Research

Research papers

  1. Decision-Focused Bias Correction for Fluid Approximation
    Can Er, Mo Liu. To be submitted. [link]

    • “Fluid approximation is biased with respect to the decision-making”
    • Should one plug in the time-varying arrival rate to replace the original demand arrival distribution when designing the capacity for multi-server system? (Short answer: No)
    • Does a decision-corrected fluid approximation always exist? (Short answer: No)
    • Does a (vectorized) point prediction always exist for multi-product multi-customer newsvendor problem? (Short answer: No)
    • We provide sufficient and necessary conditions for the existence of the decision-corrected arrival rate.

  2. Marginal Value of One Data Point in Assortment Personalization
    Mo Liu, Junyu Cao, Zuo-jun Max Shen. Resubmitted to Management Science.
    [link] [Slides]

    • “How much is the marginal contribution to the revenue increase when adding one new customer with some specific feature into the training set?”
    • By evaluating this marginal contribution of each customer, we are able to identify informative customers and reduce the size of the training set by about 80% while maintaining the same level of revenue.

  3. Active Learning For Contextual Linear Optimization: A Margin-Based Approach
    Mo Liu, Paul Grigas, Heyuan Liu, Zuo-jun Max Shen. Major Revision at Management Science.
    [link] [Slides]

    • “How to identify informative samples for decision-making?”
    • Best Student Paper Nominee at INFORMS Workshop on Data Science 2023
    • Second Place Poster Prize at YinzOR 2023

  4. Learning from Click Transition Data: Effectiveness of Greedy Pricing Policy under Dynamic Product Availability
    Mo Liu, Junyu Cao, Zuo-jun Max Shen. Major Revision at Management Science.
    [link]

    • Finalist at 2023 INFORMS Service Science Student Competition
    • Fan Favorite Flash Talk at YinzOR 2023

  5. Decision-Focused Sequential Experiment Design: A Directional Uncertainty-Guided Approach
    Beichen Wan, Mo Liu, Paul Grigas, Zuo-jun Max Shen. Working paper.
    Preliminary version at [2025 NeurIPS Workshop] [Poster]

  6. Inventory Management with LLM: Automated Decision-Making for Order Timing and Quantity
    Mo Liu, Yumo Bai, Meng Qi, Zuo-jun Max Shen. Major Revision at Service Science.
    [link]

  7. A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
    Xi Chen, Mo Liu, Yining Wang, Yuan Zhou. Accepted by Production and Operations Management.
    [link]

Patent

Joint machine learning and dynamic optimization with time series data to forecast optimal decision making and outcomes over multiple periods
Zachary Xue, Mo Liu, Markus Ettl, Shivaram Subramanian. [link]

Selected Talks