Research
Preprints
Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information (2023) [link]
Yiyang Zhang, Junyi Liu, Xiaobo Zhao. (Submitted)
Adaptive Importance Sampling Based Surrogation Methods for Bayesian Hierarchical Models, via Logarithmic Integral Optimization (2023) [link]
Ziyu He, Junyi Liu, Jong-Shi Pang. (Submitted)
Journal Publications
The Minimization of Piecewise Functions: Pseudo Stationarity.
Ying Cui, Junyi Liu, and Jong-Shi Pang. Journal of Convex Analysis, 2023. (in honor of Roger J.-B. Wets' 85th Birthday) [link]
Risk-based Robust Statistical Learning By Stochastic Difference-of-Convex Value-Function Optimization.
Junyi Liu, and Jong-Shi Pang. Operations Research, 2023. [link]
Nonconvex and Nonsmooth Approaches 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]
Coupled Learning Enabled Stochastic Optimization with Endogenous Uncertainty.
Junyi Liu, Guangyu Li, and Suvrajeet Sen. Mathematics of Operations Research, 2022. [link]
Two-stage Stochastic Programming with Linearly Bi-parameterized Recourse Functions.
Junyi Liu, Ying Cui, Jong-shi Pang, and Suvrajeet Sen.
SIAM Journal on Optimization, 2020. [link]
Finalist, Dupacova-Prekopa Best Student Paper Prize in Stochastic Programming
Asmptotical Convergence Rate of Stochastic Decomposition Algorithm for Two-stage Stochastic Quadratic Programming.
Junyi Liu, and Suvrajeet Sen.
SIAM Journal on Optimization, 2020. [link]
Coalescing Data and Decision Sciences for Analytics.
Yunxiao Deng, Junyi Liu, and Suvrajeet Sen.
TutORials at INFORMS, 2018. [link]