随机优化学会(Stochastic Programming Society)，Dupacova-Prekopa最佳学生论文奖，Finalist, 2019

清华大学优秀班主任，二等奖，2024

美国南加州大学，工业与系统工程，博士，2019

中国科学技术大学，少年班学院，统计，学士，2015

非线性规划基础（本科生课程）

运筹学(1) （确定性方法） （本科生课程）

刘俊驿老师的研究兴趣主要集中在不确定环境下数据驱动的决策模型与方法，包括：

方法：随机优化、非凸非光滑优化、风险测度优化等

应用：机器学习、决策规则学习、供应链管理、新能源电网管理等

主持国家自然科学基金项目3项（青年、面上、国家青年人才计划各1项），参与2项国家自然科学基金项目，主持一项企业资助课题。

11. Zhang, Y., Liu, J.*, Zhao, X. (2026) Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information. Accepted by Operations Research.  https://arxiv.org/abs/2304.13646

10. He, Z.*, Liu, J. , Pang, J-S. (2025) Adaptive Importance Sampling Based Surrogation Methods for Bayesian Hierarchical Models, via Logarithmic Integral Optimization. Mathematical Programming Series A.

9. Fang, Y., Liu, J.*, Pang, J-S (2025) Treatment learning with Gini constraints by Heaviside composite optimization and a progressive method. Computational Optimization and Applications.

8. Cui, Y., Liu, J.,  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., Pang, J-S.* (2022) Nonconvex and Nonsmooth Approaches for Affine Chance Constrained Stochastic Programs. Set-valued and Variational Analysis.

6. Liu, J.*, Pang, J-S. (2022) Risk-based Robust Statistical Learning By Stochastic Difference-of-Convex Value-Function Optimization. Operations Research.

5. Liu, J.*, Cui, Y., 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., Sen, S. (2021) Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty. Mathematics of Operations Research.

3. Liu, J., 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., 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., Sen, S.* (2018) Coalescing Data and Decision Sciences for Analytics, INFORMS TutORials in Operations Research.

1. Shen, B., Liu, J.*. Adaptive Learning-based Surrogate Method for Stochastic Programs with Implicitly Decision-dependent Uncertainty (2025) https://arxiv.org/abs/2505.07298

2. Zheng, K.,  Liu, J.*,  Wang, Y.,  Pang, J-S. Solving Constrained Affine Heaviside Composite Optimization Problems by a Progressive IP Approach (2026) https://arxiv.org/abs/2605.06020

3. You L., Liu, J.*. Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contamination (2026) https://arxiv.org/abs/2601.11420

4. Liu, J.*, Qin, H., Liu, J., Chou M.,  Pang, J-S. Offline Policy Learning with Weight Clipping and Heaviside Composite Optimization (2026) https://arxiv.org/abs/2601.12117