News

Abstract

AI has emerged as the most transformative and revolutionary technique, reshaping many aspects of our lives. Its intersection with science, particularly physics, has opened new avenues for understanding our world and universe. This understanding is grounded in centuries of exploration by brilliant minds. Physics studies today predominantly rely on rigorous methods founded on universal physical laws. I will discuss integrating advanced learning techniques, notably Bayesian machine learning, into computational physics in this presentation. This integration is crucial in an interdisciplinary field that combines mathematics, physics, and computer science to address meaningful, real-world problems. As the first principle, physics offers novel techniques and insights for tackling complex tasks in complex, structured data analysis. I envision synergizing physics and probabilistic learning to create a formidable tool for exploring new frontiers.

Biography

Shibo Li is an Assistant Professor in the Department of Computer Science at Florida State University, where he leads the PML4SC (Probabilistic Machine Learning for Scientific Computing) research group. His research bridges machine learning and computational physics, focusing on probabilistic modeling, surrogate modeling, operator learning, and physics-informed machine learning. Dr. Li received his Ph.D. in Computer Science from the University of Utah under the advisement of Dr. Shandian Zhe, his M.S. from the University of Pittsburgh, and his B.E. from South China University of Technology. His work has been published in top-tier venues including ICML, NeurIPS, AISTATS, and SIGKDD.