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Data-driven Decision Making

Data-driven decision-making requires domain experts to manage data, tune models, and personalize decisions, all of which is laborious and can lead to delays in downstream operations. This research introduces new machine learning-based techniques for automated decision-making that can reduce this human labor and improve the quality of operational services.

Relevant publications

  1. Got (optimal) milk? Pooling donations in human milk banks with machine learning and optimization Timothy CY Chan, Rafid Mahmood, Deborah L. O’Connor, Debbie Stone, Sharon Unger, Rachel K Wong, and Ian Y Zhu Manufacturing & Service Operations Management 2025   DOI  

    • First Place for the INFORMS Pierskalla Best Paper Award 2023.
      Finalist for the MSOM Practice-Based Research Competition 2023.
      Runner Up for the POMS College of Healthcare Operations Management (CHOM) Best Healthcare Paper Award 2023.
      Honorable Mention for the CORS Practice Prize Competition 2023.

    • Preliminary version appeared in The Journal of Nutrition.

  2. Inverse optimization: Theory and applications Timothy CY Chan, Rafid Mahmood, and Ian Yihang Zhu Operations Research 2025   arXiv     DOI  
  3. Learning to optimize contextually constrained problems for real-time decision generation Aaron Babier, Timothy CY Chan, Adam Diamant, and Rafid Mahmood Management Science 2023   arXiv  
  4. An ensemble learning framework for model fitting and evaluation in inverse linear optimization Aaron Babier, Timothy CY Chan, Taewoo Lee, Rafid Mahmood, and Daria Terekhov INFORMS Journal on Optimization 2021   arXiv     Code     DOI  

    • Honorable Mention for the 2018 CORS Best Student Paper Competition.

  5. Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation Timothy CY Chan, Adam Diamant, and Rafid Mahmood Operations Research Letters 2020   Code     DOI     Paper