Chonghuan Wang 「王崇焕」

I am an Assistant Professor of Operations Management at the Naveen Jindal School of Management at the University of Texas at Dallas since August 2025. Prior to joining UT Dallas, I earned my Ph.D. from MIT in 2025, advised by Prof. David Simchi-Levi. I received my bachelor's degree in information engineering from Shanghai Jiao Tong University in 2020 advised by Prof. Haiming Jin and Prof. Xinbing Wang.

My research focuses on improving decision-making in complex and dynamic operational systems by leveraging the power of data, causal inference, experimentation and AI. I am also broadly interested in management science, machine learning, econometrics and their interplay.

Email: Chonghuan.Wang [at] utdallas.edu / chonghuanwang9 [at] gmail.com

Address: JSOM 3.605, 800 W Campbell Rd, Richardson, TX 75080

Feel free to drop me a line if you are interested in working together! Students at all levels, both undergraduate and graduate students, are very welcomed! I am also happy to host visiting PhD students!

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Research
LLM and Generative AI
ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training
Rui Ai, Yu Pan, David Simchi-Levi, Chonghuan Wang
  • Working paper
    [Arxiv]
    • What Matters in Data for DPO?
    Yu Pan, Zhongze Cai, Huaiyang Zhong, Guanting Chen, Chonghuan Wang
  • Accepted by NeurIPS, 2025
    [Arxiv]
    • Understanding the Impact of Sampling Quality in Direct Preference Optimization
    Kyung Rok Kim, Yumo Bai, Chonghuan Wang, Guanting Chen
  • Working paper
    [Arxiv]
    • Experimentation and Experimental Design
    Experimental Design for Matching
    Chonghuan Wang
  • Working paper
    [Arxiv]
    • Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
    Shuangning Li, Chonghuan Wang, Jingyan Wang
  • Working paper
    [Arxiv]
    • Improving the Estimation of Lifetime Effects in A/B Testing via Treatment Locality
    Shuze Chen, David Simchi-Levi, Chonghuan Wang
  • Working paper
    [Arxiv]
    • Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference
    David Simchi-Levi, Chonghuan Wang. Management Science, 2024
  • MSOM Conference TIE SIG 2024
  • POMS-HK Best Student Paper Competition 2024, Honorable Mention
  • Preliminary version accepted by AISTATS, 2023
    [MS] [AISTATS] [SSRN]

    • Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
    David Simchi-Levi, Chonghuan Wang. Management Science, 2025
  • Preliminary version accepted by ICML, 2023
    [MS] [ICML] [SSRN]
    • Non-stationary Experimental Design under Structured Trends
    David Simchi-Levi, Chonghuan Wang, Zeyu Zheng
  • Preliminary version accepted by NeurIPS, 2023
  • Working Paper
    [SSRN] [NeurIPS]
    • Personalized Incentive Alignment: Correcting Utility-Driven Selection Bias in A/B Tests
    Jiachun Li, Yang Meng, David Simchi-Levi, Chonghuan Wang
  • Accepted by AISTATS, 2026
    • On Experimentation With Heterogeneous Subgroups: An Asymptotic Optimal δ-Weighted-PAC Design
    David Simchi-Levi, Chonghuan Wang, Jiamin Xu
  • Working paper
    [SSRN]
    • Data-Driven Revenue Management
    Context-Based Dynamic Pricing with Separable Demand Models
    Jinzhi Bu, David Simchi-Levi, Chonghuan Wang. Management Science, Forthcoming.
  • Preliminary version accepted by NeurIPS, 2022
    [SSRN] [NeurIPS]
    • Contextual Offline Demand Learning and Pricing with Separable Models
    Menglong Li, David Simchi-Levi, Renfei Tan, Chonghuan Wang, Michelle Wu. Management Science, Forthcoming
    [SSRN]
    Transportation Optimization
    Optimizing Cross-Line Dispatching for Minimum Electric Bus Fleet
    Chonghuan Wang, Yiwen Song, Guiyun Fan, Haiming Jin, Lu Su, Fan Zhang, Xinbing Wang.
  • IEEE Transactions on Mobile Computing, 2021 DOI: 10.1109/TMC.2021.3119421
  • Undergraduate thesis
    [TMC]
    • Towards Minimum Fleet for Ridesharing-Aware Mobility-on-Demand Systems
    Chonghuan Wang, Yiwen Song, Yifei Wei, Guiyun Fan, Haiming Jin, Fan Zhang.
  • IEEE Conference on Computer Communications (INFOCOM), 2021DOI: 10.1109/INFOCOM42981.2021.9488862
  • Undergraduate thesis
    [INFOCOM]
    • Teaching
    University of Texas, Dallas, Lecturer
    OPRE.4330 Global Logistics and Inventory Management Spring 2026
    OPRE.6370 Global Logistics and Transportation Spring 2026
    Massachusetts Institute of Technology, Teaching Assistant
    15.764 The Theory of Operations Management (6.5/7.0) Spring 2025
    1.275/IDS.305 Business and Operations Analytics (6.6/7.0) Spring 2023
    Massachusetts Institute of Technology, Lecturer
    Summer School: Research Experience in Data Science (4.8/5.0) Summer 2024
    Invited Talks
    • October, 2025: A/B Testing for Recommendation Algorithms. INFOMRS Annual Meeting 2025, Atlanta, GA.
    • January, 2025: Experimental Design in Operations. Rotman Young Scholar Seminar, Toronto, Canada.
    • October, 2024: Experimental Design in Operations. INFORMS Annual Meeting 2024, Seattle, WA.
    • August, 2024: Data-driven Price Optimization: From Observation Study To Experimental Design. Purdue Operations Conference, West Lafayette, IN.
    • July, 2024: Pricing Experimental Design. RMP Conference 2024, Los Angeles, CA.
    • June, 2024: Offline Pricing with Separable Demands. MSOM Conference 2024, Minneapolis, MN.
    • June, 2024: Non-stationary Experimental Design with Trends. MSOM Conference 2024, Minneapolis, MN.
    • June, 2024: Multi-armed Bandit Experimental Design. MSOM SIG 2024, Minneapolis, MN.
    • May, 2024: Multi-armed Bandit Experimental Design. American Causal Inference Conference 2024, Seattle, WA.
    • May, 2024: Multi-armed Bandit Experimental Design. POMS Annual Meeting 2024, Minneapolis, MN.
    • January, 2024: Adaptive Experimental Design: A fundamental trade-off. POMS-HK 2024, Hong Kong.
    • October, 2023: Adaptive Experimental Design: A fundamental trade-off. INFORMS Annual Meeting 2023, Phoenix, AZ.
    • September, 2023: Multi-armed Bandit Experimental Design. Purdue Operations Conference, West Lafayette, IN.
    • January, 2023: Multi-armed Bandit Experimental Design. POMS-HK 2023, Hong Kong.
    • October, 2022: Context-Based Dynamic Pricing with Separable Demand Models. INFORMS Annual Meeting 2022, Indianapolis, IN.

    Awards and Honors
    • Accenture Fellowship, MIT, 2023
    • Ho-Ching and Han-Ching Fund Award, MIT, 2023
    • Outstanding Graduate of Shanghai, Shanghai Education Ministry, 2020
    • Tang Lixin Scholarship, Shanghai Jiao Tong University, 2019
    • National Scholarship, Chinese Ministry of Education, 2017,2018
    • A-class Academic Excellence Scholarship, Shanghai Jiao Tong University, 2017, 2018

    Professional Services
    • Committee Member: UT Dallas JSOM Operations Management Faculty Recruiting 2025.
    • MIT Educational Counselor 2026-Present
    • Session Chair: INFORMS Annual Meeting 2023-2026, POMS Annual Meeting 2024 2026
    • Reviewer for Management Science, Operations Research, Manufacturing and Service Operations Management, Biometrika, Marketing Science, Production and Operations Management, Naval Research Logistics, INFOMRS Journal on Data Science, Transaction on Machine Learning Research, EC, NeurIPS, ICLR, ICML, KDD, IJCAI, AAAI, AISTATS.
    • Organizer, MIT Data Science Lab Seminar Series, Fall 2023 2024, Spring 2024 2025
    Misc
    • I enjoy playing ultimate frisbee and badminton. Pretend to be athletic and professional.
    • I also like playing cards, especially Texas hold'em and Guandan (a Chinese traditional card game). Pretend to be good at probability as a researcher in OR.

    Updated in April 2026. Thanks Jon Barron for the source code. Thanks my friend Minkai Xu for letting me know this great template.