COLLOQUIUM: Hao Wang, "Bayesian Deep Learning: From Reliable Neural Networks to Interpretable Foundation Models"

Abstract: 
While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning, and planning require an even higher level of intelligence. The past decade has seen major advances in many perception tasks using deep learning models. In terms of higher-level inference, however, probabilistic graphical models, with their ability to expressively describe properties of variables and various probabilistic relations among variables, are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, we have been exploring along a research direction, which we call Bayesian deep learning, to tightly integrate deep learning and Bayesian models within a principled probabilistic framework. In this talk, I will present the proposed unified framework and some of our recent work on Bayesian deep learning with various applications including interpretable large language models, network analysis, and healthcare.

Bio:
Hao Wang is currently an Associate Professor in the School of Information Sciences at the University of Illinois Urbana-Champaign. Previously he was an Assistant Professor in the Department of Computer Science at Rutgers University and a Postdoctoral Associate at the Computer Science & Artificial Intelligence Lab (CSAIL) of MIT, working with Dina Katabi and Tommi Jaakkola. He received his PhD degree from the Hong Kong University of Science and Technology, as the sole recipient of the School of Engineering PhD Research Excellence Award in 2017. He has been a visiting researcher in the Machine Learning Department of Carnegie Mellon University. His research focuses on statistical machine learning and hierarchical Bayesian deep learning, with broad applications on healthcare, recommender systems, network analysis, etc. His work on Bayesian deep learning for recommender systems has inspired thousands of follow-up works, receiving the Test of Time Award at KDD 2025. His research was recognized and supported by the Microsoft Fellowship in Asia, the Baidu Research Fellowship, the Amazon Faculty Research Award, the Microsoft AI & Society Fellowship, the NSF CAREER Award, and an NIH R01 Award.