Chonghuan Wang 「王崇焕」

I am joining the Naveen Jindal School of Management at the University of Texas at Dallas as an Assistant Professor of Operations Management in August 2025. Feel free to drop me a line if you are interested in working together!

I am now a final-year PhD candidate in Data Science Lab at MIT, advised by Prof. David Simchi-Levi. I am affiliated with the Laboratory for Information and Decision Systems, Center for Computational Science and Engineering and Department of Civil and Environmental Engineering, MIT. Prior to joining MIT, 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 designing efficient, reliable and responsible field experiments in complex and dynamic operational and service systems, such as clinical trials, online platforms, healthcare, by leveraging the power of operational data and machine learning. My research goal is to enhance social good with the power of machine learning, causal inference and experimental design. I am also broadly interested in operations research, machine learning, econometrics and their interplay.

Email: chwang9 [at] mit.edu / chonghuanwang9 [at] gmail.com

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Research (* Alphabetical Order)
Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference
David Simchi-Levi*, Chonghuan Wang*
  • Accepted by Management Science, 2024
  • MSOM TIE SIG 2024
  • POMS-HK Best Student Paper Competition 2024, Honorable Mention
  • Preliminary version accepted by AISTATS, 2023

    • Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
    David Simchi-Levi*, Chonghuan Wang*
  • Accepted by Management Science, 2024
  • Preliminary version accepted by ICML, 2023
    • Non-stationary Experimental Design under Structured Trends
    David Simchi-Levi*, Chonghuan Wang*, Zeyu Zheng*
  • Preliminary version accepted by NeurIPS, 2023
  • Full version Major Revision, Management Science
    • Context-Based Dynamic Pricing with Separable Demand Models
    Jinzhi Bu*, David Simchi-Levi*, Chonghuan Wang*
  • Preliminary version accepted by NeurIPS, 2022
  • Full version Minor Revision, Management Science
    • Contextual Offline Demand Learning and Pricing with Separable Models
    Menglong Li*, David Simchi-Levi*, Renfei Tan*, Chonghuan Wang*, Michelle Wu*
  • Full version Major Revision, Management Science
    • Experimenting on Markov Decision Process with Local Treatments
    Shuze Chen*, David Simchi-Levi*, Chonghuan Wang*
  • Working paper, Preliminary Draft
    • On Experimentation With Heterogeneous Subgroups: An Asymptotic Optimal δ-Weighted-PAC Design
    David Simchi-Levi*, Chonghuan Wang*, Jiamin Xu*
  • Working paper, Preliminary Draft
    • Optimizing Cross-Line Dispatching for Minimum Electric Bus Fleet
    Chonghuan Wang, Yiwen Song, Guiyun Fan, Haiming Jin, Lu Su, Fan Zhang, Xinbing Wang
  • Undergraduate thesis
  • IEEE Transactions on Mobile Computing, 2021 DOI: 10.1109/TMC.2021.3119421
    • Towards Minimum Fleet for Ridesharing-Aware Mobility-on-Demand Systems
    Chonghuan Wang, Yiwen Song, Yifei Wei, Guiyun Fan, Haiming Jin, Fan Zhang
  • Undergraduate thesis
  • IEEE Conference on Computer Communications (INFOCOM), 2021DOI: 10.1109/INFOCOM42981.2021.9488862

    • Teaching
    1.275/IDS.305 Business and Operations Analytics (Spring 2023)
    Teaching Assistant
    Evaluation: 6.6/7.0
    Enrollment: 42 (primarily MIT Sloan MBA students, Supply Chain Management (SCM) Master’s students, and Leaders for Global Operations (LGO) Master’s students)
    Delivered one lecture on predictive analytics and organized weekly TA sessions
    Summer School: Research Experience in Data Science (Summer 2024)
    Lecturer
    Evaluation: 4.8/5.0
    Six-week summer school at MIT for 10 undergraduates from the City University of Hong Kong
    Delivered lectures on basic and advanced data science, designed alignments, and organized the daily activities
    Invited Talks
    • 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
    • Session Chair: INFORMS Annual Meeting 2023 2024, POMS Annual Meeting 2024
    • Reviewer for Naval Research Logistics, NeurIPS 2023 2024, ICLR 2024 2025, ICML 2024, IJCAI 2024, KDD 2024, AAAI 2025
    • Organizer, MIT Data Science Lab Seminar Series, Fall 2023, Spring 2024, Fall 2024
    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 September 2024. Thanks Jon Barron for the source code. Thanks my friend Minkai Xu for letting me know this great template.