Jun-Kun Wang joined UC San Diego in July 2023. He has a joint appointment with the Halicioğlu Data Science Institute and the Department of Electrical and Computer Engineering. Prior to that, he was a postdoc at Yale University. He received his Ph.D. in CS from Georgia Tech and holds an M.S. in Communication Engineering and a B.S. in Electrical Engineering from National Taiwan University.

His research aims to make algorithms faster, build robust theoretical foundations, and overcome issues such as model mis-specification or distribution shifts encountered during the deployment of machine learning methods in real-world contexts. He is particularly interested in the interplay between optimization, sampling, and machine learning. He likes discovering connections between different research areas, e.g., optimization and no-regret learning, optimization and sampling, optimization and tackling distribution shifts. One of his research focuses is acceleration in optimization, sampling, and machine learning, which encompasses designing momentum methods in both convex and non-convex optimization as well as accelerating algorithms through innovative parameter value schemes. He also focuses on optimization for trustworthy machine learning.