讲者简介：Shuiwang Ji is currently a Professor in the Department of Computer Science & Engineering, Texas A&M University. He received the Ph.D. degree in Computer Science from Arizona State University in 2010. His research interests include machine learning, deep learning. Dr. Ji received the National Science Foundation CAREER Award in 2014. Currently, he serves as an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), ACM Transactions on Knowledge Discovery from Data (TKDD), and ACM Computing Surveys (CSUR). He regularly serves as an Area Chair or equivalent roles for AAAI Conference on Artificial Intelligence (AAAI), International Conference on Learning Representations (ICLR), International Conference on Machine Learning (ICML), International Joint Conference on Artificial Intelligence (IJCAI), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), and Annual Conference on Neural Information Processing Systems (NeurIPS). Dr. Ji is a Distinguished Member of ACM and a Senior Member of IEEE.
报告题目: Deep Learning for Quantum Chemistry and Physics
报告摘要: Quantum chemistry and physics study atomic and subatomic behaviors of particle sxystems. While physics-based equations and computations can be used, they are computationally prohibitive, limiting current studies to many-body systems with a small number of particles. In this talk, I will talk about our work on using deep learning to expedite scientific discoveries in quantum chemistry and physics. We develop and use various graph neural networks to make predictions for quantum systems. We can also generate molecular and quantum systems using generative models. To aid domain understanding, we develop explainability methods to interpret the results of deep models.