师资队伍

教师队伍

刘俊驿副教授

  • 姓名:刘俊驿

  • 办公电话: +86-10-62787546

  • 邮箱:junyiliu@tsinghua.edu.cn

  • 传真号码:

  • 地点:清华大学舜德楼515

  • 教师主页:

个人简介

刘俊驿,博士,2019年于美国南加州大学获得工业与系统工程博士学位。研究兴趣集中在不确定环境下的数据驱动的随机优化模型与方法。

刘俊驿老师每年都有计划招收博士和硕士, 欢迎具有优秀的数学与统计基础,擅长编程,对数据科学和随机优化相关研究感兴趣的同学发送简历到junyiliu@tsinghua.edu.cn。

所获奖励

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

教育背景

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

工作经历

2019年9月-2021年3月,美国南加州大学工业与系统工程系,博士后

2021年4月-2022年12月,清华大学工业工程系,助理教授

2022年12月至今,清华大学工业工程系,副教授

讲授课程

非线性规划基础(本科生课程)

研究兴趣

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、供应链管理、收益管理、个性化医疗等

Publications

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.

Preprints

2. Zhang, Y., Liu, J. *, Zhao, X. Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information (2023) https://arxiv.org/abs/2304.13646

1. He, Z.*, Liu, J. , Pang, J-S. Adaptive Importance Sampling Based Surrogation Methods for Bayesian Hierarchical Models, via Logarithmic Integral Optimization (2023) https://optimization-online.org/?p=23119