Angela Zhou

I am on the 2020-2021 academic job market.

Link to my CV.

I am a fifth-year PhD student at Cornell in Operations Research and Information Engineering. I am currently located at Cornell Tech (NYC) where I work with Nathan Kallus. My work was previously supported on a NDSEG fellowship. Previously, I studied Operations Research and Financial Engineering at Princeton University with minors in Statistics and Machine Learning/Computer Science.

I am interested in developing prescriptive analytics with theoretical guarantees for data-driven decision-making. Currently, I am working on leveraging 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. More broadly, I am interested in the interplay of statistics and optimization for decision-making, with applications to e-commerce, healthcare and policy.

Selected publications (Full List)

Author ordering on papers is alphabetical, following Operations Research convention.
  1. Minimax-Optimal Policy Learning under Unobserved Confounding Kallus, Nathan, and Zhou, Angela Management Science (Accepted). Supersedes Neurips 2018 preliminary results. 2020 [Abs] [arXiv] [Code]
  2. Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning Kallus, Nathan, and Zhou, Angela Neurips 2020 [Abs] [arXiv] [Code] [Video]
  3. Assessing algorithmic fairness with unobserved protected class using data combination Kallus, Nathan, Mao, Xiaojie, and Zhou, Angela Management Science (Accepted). A preliminary version appeared at FaCCT 2020. 2020 [Abs] [arXiv] [Code] [Video]