I am a research fellow at the Simons program on causality. In summer 2022, I will start as an Assistant Professor at USC Marshall Data Sciences and Operations in Operations.
Previously I was a FODSI postdoc at UC Berkeley, hosted by Bin Yu, and Michael I. Jordan. I obtained my PhD from Cornell University in Operations Research and Information Engineering working with Nathan Kallus at Cornell Tech. My work was previously supported on a NDSEG fellowship.
My research interests are broadly in statistical machine learning for data-driven decision making under uncertainty, and the interplay of statistics and optimization. My dissertation work developed causal inference and machine learning as a language for prescriptive analytics, making robust recommendations for action in view of fundamental practical challenges in observational/operational data. My work emphasizes credibility as a form of reliability, developing robust inferential procedures subject to analyst-tunable violations of assumptions. I am particularly interested in the implications of real-world complex environments that realize societal impacts of machine learning, such as e-commerce, healthcare and policy, for designing inferential methods and informing prescriptive insights.
My email is email@example.com.
Selected publications (Full List)Author ordering on papers is alphabetical, following Operations Research convention.
- Stateful Offline Contextual Policy Evaluation and Learning AISTATS 2022
- Minimax-Optimal Policy Learning under Unobserved Confounding Management Science (Forthcoming), supersedes Neurips 2018 version 2020
- Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning Neurips 2020
- Assessing algorithmic fairness with unobserved protected class using data combination Management Science (Forthcoming). A preliminary version appeared at FaCCT 2020 2019
|2021/22||I am a co-program chair for the second ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization.||Oct 16, 2021||New paper, Stateful Offline Contextual Policy Evaluation and Learning|