Feng Zhu

Research

My research interests lie broadly in sequential decision-making under uncertainty, with applications to supply chain management, online experimentation and revenue management. I am particularly interested in managing hidden risk in operations, including tail risk control for multi-armed bandits [5, 6, compact ver.] and risk detection and mitigation for supply chain disruption [8]. I am also interested in understanding policy robustness under (structured) non-stationary environments.

Practically, I have been working with several large companies (Accenture, DENSO, Ford) to help design and implement supply chain risk detection models and mitigation strategies. Part of my work appeared in Harvard Business Review: Fixing the U.S. Semiconductor Supply Chain. In Summer 2023, I spent a great time working on inventory simulation and optimization as a supply chain analytics intern at Ford Motor Company.

Publications and Preprints (reverse chronological order; *represents α-β author order)

[9] Dynamic Service Fee Pricing under Strategic Behavior: Actions as Instruments and Phase Transition. Rui Ai*, David Simchi-Levi*, Feng Zhu*. Under preparation.

  • Preliminary version appeared in NeurIPS 2024.
  • INFORMS 2024.

[8] Risk Detection, Response Coordination, and System Recovery under Uncertain Time-To-Recover. Pengfeng Shu*, David Simchi-Levi*, Chung-Piaw Teo*, Feng Zhu*. (One-page abstract. Full draft available upon request.)

  • Accepted by MSOM 2025 Supply Chain Management SIG.
  • MSOM 2025, INFORMS 2024.

[7] Bayesian Online Multiple Testing: A Resource Allocation Approach. ​Ruicheng Ao*, Hongyu Chen*, David Simchi-Levi*, Feng Zhu*.

  • Finalist, RMP 2024 Jeff McGill Best Student Paper Award. (Entrant: Feng Zhu)
  • INFORMS 2024, NUS Next-Gen Scholars Symposium, Purdue Operations Conference 2024, MSOM 2024.

[6] Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk. David Simchi-Levi*, Zeyu Zheng*, Feng Zhu*.

  • Preliminary version appeared in NeurIPS 2023 Spotlight (top 3%).
  • Finalist, POMS-HK 2024 Best Student Paper Competition.
  • INFORMS 2024, MSOM 2024, POMS 2024, POMS-HK 2024, INFORMS 2023, Purdue Operations Conference 2023.

[5] A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits. David Simchi-Levi*, Zeyu Zheng*, Feng Zhu*.

[4] On Greedy-like Policies in Online Matching with Reusable Network Resources and Decaying Rewards. David Simchi-Levi*, Zeyu Zheng*, Feng Zhu*.

  • Accepted by Management Science.
  • INFORMS 2022, Marketplace Innovation Workshop 2022.

[3] Offline Planning and Online Learning under Recovering Rewards. David Simchi-Levi*, Zeyu Zheng*, Feng Zhu*.

[2] Dynamic Pricing in a Non-stationary Growing Environment. Feng Zhu, Zeyu Zheng. 

[1] Assign-to-Seat: Dynamic Capacity Control for Selling High-Speed Train Tickets. Feng Zhu, Shaoxuan Liu, Rowan Wang and Zizhuo Wang.

  • Published in Manufacturing & Service Operations Management.