Dr. Yuhua Zhu

Biography

Yuhua Zhu is a tenure-track Assistant Professor whose research focuses on interface partial differential equations and machine learning. He is particularly interested in using differential equations to understand and design efficient reinforcement learning algorithms, nonconvex optimization algorithms, and generative algorithms. In addition to his teaching responsibilities, he organizes the Data Theory Seminar held once a quarter, providing a platform for discussing current topics in data science. Zhu has a strong academic background, having completed his Ph.D. in Mathematics at the University of Wisconsin-Madison where he was advised by Shi Jin, and he held a postdoctoral position at Stanford University mentored by Lexing Ying. His research interests extend to continuous-time reinforcement learning, multi-armed bandits, gradient-free optimization, uncertainty quantification, and kinetic equations.

Research Interests