Ying Cui

Ying Cui serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley, where she conducts foundational research in data science optimization. She holds editorial responsibilities as associate editor for Mathematical Programming and serves as vice chair of Nonlinear Optimization for the INFORMS Optimization Society.

Her academic background includes:

• Ph.D. in Mathematics from the National University of Singapore
• B.Sc. in Mathematics from Zhejiang University, China

Dr. Cui's research centers on mathematical optimization frameworks for complex decision systems, with particular expertise in nonsmooth/semidefinite variational analysis, stochastic programming under uncertainty, and computational methods for nonconvex machine learning problems. Her work bridges theoretical advances with practical applications in operations research and statistical estimation, emphasizing algorithmic efficiency for modern data challenges.

Her major recognitions include:

• Mathematical Programming Meritorious Service Award (2023)
• NSF CISE Research Initiation Initiative Award (2022)
• INFORMS JFIG Paper Prize (Third Place, 2022)
• Louis Chen Hsiao Yun Best Dissertation Prize (NUS, 2016)

Dr. Cui secured significant research funding through the NSF CRII Award (2022) and actively mentors graduate students, evidenced by her finalist status as faculty advisor for the Dupacova-Prekopa Best Student Paper Prize (2023). She has co-authored the influential monograph Modern Nonconvex Nondifferentiable Optimization in the MOS/SIAM Series on Optimization, establishing foundational methodologies for contemporary optimization challenges.