I’m a Ph.D student at Cornell University, broadly interested in causal inference, experimental design, and applications in high-risk domains such as healthcare and policy. I am coadvised by Professors Raaz Dwivedi and Nathan Kallus.

Previously, I was a research assistant for the Tobin Economics Research Program, Lawrence Berkeley National Laboratory, and Yale’s Department of Statistics and Data Science. I’ve also held interdisciplinary opportunities through the Information Society Project, and gained industry experience as an intern on the investment team at Bridgewater Associates and on the product algorithms team at Meta.

See my publications, code, and ongoing research projects below!

LinkedIn

Email

Awards

• National Defense Science and Engineering Graduate Fellowship (2023)
• Finalist, 2023 INFORMS Data Mining and Decision Analysis Workshop Best Paper Competition – Theoretical Track

Works in Progress

• SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement
  • arXiv link

Publications

• Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits (w. Dominik Meier, Kyra Gan, Nathan Kallus)
  • AISTATS, 2025
• CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies (w. Ana-Roxana Pop, Kyra Gan, Sam Corbett-Davies, Israel Nir, Ariel Evnine, Nathan Kallus)
  • KDD, 2025
• Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams (w. Kyra Gan, Nathan Kallus)
  • ICML, 2024 (Talk available through International Seminar on Selective Inference)
• Kernel Debiased Plug-in Estimation (w. Kyra Gan, Ivana Malenica, Yaroslav Mukhin)
  • ICML, 2024 (Finalist at INFORMS DMDA Workshop, Theoretical Track)
• Effective Missing Value Imputation Methods for Building Monitoring Data (w. T. Dayrit, Y. Gao, Z. Wang, A. Sim, K. Wu)
  • 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 2866-2875

Software

• distillML: Interpretable Machine Learning Methods and Surrogate Model Methods (w. T. Saarinen, S. Walter, J. Sekhon)
  • Github Repository
  • Website