2023 Winner(s)
- Yanlin Qu, Stanford University
Winning material:
Computable Bounds on Convergence of Markov Chains in Wasserstein Distance
Purpose of the Award
The Applied Probability Society seeks to identify and honor outstanding papers in the field of applied probability that are written primarily by a student. We define applied probability broadly, as any paper related to the modeling, analysis, and control of stochastic systems. The paper’s contribution may lie in the formulation of new mathematical models, in the development of new mathematical or computational methods, in the innovative application of existing methods, or in the opening of new application domains.
Application process:
Click here for more information.
Past Awardees
2023
Winner(s)
Yanlin Qu,
Stanford University
2023
Finalist
David Cheikhi,
Columbia University
Anna Winnicki,
University of Illinois Urbana-Champaign
Yunbei Xu,
Columbia University