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Junyi Liu, Associate Professor

Contacts

Name: Junyi Liu Phone: +86-10-62787546 E-mail: junyiliu@tsinghua.edu.cn Fax: Address: Room 515, Shunde Building, Tsinghua University Homepage:

Biography

I am currently an (untenured) associate professor at the Department of Industrial Engineering at Tsinghua University.

My research of interest lies in the intersection of stochastic programming, statistics, and machine learning, with a focus on nonconvex and nonsmooth stochastic optimization, and applications.  

I was a postdoctoral researcher at the Industrial and Systems Engineering Department at the University of Southern California, working with Prof. Jong-Shi Pang from Sep 2019 to March 2021. I obtained my Ph.D. degree in 2019 at the University of Southern California, supervised by Prof. Suvrajeet Sen, and obtained a B.S. degree in 2015 with a major in Statistics, at the School of Gifted Young at the University of Science and Technology of China.


Honors and Awards

1. Finalist, Dupacova-Prekopa Best Student Paper Prize in Stochastic Programming, 2019.
(Awarded by the Committee on Stochastic Programming for the paper “Two-stage stochastic programming with linearly bi-parameterized recourse functions". Committee on Stochastic Programming selects 4 finalists every 3 years in recognizing the outstanding student-authored papers in stochastic programming.)
2. Second place, Daniel J. Epstein Institute Research Festival, University of Southern California, 2019.
3. Viterbi Graduate School Graduate Fellowship, University of Southern California, 2015.    

Educational Background

Ph.D. Industrial and Systems Engineering, University of Southern California, 2019
B.S., Statistics, School of Gifted Young, University of Science and Technology of China, 2015    

Employment History

Associate Professor, Industrial Engineering, Tsinghua University, December 2022 – Now

Assistant Professor, Industrial Engineering, Tsinghua University, April 2020 – December 2022

Postdoc Associate, Industrial and Systems Engineering, University of Southern California, Sep 2019 - March 2021    

Courses

Foundations of Nonlinear Programming (an undergraduate course, 2021 fall semester)    

Research Interests

Data-driven models and methodologies for decision-making under uncertainty.
Methodologies: stochastic optimization, nonsmooth and nonconvex optimization,  risk measure minimization
Applications: treatment learning, machine learning, supply chain management, revenue management, risk management.    

   

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




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Department of Industrial Engineering, Tsinghua University
Phone: 010-62772989
Fax:010-62794399
E-mail:ieoffice@tsinghua.edu.cn
Address:Shunde Building, Tsinghua University, Beijing 100084


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