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Keynote
IK01 - Machine Learning for Accelerating Simulation and Scientific Computing
Presenter
Fei Sha is currently a research scientist at Google Research, where he leads a team of scientists and engineers working on scientific machine learning with a specific application focus towards AI for Weather and Climate. He was a full professor and the Zohrab A. Kaprielian Fellow in Engineering at the Department of Computer Science, University of Southern California. His primary research interests are machine learning and its application to various AI problems: speech and language processing, computer vision, robotics and recently scientific computing, dynamical systems, weather forecast and climate modeling. Dr. Sha was selected as an Alfred P. Sloan Research Fellow in 2013, and also won an Army Research Office Young Investigator Award in 2012. He has a Ph.D in Computer and Information Science from University of Pennsylvania and B.Sc and M.Sc from Southeast University (Nanjing, China).
Description
Leveraging large-scale data and accelerated computing systems, statistical learning has led to significant paradigm shifts in many scientific disciplines. Grand challenges in science have been tackled with exciting synergy between disciplinary science, physics-based simulations via high-performance computing, and powerful learning methods. In this talk, Fei Sha will describe two vignettes of our research in this theme: probabilistic generative AI technology for uncertainty quantification, and closure modeling. He will also demonstrate how those technologies can be effectively applied to weather and climate, addressing crucial problems in those areas.
Note: The research work presented in this talk is based on joint and interdisciplinary research work of several teams at Google Research.