My research focuses on the mathematical foundation of data science with emphasis on optimization techniques for operations research, machine learning and statistical estimations. This includes computational methods for large scale semidefinite programming, stochastic programming and modern nonconvex nondifferentiable optimization problems. I am also interested in second order variational analysis for matrix optimization problems.

Ying Cui is currently an assistant professor in the Department of Industrial and Systems Engineering at the University of Minnesota. Prior to that appointment, she was a postdoc research associate in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. 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."

Education

Ph.D., Mathematics, National University of Singapore, 2016
B.Sc., Mathematics, Zhejiang University, 2011
 

Honors and Awards

• Third Place, INFORMS Junior Faculty Interest Group (JFIG) Paper Prize (2022)
• Best Problem-Driven Analytical Research Paper Award (2021)
• First Place in Best Paper Competition, Post-Pandemic Supply Chain and Healthcare Management Conference (2021)
• Runner-up of the POMS HOCM Best Paper Award (2021)
• Louis Chen Hsiao Yun Best Dissertation Prize, National University of Singapore (2016)

Selected Publications

Ying Cui, Ling Liang, Defeng Sun, Kim-Chuan Toh, "On degenerate doubly nonnegative projection problems," Mathematics of Operations Research.

Zhengling Qi, Ying Cui, Yufeng Liu, Jong-Shi Pang, “Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization,’’ forthcoming in Mathematics of Operations Research, 2021.

Ying Cui, Defeng Sun and Kim-Chuan Toh, "Computing the Best Approximation Over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone," SIAM Journal on Optimization, 29 (2019) 2785-2813.

Zhengling Qi, Ying Cui, Yufeng Liu and Jong-Shi Pang, "Estimation of Individualized Decision Rules Based on an Optimized Covariate-dependent Equivalent of Random Outcomes," SIAM Journal on Optimization, 29 (2019) 2337-2362.

Ying Cui, Defeng Sun and Kim-Chuan Toh, "On the R-Superlinear Convergence of the KKT Residuals Generated by the Augmented Lagrangian Method for Convex Composite Conic Programming," Mathematical Programming, Series A,178 (2019) 381-415.

Ying Cui, Jong-Shi Pang and Bodhisattva Sen, "Composite Difference-Max Programs for Modern Statistical Estimation Problems," SIAM Journal on Optimization, 28 (2018) 3344-3374.

Ying Cui and Defeng Sun, "A Complete Characterization of the Robust Isolated Calmness of Nuclear Norm Regularized Convex Optimization Problems," Journal of Computational Mathematics, 36 (2018) 441-458.

Ying Cui, Chao Ding and Xinyuan Zhao, "Quadratic Growth Conditions for Convex Matrix Optimization Problems Associated with Spectral Functions," SIAM Journal on Optimization, 27 (2017) 2332-2355.

Ying Cui, Xudong Li, Defeng Sun and Kim-Chuan Toh, "On the Convergence Properties of a Majorized ADMM for Linearly Constrained Convex Optimization Problems with Coupled Objective Functions," Journal of Optimization Theory and Applications, 169 (2016) 1013-1041.

Ying Cui, Chenlei Leng and Defeng Sun, "Sparse Estimation of High-dimensional Correlation Matrices," Computational Statistics & Data Analysis, 93 (2016) 390-403.