Bronis de Supinski

Bronis R. de Supinski is the Chief Technology Officer (CTO) for Livermore Computing (LC) at Lawrence Livermore National Laboratory (LLNL). In this role, he is responsible for formulating LLNL's large-scale computing strategy and overseeing its implementation. His position requires frequent interaction with high performance computing (HPC) leaders and he oversees several collaborations with the HPC industry as well as academia. He is also the LLNL principal point of contact for the Scientific Discovery through Advanced Computing (SciDAC) program's Institute for Sustained Performance, Energy and Resilience (SUPER), for which he leads the resilience thrust.

Prior to becoming CTO for LC, Bronis led several research projects in LLNL's Center for Applied Scientific Computing (CASC). Most recently, he led the Exascale Computing Technologies (ExaCT) project and co-led the Advanced Simulation and Computing (ASC) program's Application Development Environment and Performance Team (ADEPT). ADEPT is responsible for the development environment, including compilers, tools and run time systems, on LLNL's large-scale systems. ExaCT explored several critical directions related to programming models, algorithms, performance, code correctness and resilience for future large scale systems. He currently continues his interests in these topics, particularly programming models, and serves as the Chair of the OpenMP Language Committee.

Bronis earned his Ph.D. in Computer Science from the University of Virginia in 1998, and he joined CASC in July 1998. His dissertation investigated shared memory coherence based on isotach logical time systems. His research has covered a wide range of topics, including applications of data mining techniques to performance analysis and modeling including performance modeling through non-linear regression techniques (i.e., artificial neural networks and piecewise polynomial regression), investigations into mechanisms and tools to improve memory performance, a variety of optimization techniques and tools for MPI, and several issues with OpenMP, including its memory model and tool support.

Throughout his career, Bronis has won several awards, including the prestigious Gordon Bell Prize in 2005 and 2006, as well as an R&D 100 for his leadership of a team that developed a novel scalable debugging tool. He serves on the program committees of numerous conferences and workshops. He is a member of the ACM and the IEEE Computer Society.

Shreyansh Daftry

Shreyansh Daftry is a researcher, technologist and consultant in the fields of Artificial Intelligence (AI) and Space Technology. He is interested in pushing the boundaries of technology with innovation in the fields of Computer Vision, Machine Learning and Autonomous Robotics - Drones, Self-Driving (or Flying) Cars, etc. His lifelong ambition is to promote both the exploration of space and improvement of sustainable living on Earth.

Shreyansh received his M.S. degree in AI and Robotics from School of Computer Science, Carnegie Mellon University USA in 2016, and his B.S. degree in Electronics and Communication Engineering in 2013. Currently, he is a Research Scientist at NASA Jet Propulsion Laboratory (JPL) in California, working on AI technologies for robotic exploration of Earth, Mars and beyond!

Dirk Van Essendelft

Dr. Van Essendelft is the principle investigator for the integration of AI/ML with scientific simulations within in the Computational Device Engineering Team at the National Energy Technology Laboratory.  The focus of Dr. Van Essendelft’s work is building a comprehensive hardware and software ecosystem that maximizes speed, accuracy, and energy efficiency of AI/ML accelerated scientific simulations.  Currently, his work centers around building Computational Fluid Dynamics capability within the TensorFlow framework, generating AI/ML based predictors, and ensuring the ecosystem is compatible with the fastest possible accelerators and processors in industry.  In this way, Dr. Van Essendelft is developing NETL’s first cognitive-in-the-loop simulation capability in which AI/ML models can be used any point to bring acceleration and/or closures in new ways.  Dr. Van Essendelft sits on the Technical Advisory Group for NETL’s new Science-Based Artificial Intelligence/Machine Learning Institute (SAMI) and holds degrees in Energy and Geo-Environmental Engineering, Chemical and Biochemical Engineering, and Chemical Engineering from the Pennsylvania State University, University of California, Irvine, and Calvin College respectively.

Recent publications:

• Rocki, K., Van Essendelft, D., Sharapov, I., Schreiber, R., Morrison, M., Kibardin, V., Portnoy, A., Dietiker, J. F., Syamlal, M., and James, M. (2020) Fast stencil-code computation on a wafer-scale processor, In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp pp 1-14, IEEE Press, Atlanta, Georgia.

Olivier Beaumont

Olivier Beaumont, Ph.D. holds a senior researcher position (Directeur de Recherche) at Inria since October 2008. He defended his PhD thesis in 1999 and his Habilitation in 2004. His main interests are in scheduling, load balancing, HPC and memory optimization and parallelization of training. He served as PC Chair (Algorithm Track) for many HPC conferences (SuperComputing, IPDPS, ICPP, HIPC,...) and he is acting as Associate Editor in Chief of JPDC (Journal of Parallel and Distributed Computing). He is the author of more than 90 papers in international journals and conferences.

Johannes Langguth

Johannes Langguth is a research scientist at Simula reseach laboratory, Oslo, Norway. He received his PhD in computer science from the University of Bergen, Norway in 2011, and master degrees in computer science and economics from university of Bonn, Germany. After a postdoctoral appointment at ENS Lyon, France, he joined Simula in 2012. His research focuses on the design of discrete algorithms for irregular problems on parallel heterogeneous architectures such as multi-core CPUs and GPUs, and their applications in scientific computing, graph analytics, machine learning, computational social science, and high-performance codes for cardiac electrophysiology.