Daniel Kuhn Winner of the Frederick W. Lanchester Prize 2020
The 2020 Frederick W. Lanchester Prize was awarded to Peyman Mohajerin Esfahani and Daniel Kuhn for their paper "Data-driven Distributionally Robust Optimization using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations" (Mathematical Programming 2018). The Lanchester prize recognizes the best contribution to operations research and the management sciences published in English in the past five years.
The winning paper addresses a fundamental challenge in optimization under uncertainty: that the distribution of the uncertain problem parameters, which is needed to compute the expected value of the objective function, is unknown. In practice, one has access to a set of training samples from this distribution. In this case, a natural goal is to find a procedure that transforms the training data to a hopefully near-optimal decision and a prediction of its expected cost. The paper constructs a data-driven approach to decisions by solving a distributionally robust optimization problem over a Wasserstein ball.
These contributions are not only foundational but they have also paved the way for a new perspective on popular methods in statistics and machine learning, and as well as applications.
Mohajerin Esfahani, P. & Kuhn, D. Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations. Mathematical Programing 171, 115–166. 2018.