I’m a Ph.D student at Cornell University, broadly interested in causal inference, machine learning interpretability, and applications in high-risk domains such as healthcare and policy.

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

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