their results. His work is very interdisciplinary in nature and he collaborates regularly with biologists, computational scientists, and software engineers in order to facilitate his work.

Marc Hamilton leads the worldwide solutions architecture and engineering team at NVIDIA. He is responsible for working with customers and partners to deliver the world's best end-to-end solutions for artificial intelligence and deep learning, professional visualization, and high performance computing. Prior to joining NVIDIA in 2013, Hamilton worked at HP in the hyperscale business unit, and at Sun Microsystems in the HPC and data center groups. Hamilton holds a Bachelor of Science in math and computer science from UCLA, and a Master of Science in electrical engineering from the University of Southern California.

Nicola Venuti is an HPC Specialist Solution Architect at Amazon Web Services. He started out working for Nice Software in 2006 and joined AWS in 2016 through the acquisition of Nice Software. In AWS he focuses on Computer Aided Engineering (CAE) and Autonomous Vehicles (AV) workloads.

Rick Watkins is the Director of Cloud and Appliance Solutions at Altair, where he focuses on accelerating the collaboration between High Performance Computing (HPC) and Computer Aided Engineering (CAE). Preceded by three-years of engineering at The Boeing Co. and Altair, Rick’s previous 12 years were spent in Business Development for Altair’s Enterprise Computing and HyperWorks Business Units from his home in Houston, TX, USA.

Jian Tao is a TEES Research Scientist / Computational Scientist / Adjunct Professor at Texas A&M University. He received his Ph.D in Computational Astrophysics from Washington University in St. Louis in 2008. His research interests include computational physics, high performance computing, parallel programming, numerical algorithms, computational framework, machine learning, and workflow management. He is the NVIDIA DLI University Ambassador at Texas A&M University and a contributor of the SPEC CPU2017 benchmark suite.

Dr. Roitberg was born and raised in Argentina, and he received his BS degree in Chemistry from the University of Buenos Aires. He them moved to the US to pursue his PhD in chemistry, at the University of Illinois at Chicago, followed by a postdoctoral stay at Northwestern University. He then joined a National laboratory (NIST) as staff and moved to the University of Florida as Assistant Professor in 2001. He is now a Full Professor there at UF, on both the chemistry and physics departments. Dr. Roitberg has graduated 20 PhD students so far. He has received several awards, including being named a Fellow of both the American Physical Society and the American Chemical Society. He was also named an Ulam Scholar at Los Alamos National laboratory and received the Raices prize (roots) from the Ministry of Science in his Native Argentina. Dr. Roitberg has published over 160 scientific articles, and his h-index is 55, with over 24000 citations to his papers.

Frank Würthwein’s research focuses on Experimental Particle Physics with the CMS experiments at the Large Hadron Collider. Würthwein’s group helped discover the Higgs and searched for dark matter. His involvement with IceCube is in the context of the Open Science Grid (OSG), an NSF funded Cyberinfrastructure that integrates distributed compute and storage resources globally, on-premise and in the cloud.

Julie Bernauer is Director for Deep Learning Systems Software at NVIDIA Corporation. Her team focuses on several aspects of deep learning systems including performance and large scale deep learning and deployments for hyperscale and cloud services. She joined NVIDIA in 2015 after fifteen years in academia as an expert in machine learning for computational structural biology. She obtained her PhD from Université Paris-Sud studying geometric and statistical models for modelling protein complexes. After a post-doc at Stanford University with Pr. Michael Levitt, Nobel Prize in Chemistry 2013, she joined Inria, the French National Institute for Computer Science. While Senior Research Scientist at Inria, Adjunct Associate Professor of Computer Science at École Polytechnique and Visiting Research Scientist at SLAC, her work focused on computational methods for structural bioinformatics, specifically scoring functions for macromolecule docking using machine learning, and statistical potentials for molecular simulations.

Vishal works as a Developer Technology with NVIDIA, with focus on performance optimization for GPU applications. He has been working in the field of High-Performance Computing for over 7 years. His day to day activities involve collaborations with domain scientists in CFD, biomechanics, weather & climate modelling and guiding them for hybrid GPU computing / GPU performance optimization. He is also actively involved in developing high performance Machine Learning algorithms in NVIDIA RAPIDS.

Martijn Marsman is a senior scientist at the University Vienna, and currently coordinates the development effort in the VASP Software GmbH. He has a Ph.D. in physics from the Technical University Delft and roughly 25 years of experience in the field of ab initio electronic structure calculations and high performance computing. His main personal focus is on code optimization for current and future hardware.

Annemarie Southwell manages the GPU compiler team in NVIDIA's HPC compilers group. For several years she also ran release engineering for these compilers and related tools. Before joining the management team, Annemarie was a primary contributor to the development of a custom Fortran language editing facility, OpenMP/MPI-enabled debug engine and user interface, and project build system for the PGI Fortran compiler's integration with Visual Studio. She has also worked directly as an individual contributor on the compilers and debugger.

David Lecomber is Senior Director for Infrastructure and HPC tools at Arm. He has a long history in HPC, having led the tools company, Allinea, which is now part of Arm and whose products now form Arm Forge. He is a strong believer in the necessity of tools to increase the impact of HPC through both reducing developer time and improving the efficiency and capability of scientific simulation.

Paola Buitrago is Director of AI and Big Data group at the Pittsburgh Supercomputing Center, a joint effort of Carnegie Mellon University and the University of Pittsburgh. Paola is PI for Open Compass, a platform for AI research on emerging hardware and software technologies enabling development of advanced algorithms and modeling approaches, and co-PI for Bridges. Paola's diverse background includes research in deep learning, large scale data, and workflow management for high energy physics experiments at Fermilab. Paola developed courses in machine learning, simulation, and optimization at her university, is passionate about education in technology, and launched an education-focused start-up. Paola holds degrees in Chemical Engineering and Systems and Computing Engineering.

Robert Maynard is a principal engineer who joined Kitware in 2010. He currently manages the release process for CMake. His efforts have helped to implement support for the CUDA language, improve support for OBJECT targets, enhance user documentation, and add support for MSVC and Intel compiler feature detection in CMake. Robert is also a primary developer of VTK-m. His work on the open source toolkit focuses on finely-threaded visualization algorithms that run on GPUs and other accelerators. Along with VTK-m, Robert has contributed to numerous projects such as ParaView, the Visualization Toolkit (VTK), and Computational Model Builder (CMB). Robert received his B.S. in computer science from Laurentian University. In his free time, he is an avid curler. He plays out of a club in Upstate New York.

Tom is HPC Architect at the Schlumberger Norway Technology Center in Oslo. He is working on next-generation subsurface modeling and was previously HPC Architect for the Schlumberger Reservoir Simulators ECLIPSE and INTERSECT. His background is Applied Mathematics and he has a keen interest in HPC, machine learning and deep learning.

Chris J. Newburn (CJ) is the Principal HPC Architect for NVIDIA Compute Software, with a special focus on programming models for scale. He has contributed to a combination of hardware and software technologies over the last twenty years. He has a passion for architecting the richness of heterogeneous platforms so that they are easier to use and have lasting value. He has over 80 patents. We wrote a binary-optimizing, multi-grained parallelizing compiler as part of his Ph.D. at Carnegie Mellon University. Before grad school, in the 80s, he did stints at a couple of start-ups, working on a voice recognizer and a VLIW supercomputer. He's delighted to have worked on volume products that his Mom uses.

Chintan Patel is a Sr. Manager, Product Marketing at NVIDIA focused on bringing GPU-accelerated solutions to the HPC community. He leads the management and offering of the HPC application containers on the NGC registry. Prior to NVIDIA, he held product management, product marketing and engineering roles. He holds an MBA from Santa Clara University and a bachelor's degree in electrical engineering and computer science from UC Berkeley.

Alan Edelman is a professor of mathematics and leads the Julia laboratory in the Computer Science & AI Laboratory at MIT. He is also chief scientist at Julia Computing. Edelman works on High Performance Computing, numerical computation, linear algebra, random matrix theory, and geometry. Edelman learned many lost lessons as a graduate student at MIT moonlighting at Thinking Machines Corporation in the 1980s where he won a Gordon Bell Prize. He grew to believe that breakthroughs in HPC could come from raising the levels of abstraction through high level languages that are built from the ground up for performance and productivity. 

Todd Hutchinson leads the Computational Meteorological Analysis and Prediction group at The Weather Company (TWC). His team of scientists and software engineers develop and operate TWC's global numerical weather prediction systems. Todd has been developing weather forecast products for TWC since 1997. Todd received a bachelor's degree, with majors in both atmospheric science and physics from the University at Albany and a master's degree in meteorology from the University of Oklahoma.

Dirk Van Essendelft obtained a BSE in Chemical Engineering from Calvin College in 2003, a MSE in Chemical and Biochemical Engineering from University of California in 2005, Irvine, and a Ph.D. in Energy and Geo-Environmental Engineering from The Pennsylvania State University in 2008. He has work experience in pharmaceuticals, carbon capture and storage, bio-energy conversion, gasification, and high-performance computing. His current research interests are in computational fluid dynamics for large scale industrial modeling using super computers. He is developing unique solutions that merge traditional HPC workloads with Artificial Intelligence for increased acceleration without sacrificing accuracy.