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Committee Member Biographical Information

DONA L. CRAWFORD, Chair, retired as the associate director for computation from the Lawrence Livermore National Laboratory (LLNL), where she led the laboratory’s high-performance computing efforts. In that capacity, Crawford was responsible for the development and deployment of an integrated computing environment for petascale simulations of complex physical phenomena. Prior to her LLNL appointment in 2001, Crawford was with Sandia National Laboratories since 1976 serving on many leadership projects including the Accelerated Strategic Computing Initiative and the Nuclear Weapons Strategic Business Unit. Crawford serves on the National Academies of Sciences, Engineering, and Medicine’s Laboratory Assessments Board and has previously served on several National Academies’ committees including the Committee to Evaluate Post-Exascale Computing for the National Nuclear Security Administration, the Committee to Review Governance Reform in the National Nuclear Security Administration, and the Committee to Evaluate the National Science Foundation’s Vertically Integrated Grants for Research and Education Program. She received her MS in operations research from Stanford University.

SYED BAHAUDDIN ALAM is an assistant professor of nuclear, plasma, and radiological engineering at the University of Illinois Urbana-Champaign (UIUC), where he leads the MARTIANS (Machine Learning & ARTificial Intelligence for Advancing Nuclear Systems) Laboratory. He was named as the national artificial intelligence (AI) leader in UIUC’s official response to the White House AI Action Plan (2025). He holds a joint appointment at the National Center for Supercomputing Applications. Alam’s research expertise centers on energy-efficient AI and

digital twins, with a primary focus on developing real-time AI algorithms for nuclear and energy systems. He has been recognized with numerous prestigious awards, including the Nuclear News 40 Under 40, Dean’s Award for Excellence in Research from the UIUC Grainger College of Engineering, Illinois Innovation Award finalist for excellence in cutting-edge innovation, a “Top of Minds” feature by UIUC Grainger College, the Cambridge Philosophical Society Award, the American Nuclear Society Best Paper Award, the Cambridge Trust Award, and an Outstanding Teaching Award. He earned his PhD (2018) and MPhil (2013) in nuclear engineering from the University of Cambridge and BSc (2011) in electrical and electronic engineering from the Bangladesh University of Engineering and Technology.

MARTA D’ELIA is the director of AI and ModSim at Atomic Machines and an adjunct professor at the Stanford University Institute for Computational & Mathematical Engineering. She previously worked at Pasteur Labs, Meta, and Sandia National Laboratories as a principal scientist and tech lead. She holds a PhD in applied mathematics and master’s and bachelor’s degrees in mathematical engineering. Her work deals with design and analysis of machine learning (ML) models and optimal design and control for complex industrial applications. She is an expert in nonlocal modeling and simulation, optimal control, and scientific ML. She is an associate editor of Society and Industrial and Applied Mathematics (SIAM) and Nature journals, a member of the SIAM industry committee, the vice chair of the SIAM Northern California section, and a member of the NVIDIA advisory board for scientific ML.

KRISHNA GARIKIPATI obtained his PhD at Stanford University in 1996, and after a few years of postdoctoral work, he joined the University of Michigan in 2000, rising to professor in the Departments of Mechanical Engineering and Mathematics. Between 2016 and 2022, he served as the director of the Michigan Institute for Computational Discovery & Engineering. In January 2024 he moved to the Department of Aerospace and Mechanical Engineering at the University of Southern California. His research is in computational science, with applications drawn from biophysics, materials physics, mechanics, and mathematical biology. Of recent interest are data-driven approaches to computational science. He has been awarded the Department of Energy Early Career Award for Scientists and Engineers, the Presidential Early Career Award for Scientists and Engineers, and a Humboldt Research Fellowship. He is a fellow of the U.S. Association for Computational Mechanics, the International Association for Computational Mechanics, and the Society of Engineering Science; a Life Member of Clare Hall at the University of Cambridge; and a visiting scholar in computational biology at the Flatiron Institute of the Simons Foundation.

SHIRLEY HO is a senior research scientist at the Center for Computational Astrophysics at the Simons Foundation. She joined the Foundation in 2018 to lead the Cosmology X Data Science group. Her research interests range from cosmology to developing new ML methods for scientific data that leverage shared concepts across scientific domains. Ho has extensive expertise in astrophysical theory, observation, and data science. She focuses on novel statistical and ML tools to address cosmic mysteries such as the origins and fate of the universe. Ho analyzes data from surveys by the Atacama Cosmology Telescope, the Euclid Observatory, the Large Synoptic Survey Telescope, the Simons Observatory, the Sloan Digital Sky Survey, and the Roman Space Telescope, among others, to understand our universe’s evolution. She earned her PhD in astrophysical sciences from Princeton University in 2008 and BS degrees in computer science and physics from University of California, Berkeley, in 2004. Ho was previously a Chamberlain and Seaborg Fellow at Lawrence Berkeley National Laboratory (LBNL). She joined Carnegie Mellon University as an assistant professor in 2011, becoming the Cooper Siegel Career Development Chair Professor and a tenured associate professor. In 2016 she moved to LBNL as a senior scientist.

SCOTT H. HOLAN is a Curators’ Distinguished Professor and the department chair in the Department of Statistics and Data Science at the University of Missouri and serves as a senior research fellow in the Research and Methodology Directorate at the U.S. Census Bureau. His research expertise includes developing statistical and ML methodology for dependent data (spatial, spatiotemporal, functional, and multivariate, among others), Bayesian methods, environmental and ecological statistics, official statistics, and survey methodology. He is an elected Fellow of the American Statistical Association (2014), an elected member of the International Statistical Institute (2017), an elected Fellow of the Institute of Mathematical Statistics (2021), and an elected Fellow of the American Association for the Advancement of Science (2024). Holan was a previous co-awardee of the Statistical Partnerships Among Academe, Industry, and Government Award (2017).

MICHAEL KEARNS is a professor and the National Center chair of the Department of Computer and Information Science at the University of Pennsylvania and the founding director of the Warren Center for Network and Data Sciences. His research interests include topics in ML, AI, algorithmic game theory and microeconomics, computational social science, and quantitative finance and algorithmic trading. Kearns often examines problems in these areas using methods and models from theoretical computer science and related disciplines. He also often participates in empirical and experimental projects, including applications of ML to problems in algorithmic trading and quantitative finance, and human-subject experiments on strategic and economic interaction in social networks.

Kearns spent 1991–2001 in ML and AI research at AT&T Bell Labs and in the last 4 years of his appointment was head of the AI department, which conducted a broad range of systems and foundational AI work. Kearns received his undergraduate degrees from the University of California, Berkeley, in mathematics and computer science and his PhD in computer science from Harvard University. In 2020, Kearns joined Amazon Web Services as an Amazon Scholar, focusing on fairness, privacy, and other “responsible AI” topics. He is an elected member of the National Academy of Sciences.

PETROS KOUMOUTSAKOS is the Herbert S. Winokur Jr. Professor for Computing in Science and Engineering. He also currently holds a visiting researcher position at Google DeepMind in London. He studied Naval Architecture (diploma from the National Technical University of Athens, MEng from the University of Michigan, and received a PhD in aeronautics and applied mathematics from the California Institute of Technology [Caltech]). He has conducted postdoctoral studies at the Center for Parallel Computing at Caltech and at the Center for Turbulent Research at Stanford University and NASA Ames. He has served as the chair of computational science at ETHZurich (1997–2020). Koumoutsakos is an elected Fellow of the American Society of Mechanical Engineers, the American Physical Society, and the Society of Industrial and Applied Mathematics. He is a recipient of the Advanced Investigator Award from the European Research Council and the Association for Computing Machinery’s Gordon Bell prize in supercomputing. He is an elected International Member of the National Academy of Engineering.

BRIAN KULIS is an associate professor at Boston University, with appointments in the Department of Electrical and Computer Engineering, the Department of Computer Science, the Faculty of Computing and Data Sciences, and the Division of Systems Engineering. From 2019 to 2023, he was also an Amazon Scholar, working with the Alexa team. Previously, he was the Peter J. Levine Career Development Assistant Professor at Boston University. Before joining Boston University, he was an assistant professor in computer science and in statistics at Ohio State University. Prior to that he was a postdoctoral fellow at the University of California, Berkeley, Electrical Engineering & Computer Sciences. His research focuses on ML, statistics, computer vision, and large-scale optimization. He obtained his PhD in computer science from the University of Texas in 2008 and his BA from Cornell University in computer science and mathematics in 2003. For his research, he has won three best paper awards at top-tier conferences—two at the International Conference on Machine Learning (2005 and 2007) and one at the IEEE Conference on Computer Vision and Pattern Recognition (2008). He was also the recipient of a National Science Foundation (NSF) CAREER Award in 2015.

DANIEL I. MEIRON is currently a professor of aerospace and applied and computational mathematics. His research interests are primarily in computational fluid dynamics with connections to high-performance computing. He also has interests in computational materials science. He received an ScD in applied mathematics at the Massachusetts Institute of Technology working under Steven A. Orszag. He has participated as part of a recent National Academies’ study on exascale computing.

NATHANIEL TRASK recently joined the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania after spending 8 years as technical staff at Sandia National Laboratories. His research focuses on developing foundational aspects of scientific machine learning (SciML) for high-consequence engineering settings. By integrating concepts from modern physics and probability into the design of deep learning architectures, he leads a research program employing SciML for scientific discovery as well as to construct digital twins of complex systems. He is the deputy director of the Scalable, Efficient and Accelerated Causal Reasoning Operators, Graphs and Spikes for Earth and Embedded Systems Center, an Office of Science funded multi-institutional center developing next-generation AI architectures for Earth and embedded systems. He has received the Department of Energy Early Career Award, as well as the NSF Mathematical Science Postdoctoral Fellowship. His doctoral training was in applied mathematics, with a focus on developing novel optimization-based discretizations of partial differential equations to simulate multiphysics and multiscale problems. After moving to Sandia National Laboratories for a fellowship, he went on to work extensively on ML applied to material science and physics in extreme environments.