Zhiqi Zhang(张祉淇)

Zhiqi Zhang(张祉淇)

PhD Candidate at Washington University in St. Louis

Hi! This is Zhiqi Zhang.

Hi! This is Zhiqi Zhang. I am a Ph.D. candidate majoring in Supply Chain, Operations, and Technology at Olin Business School, Washington University in St. Louis (WashU).

My research explores how AI-driven causal inference can enhance decision-making in digital platforms. My research interests include Digital Platforms, Artificial Intelligence, Machine Learning, Causal Inference, Field Experiments, and Structural Modeling. I am advised by Professor Dennis Zhang.

Education

  • Washington University in St. Louis, Ph.D. in Supply Chain, Operations, and Technology (2021 – now)
  • Shanghai Jiao Tong University, B.Eng. in Industrial Engineering (2016 - 2020)
  • Yale University, Summer Session (Jul. 2018 - Aug. 2018)

Papers

  1. Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence, with Zikun Ye, Dennis Zhang, Heng Zhang, Renyu Zhang, Management Science(2025).
    • Accepted by ACM Conference on Economics and Computation (EC) 2023.
  2. Personalized Policy Learning through Discrete Experimentation: Theory and Empirical Evidence, with Zhiyu Zeng, Ruohan Zhan, Dennis Zhang. Job Market Paper.
    • Winner of Buchan Prize Paper Competition, Olin Business School, 2025
  3. The Impact of Recommender Systems on Content Consumption and Production: Evidence from Field Experiments and Structural Modeling, with Zhiyu Zeng, Tat Chan, Dennis Zhang, major revision at Management Science.
    • First place of the Service Science Best Cluster Paper, 2025
  4. Bias in Offline Retailing Experiment: Evidence and Solution, with Jiayi Zhang, Ruohan Zhan, Dennis Zhang, work in progress.

Conference Talks

Personalized Policy Learning through Discrete Experimentation: Theory and Empirical Evidence

  • INFORMS Annual Meeting, Atlanta, GA, 2025
  • POMS Annual Meeting, Atlanta, GA, 2025
  • POMS-HK International Conference, Hong Kong, 2025
  • INFORMS Annual Meeting, Seattle, WA, 2024
  • CODE@MIT, Cambridge, MA, 2024
  • MSOM Annual Meeting, Minneapolis, MN, 2024
  • POMS Annual Meeting, Minneapolis, MN, 2024
  • INFORMS Annual Meeting, Phoenix, AZ, 2023

The Impact of Recommender Systems on Content Consumption and Production: Evidence from Field Experiments and Structural Modeling

  • POMS Annual Meeting, Orlando, FL, 2023

Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence

  • Workshop on Empirical Research in Operations Management, The Wharton School, Philadelphia, PA, 2023
  • POMS Annual Meeting, Orlando, FL, 2023
  • INFORMS Annual Meeting, Indianapolis, IN, 2022
  • POMS Annual Meeting, Virtual Conference, 2022

Teaching

  • PhD Core:
    • SCOT 654 Research Topics in Supply Chain, Guest Lecturer; SP2024
    • SCOT 652 Dynamic Programming, TA; FL2024
    • MKT 680 A AI & Machine Learning for Business Applications, TA; FL2022, FL2023, FL2024
  • Master Core:
    • DAT 301 Data Analytics in Python, TA; SP2025
    • SCOT 531 Supply Chain Finance, TA; SP2024
  • Undergraduate Core:
    • SCOT 554 Operations Analytics, TA; SP2023
    • SCOT 5704 Operations Management, TA; FL2022
    • SCOT 500D Project Management, TA; FL2022, SP2023

Academic Services

  • Journal Reviewer: Manufacturing and Service Operations Management
  • Session Chair: 2024,2025 INFORMS Annual Meeting, 2025 POMS-HK International Conference
  • Judge: 2025 INFORMS workshop on Data Science, 2025 INFORMS Behavioral Operations Management Section Best Working Paper Competition

Contacts

Email: z.zhiqi@wustl.edu