Research
Research
Modern Nonconvex Nondifferentiable Optimization.
Ying Cui and Jong-Shi Pang. MOS/SIAM Series on Optimization, 2021. [link]
Subgradient Regularization: A Descent-Oriented Subgradient Method for Nonsmooth Optimization.
Hanyang Li and Ying Cui. [link]
Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction.
Jake Roth and Ying Cui. [link] [code]
Ying Cui, Tim Hoheisel, Tran TA Nghia, Defeng Sun. [link]
Variational Theory and Algorithms for a Class of Asymptotically Approachable Nonconvex Problems.
Hanyang Li and Ying Cui. Mathematics of Operations Research, 2025. [link]
On O(n) Algorithms for Projection onto the Top-k-sum Sublevel Set.
Jake Roth and Ying Cui. Mathematical Programming Computation, 2025. [link] [code]
Analysis of a Class of Minimization Problems Lacking Lower Semicontinuity.
Shaoning Han, Ying Cui and Jong-Shi Pang. Mathematics of Operations Research, 2025. [link]
Towards Global Solutions for Nonconvex Two-Stage Stochastic Programs: A Polynomial Lower Approximation Approach.
Suhan Zhong, Ying Cui and Jiawang Nie. SIAM Journal on Optimization, 2024. [link]
No Panic in Pandemic: The Impact of Individual Choice on Public Health Policy and Vaccine Priority.
Miao Bai, Ying Cui and Guangwen Kong, Anthony Zhenhuan Zhang. Manufacturing & Service Operations Management, 2024. [link]
First Place in Best Paper Competition, Post-Pandemic Supply Chain and Healthcare Management Conference (2021).
Best Problem-Driven Analytical Research Paper Award (2021).
Runner-up of the POMS HOCM Best Paper Award (2021).
Media mention: An editorial article based on this research has been published in THE HILL .
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models.
Yangjing Zhang, Ying Cui, Kim-Chuan Toh and Bodhisattva Sen. Journal of Machine Learning Research, 2024. [link] [code]
A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse.
Hanyang Li and Ying Cui. SIAM Journal on Optimization, 2024. [link] [code]
Runner-up (Hanyang Li), Dupacova-Prekopa Best Student Paper Prize in Stochastic Programming (2023).
Third Place in INFORMS Junior Faculty Interest Group (JFIG) Paper Competition (2022).
Comparing Solution Paths of Sparse Quadratic Minimization with a Stieltjes Matrix.
Ziyu He, Shaoning Han, Andres Gomez, Ying Cui and Jong-Shi Pang. Mathematical Programming, 2024. [link]
Convex and Nonconvex Risk Based Statistical Learning at Scale.
Can Wu, Ying Cui, Donghui Li and Defeng Sun. INFORMS Journal on Computing, 2023. [link] [code]
A Nonconvex and Nonsmooth Approach for Affine Chance Constrained Stochastic Programs.
Ying Cui, Junyi Liu and Jong-Shi Pang. Set-Valued and Variational Analysis, 2022. [link]
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. [link]
On Degenerate Doubly Nonnegative Projection Problems.
Ying Cui, Ling Liang, Defeng Sun and Kim-Chuan Toh. Mathematics of Operations Research, 2021. [link]
Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization.
Zhengling Qi, Ying Cui, Yufeng Liu and Jong-Shi Pang. Mathematics of Operations Research, 2021. [link]
Two-Stage Stochastic Programming with Linearly Bi-Parameterized Quadratic Recourse.
Junyi Liu, Ying Cui, Jong-Shi Pang and Suvrajeet Sen. SIAM Journal on Optimization, 2020. [link]
Multi-Composite Nonconvex Optimization for Training Deep Neural Networks.
Ying Cui, Ziyu He and Jong-Shi Pang. SIAM Journal on Optimization, 2020. [link]
Computing the Best Approximation Over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone.
Ying Cui, Defeng Sun and Kim-Chuan Toh. SIAM Journal on Optimization, 2019. [link]
Estimation of Individualized Decision Rules Based on an Optimized Covariate-dependent Equivalent of Random Outcomes.
Zhengling Qi, Ying Cui, Yufeng Liu and Jong-Shi Pang. SIAM Journal on Optimization, 2019. [link]
On the R-Superlinear Convergence of the KKT Residuals Generated by the Augmented Lagrangian Method for Convex Composite Conic Programming.
Ying Cui, Defeng Sun and Kim-Chuan Toh. Mathematical Programming, 2019. [link]
Composite Difference-Max Programs for Modern Statistical Estimation Problems.
Ying Cui, Jong-Shi Pang and Bodhisattva Sen. SIAM Journal on Optimization, 2018. [link]
Quadratic Growth Conditions for Convex Matrix Optimization Problems Associated with Spectral Functions.
Ying Cui, Chao Ding and Xinyuan Zhao. SIAM Journal on Optimization, 2017. [link]
On the Convergence Properties of a Majorized ADMM for Linearly Constrained Convex Optimization Problems with Coupled Objective Functions.
Ying Cui, Xudong Li, Defeng Sun and Kim-Chuan Toh. Journal of Optimization Theory and Applications, 2016. [link]