We would like to announce and congratulate the finalists for the 2021 Applied Probability Society student paper competition.
In alphabetical order:
Mark Gluzman (Cornell U.) for the paper ``Queueing Network Controls via Deep Reinforcement Learning'' (faculty co-author: Jim Dai)
Omar Mouchtaki (Columbia U.) for the paper ``How Big Should Your Data Really Be? Data-Driven Newsvendor and the Transient of Learning'' (faculty co-author: Omar Besbes)
Yunbei Xu (Columbia U.) for the paper ``Towards Optimal Problem Dependent Generalization Error Bounds In Statistical Learning Theory'' (faculty co-author: Assaf Zeevi)
Xiangyu Zhang (Cornell U.) for the paper ``Restless Bandits with Many Arms: Beating the Central Limit Theorem'' (faculty co-author: Peter Frazier)
The four finalists will present their work in a dedicated session during the INFORMS annual meeting. Precise timing of the session is forthcoming.
Itai Gurvich, Chair of the competition committee
(see here for the competition rules and the list of committee members)