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 Kyra Gan 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 at Bridgewater Associates.

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

Resume

LinkedIn

Email

Works in Progress

• Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits (w. Dominik Meier, Kyra Gan, Nathan Kallus)
  • arXiv link

Conference Publications

• Kernel Debiased Plug-in Estimation (w. Kyra Gan, Ivana Malenica, Yaroslav Mukhin)
  • ICML, 2024 (Finalist at INFORMS DMDA Workshop, Theoretical Track)
• 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)
• 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, arXiv link

Software

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

Workshop Proceedings

• 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