Richard Y Zhang

Richard Y Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois, affiliated with the Coordinated Science Lab. His research focuses on optimization theory, nonconvex optimization, semidefinite programming, and their applications in power systems, machine learning, and control systems. Key topics include low-rank matrix recovery, spurious local minima analysis, and robust optimization methods. He has received the NSF CAREER Award in 2021 for early career achievements.

Research Interests:

• Nonconvex Optimization
• Semidefinite Programming
• Power System Analysis
• Low-Rank Matrix Recovery
• Machine Learning Algorithms
• Control Systems Design

Recent work emphasizes certified optimization methods with global guarantees, adversarial machine learning robustness, and efficient algorithms for large-scale systems. His publications explore topics like phase synchronization in power systems, preconditioned gradient descent techniques, and sparse semidefinite program optimizations.

• Scientific Awards: NSF CAREER Award (2021)

Advising and Grants: While specific student names aren’t listed, his research group focuses on cutting-edge optimization theory with applications to energy systems and data science. Collaborations include work on power grid stability, neural network certification, and large-scale convex/nonconvex problem solving.

Labs/Teams: Active in the Coordinated Science Lab’s optimization and control research clusters.>