Publications and Preprints

My research studies the foundations of machine learning for stochastic optimization. Specifically, my research focuses on understanding the performance of data-driven optimization algorithms in the real-world "data -- model -- evaluation" pipeline. This includes developing:
  • fundamental tools in evaluation and selection of data-driven optimization models [J3, C4, W3];

  • efficient data integration models with performance guarantees [J5, J2, J1, C1];

  • empirical data benchmark and data-driven tools in understanding distribution shifts [J5, J4, W2].

Practically, I have been working with several companies and institutions, including the Fire Department of the City of New York (FDNY, [W4]) and Merck Sharp & Dohme (MSD, [J5]) to help design and implement data-driven optimization models in real operations. In Summer 2023, I spent a great time working on uncertainty attribution of inventory production control simulation systems as a research scientist intern at Supply Chain Optimization Technologies Team in Amazon.

For papers listed as follows, * means that authors are listed in alphabetical order. + means that authors are equally contributed (order listed alphabetically).

Journal Articles Published or Under Revisions

Refereed Conference Publications

Working Papers