Baoyu Zhou

Baoyu Zhou is an Assistant Professor of Industrial Engineering at Arizona State University (ASU), School of Computing and Augmented Intelligence. He holds a PhD in Industrial and Systems Engineering from Lehigh University (2018–2022), an M.S. in Industrial Engineering from Lehigh University (2016–2018), and a B.E. in Mechanical Engineering from Shanghai Jiao Tong University (2012–2016). His research focuses on developing efficient algorithms for large-scale, stochastic, and constrained optimization problems, with contributions to sequential quadratic programming, nonsmooth optimization, and derivative-free methods.

Before joining ASU, Zhou was a postdoctoral researcher at the University of Michigan (Department of Industrial and Operations Engineering) and the University of Chicago (Booth School of Business). He has received the Van Hoesen Family Best Publication Award and the Elizabeth V. Stout Dissertation Award. His work bridges optimization theory and practical applications, emphasizing scalability and robustness in complex systems.

Zhou teaches courses such as IEE 470: Stochastic Operations Research at ASU and has guest-lectured at the University of Michigan. He actively contributes to the academic community through organizing conference sessions, reviewing for top journals, and participating in workshops at NeurIPS and SIAM. His group currently advises three PhD students focusing on optimization algorithms and their applications.

Key research areas include large-scale continuous optimization, constrained stochastic optimization, and derivative-free methods. His publications span journals like SIAM Journal on Optimization and INFORMS Journal on Optimization, addressing challenges in nonlinear systems, variance reduction, and algorithmic convergence.