An assistant professor of electrical and computer engineering, Jin draws on the complex mathematical subfields of statistics, optimization and game theory to uncover the obscure innerworkings of these models. One important area of his research focuses on making reinforcement learning — one of the most common approaches to training a machine learning model — work better under real world environments.

His work has appeared in several of the most important conferences in machine learning and computer science theory, giving his work high visibility and broad recognition within those fields. In just four years, Jin has also established himself as a respected teacher, winning two Dean’s Commendation Awards. His research group includes six Ph.D. students: three from ECE, one from computer science and two from operations research and financial engineering. “He’s a star in interdisciplinary research,” said James Sturm, chair of electrical and computer engineering.

Jin was previously the recipient of an NSF CAREER Award, a 2022 SEAS Innovation Award and Best Paper Awards from the workshops at the International Conference on Learning Representations and the International Conference on Machine Learning. “This is an outstanding record,” Sturm said, “especially so for someone who began at Princeton right after his Ph.D., with no postdoc.”