Angela Zhou


401S Bridge Hall

I am an Assistant Professor at USC Marshall Data Sciences and Operations, in the Operations group.

Previously I was a research fellow at the Simons program on causality, 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 data-driven decision making under uncertainty, including operations, statistical machine learning, and causal inference, and the interplay of statistics and optimization.

I’m happy to chat about possible collaborations. If you are a student at USC, please feel free to email me (and check with your advisor). If you are interested in becoming a PhD student at USC, please apply to the Data Sciences and Operations PhD program if you’d like to work with me.

Email: zhoua at

[Scholar], [CV], [tw]


selected publications

  1. Empirical Gateaux Derivatives for Causal Inference
    Michael Jordan, Yixin Wang, and Angela Zhou
    conference version at Neurips; journal version in preparation 2022
  2. Minimax-Optimal Policy Learning under Unobserved Confounding
    Nathan Kallus, and Angela Zhou
    Management Science (Forthcoming), supersedes Neurips 2018 version 2020
  3. Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
    Nathan Kallus, and Angela Zhou
    Neurips 2020
  4. Assessing algorithmic fairness with unobserved protected class using data combination
    Nathan Kallus, Xiaojie Mao, and Angela Zhou
    Management Science (Forthcoming). A preliminary version appeared at FaCCT 2020 2019


Sep 14, 2022 Two papers accepted at Neurips 2022: Off-Policy Evaluation with Policy-Dependent Response and Empirical Gateaux Derivatives for Causal Inference.
May 22, 2022 Started at USC Marshall.