Nan Jiang
About
Nan Jiang (姜楠) is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on building the theoretical foundations of reinforcement learning (RL), particularly in the function-approximation setting, with an emphasis on developing sample-efficient algorithms by drawing from statistical learning theory. He earned his Ph.D. in Computer Science and Engineering from the University of Michigan and was a postdoctoral researcher at Microsoft Research New York City before joining UIUC. Jiang's contributions have been recognized with several honors, including the NSF CAREER Award, a Sloan Research Fellowship, and a Google Research Scholar Award. He also serves as an action editor for the Journal of Machine Learning Research and an editor for Foundations and Trends in Machine Learning. Beyond research, he is deeply committed to education and mentorship, having received multiple teaching awards at UIUC, and his work continues to shape both theoretical and applied aspects of reinforcement learning. Read more