Hao Wang

Machine learning, deep learning, large language models & foundation models, AI for healthcare, trustworthy & safe AI, Bayesian deep learning, interpretable AI, causality

• ACM SIGKDD 2025 Test of Time Award (2025). 
• Microsoft Research AI & Society Fellowship (2024). 
• NSF CAREER Award (2024). 
• NIH R01 Award (2024). 
• Nokia Faculty Research Award (2024). 
• Meta Faculty Research Award (2024). 
• Ten Notable Advances in 2022 by Nature Medicine (selected from Nature, Lancet, Science, NEJM). 
• Best Paper Finalist CVPR 2022. 
• Amazon Faculty Research Award (2020). 
• PhD Research Excellence Award (1 awardee in the School of Engineering). 

Wang's research focuses on statistical machine learning, Bayesian deep learning, and trustworthy AI, with applications in large language models, computer vision, healthcare, recommender systems, and time series analysis. His work on Bayesian deep learning for recommender systems and personalized modeling has inspired thousands of follow-up studies, becoming the most cited paper from the 2015 ACM SIGKDD Conference on Knowledge Discovery and Data Mining and receiving the 2025 ACM SIGKDD Test of Time Award. 

His research has also been recognized with the NSF CAREER Award, an NIH R01 Award, the Amazon Faculty Research Award, the Microsoft AI & Society Fellowship, the Microsoft Fellowship in Asia, and the Baidu Research Fellowship. Before joining Illinois, Wang was an assistant professor in the Department of Computer Science at Rutgers University and a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He received his PhD in computer science from the Hong Kong University of Science and Technology, where he was the sole recipient of the School of Engineering PhD Research Excellence Award.

T. Zhang*, H. Shi*, Y. Wang, H. Wang, X. He, Z. Li, H. Chen, L. Han, K. Xu, H. Zhang, D. N. Metaxas, H. Wang. TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning. ICLR 2026.

V. Venkataramani, H. Shi, Z. Ke, A. Xu, X. He, Y. Zhou, S. Yavuz, H. Wang*, S. Joty*. MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems. ICML 2026.

H. Wang*, S. Tan*, H. Wang. Probabilistic Conceptual Explainers: Towards Trustworthy Conceptual Explanations for Vision Foundation Models. ICML 2024.

W. Lin, H. Lan, H. Wang, B. Li. OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks. CVPR 2022. (Best Paper Finalist).

Y. Yang, Y. Yuan, G. Zhang, H. Wang, ..., D. Katabi. Artificial Intelligence-Enabled Detection and Assessment of Parkinson’s Disease Using Nocturnal Breathing Signals. Nature Medicine, 2022. (Selected as one of the “Ten Notable Advances in 2022” by Nature Medicine.)

M. Zhao*, K. Hoti*, H. Wang, A. Raghu, D. Katabi. Assessment of Medication Self-Administration Using Artificial Intelligence. Nature Medicine, 2021.

H. Wang, N. Wang, D.-Y. Yeung. Collaborative Deep Learning for Recommender Systems. KDD 2015. (ACM KDD 2025 Test of Time Award. Most cited paper among all papers at KDD 2015.)

H. Wang, D.-Y. Yeung. Towards Bayesian Deep Learning: A Framework and Some Existing Methods. IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(12): 3395-3408, 2016.

H. Wang, X. Shi, D.-Y. Yeung. Natural-Parameter Networks: A Class of Probabilistic Neural Networks. NIPS/NeurIPS 2016.

X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, W.-C. Woo. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. NIPS/NeurIPS 2015. (Second most cited paper among all papers at NIPS 2015.)

Keynote talk at ICML 2025 Workshop on Foundation Models for Structured Data: “(Hierarchical) Bayesian Deep Learning: From Reliable Neural Networks to Interpretable Foundation Models”

ACM SIGKDD 2025 Test of Time Award Talk: “A Decade of Deep Recommender Systems: Foundations and Trends”