Hao Wang

Hao Wang is an Assistant Professor in the Department of Computer Science at Rutgers University, where he leads the Machine Learning Lab. His research spans statistical machine learning, deep learning, and large language models (LLMs), with applications in trustworthy & safe AI, healthcare, recommender systems, computer vision, natural language processing, network analysis, and data mining. He previously held postdoctoral positions at MIT's Computer Science & Artificial Intelligence Lab and received his PhD in CSE from Hong Kong University of Science and Technology (HKUST) under Prof. Dit-Yan Yeung, with earlier studies at Shanghai Jiao Tong University under Prof. Wu-Jun Li.

• Education: BS (Shanghai Jiao Tong, 2013), PhD (HKUST, 2016)
• Current Role: Assistant Professor, Rutgers University
• Research Themes: Bayesian deep learning, causal reasoning, domain adaptation, and safety in AI systems

His work emphasizes Bayesian deep learning for interpretable and robust AI, including applications in healthcare (e.g., cancer diagnosis, Parkinson’s disease detection) and foundational contributions to domain adaptation, continual learning, and trustworthy LLMs. Recent publications focus on multimodal LLMs, calibration techniques, and concept-based domain adaptation frameworks.

Notable awards include the ACM SIGKDD 2025 Test of Time Award, NIH R01 Award, NSF CAREER Award, and Microsoft Research AI & Society Fellowship. He has also secured grants like NSF IIS-2127918 and Amazon Faculty Research Award.

Hao Wang actively recruits PhD students starting in Fall 2025 and collaborates across disciplines in AI, computer vision, and healthcare. His lab's outputs include survey papers on Bayesian deep learning and practical implementations for recommender systems.