Sean Sinclair



Something went wrong Sean Sinclair is a final-year Ph.D. student in the School of Operations Research and Information Engineering at Cornell University coadvised by Christina Lee Yu and Siddhartha Banerjee. His research focuses on developing algorithms for data-driven sequential decision making in societal applications. He bridges algorithmic techniques in reinforcement learning to an operations management perspective with an emphasis on models, data uncertainty, and objectives. Recent contributions include instance-specific optimal regret guarantees for nonparametric reinforcement learning, Pareto-optimal fair resource allocation, and data-efficient algorithms for cloud compute allocations. Complementing this, he also designs open-source code instrumentation and methodology to empirically analyze the multi-criteria performance of algorithms on these problems.

His paper, "Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve" is a finalist for the 2022 INFORMS Diversity, Equity, and Inclusion Student Paper Competition. Sean was selected for the 2022 Future Leaders Summit at the Michigan Institute for Data Science. In 2020 and 2022 he was a visitor at the Simons Institute for the programs on the Theory of Reinforcement Learning and Data-Driven Decision Processes. During the Summer of 2021 he was a research intern at Microsoft Research under Adith Swaminathan.

He graduated with a B.S. in Honours Mathematics and Computer Science from McGill University where he worked on a project with Tony Humphries. Before returning to graduate school he spent two and a half years teaching mathematics, science, and English in a small community in rural Ghana with the Peace Corps, and after worked at National Life as a financial analyst.


I am on the academic job market this year.

Office: 294 Rhodes Hall
Email: srs429 at cornell.edu
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News

October 2022: My papers "Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve" and "Adaptive Discretization in Online Reinforcement Learning" were accepted to Operations Research!
September 2022: My paper, "Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve" is a finalist for the 2022 INFORMS DEI Best Student Paper Award!
August 2022: On August 23 I will be speaking at the Data Driven Decision Processes Bootcamp on Online Reinforcement Learning and Regret.
June 2022: In August 15-18 I will be speaking at the Summer Bootcamp at Kelogg on RL in Operations.
June 2022: This Fall I'll be a visiting graduate student at Simons Institute for the Data-Driven Decision Processes program.
May 2022: In June I'll be speaking at the Workshop on Algorithms for Learning and Economics and participating in a panel on Socially Responsible ML.


Selected Publications

Hindsight Learning in MDPs with Exogenous Inputs [arXiv] Working Paper
Sean R. Sinclair, Felipe Frujeri, Ching-An Cheng, and Adith Swaminathan.

Adaptive Discretization in Online Reinforcement Learning [arXiv] Accepted in Operations Research
Sean R. Sinclair, Siddhartha Banerjee, and Christina Lee Yu.

Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve [arXiv] [video] Accepted in Operations Research
Sean R. Sinclair, Gauri Jain, Siddhartha Banerjee, and Christina Lee Yu.
Finalist for the 2022 INFORMS DEI Best Student Paper Award