8. Cui, Y., Liu, J. , and Pang, J-S*. (2023) The Minimization of Piecewise Functions: Pseudo Stationarity. Journal of Convex Analysis. (in honor of Roger J.-B. Wets' 85th Birthday)
7. Cui, Y., Liu, J. and Pang, J-S.* (2022) Nonconvex and Nonsmooth Approaches for Affine Chance Constrained Stochastic Programs. Set-valued and Variational Analysis.
6. Liu, J.* and Pang, J-S. (2022) Risk-based Robust Statistical Learning By Stochastic Difference-of-Convex Value-Function Optimization. Operations Research.
5. Liu, J.*, Cui, Y., and Pang, J-S. (2022) Solving Nonsmooth and Nonconvex Compound Stochastic Programs with Applications to Risk Measure Minimization. Mathematics of Operations Research.
4. Liu, J.*, Li, G. and Sen, S. (2021) Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty. Mathematics of Operations Research.
3. Liu, J. and Sen, S.* (2020) Asymptotic Convergence Rate of Stochastic Decomposition Algorithm for Two-stage Stochastic Quadratic Programming. SIAM Journal on Optimization.
2. Liu, J., Cui, Y.*, Pang, J-S. and Sen, S. (2020) Two-stage Stochastic Programming with Linearly Bi-parameterized Recourse Functions. SIAM Journal on Optimization. (Finalist of 2019 Dupacova-Prekopa Best Student Paper Prize in Stochastic Programming)
1. Deng, Y., Liu, J. and Sen, S.* (2018) Coalescing Data and Decision Sciences for Analytics, INFORMS TutORials in Operations Research.