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Data-driven Optimization in Operations Research

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. Inverse optimization: Theory and applications Timothy CY Chan, Rafid Mahmood, and Ian Y Zhu Operations Research 2023   arXiv  
  2. 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  
  3. 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.