2023 Winner(s)
- Jose Blanchet, Columbia University
- Yang Kang, Columbia University and The D.E. Shaw Group
- Karthyek Murthy,
Singapore University of Technology and Design
Citation : The award is for publication(s) "Quantifying distributional model risk via optimal transport" appearing in Mathematics of Operations Research 44(2) 565-600 in 2019 (authors Blanchet and Murthy) and "Robust Wasserstein profile inference and applications to machine learning" appearing in Journal of Applied Probability 56(3) 830-857 in 2019 (authors Blanchet, Kang, and Murthy). These papers by Blanchet, Kang, and Murthy develop foundational tools that use optimal transport to effectively quantify distributional model risk in a broad range of applications, including distributionally robust optimization, diffusion approximations, and adversarially robust machine learning. The papers also reveal how optimal transport based distributionally robust optimization can effectively influence model selection, and provide an alternate explanation for how it leads to solutions offering good out-of-sample performance in high-dimensional settings.
Purpose of the Award
This award recognizes outstanding contributions to applied probability.
Application process:
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Past Awardees
Itai Gurvich, Cornell University
Alberto Vera, Cornell University
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