Richard Y. Zhang

Richard Y. Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. He earned his PhD in Electrical Engineering & Computer Science from MIT in 2017 and completed a postdoc at UC Berkeley's Industrial Engineering & Operations Research Department (2017-2019). His research bridges optimization and machine learning, focusing on low-rank optimization as both a theoretical framework for signal recovery and a computational tool for large-scale algorithms in power systems and neural networks.

Current research explores nonconvex optimization landscapes, with applications in unsupervised learning, adversarially robust neural networks, and electric grid state estimation. His group has developed algorithms for dictionary learning, semidefinite programming relaxations (SDP-CROWN), and preconditioned optimization methods. Key collaborators include researchers from MIT, UC Berkeley, and the Power Systems Engineering Research Center (PSERC).

• NSF CAREER Award (2021)
• Area Chair: NeurIPS (2021-present), ICML (2023-present), ICLR (2024-present)
• Advising: Hong-Ming Chiu, Iven Guzel, June Hou; Alumni: Gavin Zhang (PhD '24, now at Meta)

His recent work includes groundbreaking contributions to the theoretical understanding of low-rank matrix recovery and practical applications in power system optimization. He teaches ECE 330 (Power Circuits & Electromechanics) and ECE 530 (Large-Scale System Analysis), emphasizing the interplay between theory and real-world implementation.