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.

A summary of my research can be found here.

Working Papers

  1. Uncertain Search with Knowledge Transfer, with Woonghee T. Huh, Michael J. Kim.
    Accepted at Management Science.

  2. Two-Sided Pricing and Learning with Buyer Choice: A Generalized SGD Method, with Woonghee T. Huh.
    Submitted.

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

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

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

Publications

  1. 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.

  2. 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.

  3. 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.