Ying Cui

Ying Cui is an assistant professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Before joining Berkeley, she was an assistant professor in the Department of Industrial and Systems Engineering at the University of Minnesota. She was a postdoc research associate in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California working with Professor Jong-Shi Pang. Cui completed her PhD in Mathematics at the National University of Singapore.

Her research focuses on the mathematical foundation of data science with emphasis on optimization techniques for operations research, machine learning and statistical estimations. She is particularly interested in leveraging nonsmoothness to design efficient algorithms for large scale nonlinear optimization problems. She is the co-author of the recently published monograph ``Modern Nonconvex Nondifferenable Optimization’'.

1. A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse. Hanyang Li and Ying Cui. SIAM Journal on Optimization, 2023.

2. Solving Nonsmooth Nonconvex Compound Stochastic Programs with Applications to Risk Measure Minimization. Junyi Liu, Ying Cui and Jong-Shi Pang. Mathematics of Operations Research, 2022.

3. On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models. Yangjing Zhang, Ying Cui, Kim-Chuan Toh and Bodhisattva Sen (2022).

4. No Panic in Pandemic: The Impact of Individual Choice on Public Health Policy and Vaccine Priority. Anthony Zhenhuan Zhang, Miao Bai, Ying Cui and Guangwen Kong (2021).

5. Modern Nonconvex Nondifferentiable Optimization. Ying Cui and Jong-Shi Pang. MOS/SIAM Series on Optimization, 2021.