Research

My research focuses on data-driven decision making with applications such as dynamic pricing and inventory management. I am particularly interested in

  • investigating the cost of learning by developing and analyzing online algorithms;
  • understanding interesting exploration-exploitation trade-offs by modeling and solving sequential decision problems using Bayesian learning and dynamic programming.

Working Papers

  1. Two-Sided Pricing and Learning with Buyer Choice: A Generalized SGD Method, with Woonghee T. Huh.
    Major Revision at Operations Research.

  2. Competition in Pricing Algorithms: Stability, Exploration, and Supracompetitive Outcomes, with A. Ömer Sarıtaç. Submitted.

  3. A Search Game and Its Applications, with Christopher S. Tang. Submitted.

  4. The Power of Many: A Novel Aggregator Business Model for Vehicle-to-Grid Services, with Yangfang Zhou, Owen Q. Wu, John Pang. Manuscript in Preparation.

  5. From Full to Partial Personalization: Performance Bounds for Fixed-Price Policies in Dynamic Pricing with Limited Inventory, with Zhuyu Liu, Woonghee T. Huh. Manuscript in Preparation.

  6. Risk-Averse Dynamic Pricing and Demand Learning, with Woonghee T. Huh, Michael J. Kim.
    To be Resubmitted.

  7. New Product Dynamic Pricing: The Value of Transfer Learning, with Woonghee T. Huh.
    Manuscript in Preparation.

Publications

  1. Woonghee T. Huh, Michael J. Kim, Meichun Lin (2026). Uncertain Search with Knowledge Transfer. Management Science, 72(2): 874-892.

  2. Meichun Lin, Woonghee T. Huh, Harish Krishnan, Joline Uichanco (2022). Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation. Operations Research, 70(4): 1996-2012.

  3. Woonghee T. Huh, Michael J. Kim, Meichun Lin (2022). Bayesian Dithering for Learning: Asymptotically Optimal Policies in Dynamic Pricing. Production and Operations Management, 31(9): 3576-3593.

  4. Meichun Lin, Woonghee T. Huh, Guohua Wan (2021). Multi-Period Lot-Sizing with Supplier Selection: Structural Results, Complexity and Algorithm. Operations Research Letters, 49(4): 602-609.

    • Finalist, INFORMS Undergraduate Operations Research Prize, 2018

Conference Talks

  • Uncertain Search with Knowledge Transfer.
    • MSOM Conference, Munich, Germany, June 2022.
  • Risk-Averse Dynamic Pricing and Demand Learning.
    • INFORMS Annual Meeting, Virtual, Oct. 2021.
  • Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation.
    • POMS 31st Annual Conference, Virtual, Apr. 2021.
  • Bayesian Dithering for Learning: Asymptotically Optimal Policies in Dynamic Pricing.
    • MSOM Conference, Virtual, June 2021.
    • INFORMS Annual Meeting, Virtual, Nov. 2020.
  • Multi-Period Lot-Sizing with Supplier Selection: Structural Results, Complexity and Algorithm.
    • INFORMS Annual Meeting, Phoenix, Nov. 2018.