Advances in AI are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). In this talk, I will present our work on understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, PDE) scales. My talk will focus on how to capture symmetries in physical systems. I will also discuss a few other technical challenges, including explainability, out-of-distribution generalization, and foundation and large language models.
Dr. Shuiwang Ji is currently a Professor and Presidential Impact Fellow in the Department of Computer Science & Engineering, Texas A&M University. His research interests include machine learning and AI for science. Dr. Ji received the National Science Foundation CAREER Award in 2014. Currently, he serves as an Associate Editor for TPAMI, TKDD, and ACM Computing Surveys. He regularly serves as an Area Chair for ICLR, ICML, and NeurIPS, and senior PC for KDD. Dr. Ji is a Fellow of IEEE and AIMBE, and a Distinguished Member of ACM.
Part of the Illinois Computer Science Speakers Series. Faculty Host: Heng Ji
Meeting ID: 883 4082 0127
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