معرفی
Jun-Kun Wang is an Assistant Professor at the University of California, San Diego (UCSD), with a joint appointment in the Department of Electrical and Computer Engineering and the Halicioğlu Data Science Institute. He joined UCSD in July 2023, previously serving as a postdoc at Yale University. His research focuses on optimization, sampling, and machine learning, emphasizing acceleration techniques and theoretical guarantees. He explores connections between optimization and areas like no-regret learning, sampling, and hypothesis testing.
Education: PhD in Computer Science from Georgia Tech (advised by Jacob Abernethy), M.S. in Communication Engineering and B.S. in Electrical Engineering from National Taiwan University.
Research Interests: Acceleration in optimization and sampling, trustworthy machine learning, momentum methods, and algorithmic convex optimization. His work bridges theoretical foundations and practical applications, with publications in top-tier venues like COLT, ICML, ICLR, and NeurIPS.
Teaching: Courses include ECE 174 (Linear/Nonlinear Optimization), ECE 273 (Convex Optimization), and DSC 211 (Optimization). His lectures cover topics such as gradient descent, duality theory, mirror descent, and non-convex optimization.
Lab/Team: Leads the Optimization and Machine Learning Group, advising PhD students Can Chen and Maria-Eleni Sfyraki, and MS student Yi Liu. His group focuses on theoretical and applied aspects of optimization algorithms.
