I am a postdoc at UC Berkeley. In spring 2022, I will be 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.
My research interests are broadly in statistical machine learning for data-driven decision making under uncertainty (and ambiguity), 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 firstname.lastname@example.org.
Selected publications (Full List)Author ordering on papers is alphabetical, following Operations Research convention.
- Stateful Offline Contextual Policy Evaluation and Learning 2021
- 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
|Oct 16, 2021||New paper, Stateful Offline Contextual Policy Evaluation and Learning|
|Oct 15, 2021||Talks at INFORMS 2021, Session SD33 (in person). And Berkeley 10/29: Semi-Autonomous Systems Seminar; BLISS Seminar|
|Aug 1, 2021||Moving to Berkeley for postdoc. Ping if you’re around!|
|Jun 24, 2021||Giving a talk at Center for Causal Inference Symposium|