I am a fourth year Biostatistics & Bioinformatics PhD candidate at Duke University. I study the use of machine learning to facilitate interpretable causal inference, with a particular focus on addressing the growing complexity of real-world data. I am advised by Cynthia Rudin, David Page, and Alexander Volfovsky.

In Summer 2024, I interned as a Research Scientist at Meta, developing an approach for offline evaluation of ad-ranking models. Prior to starting my PhD, I was a Data Scientist at Optum and received my B.S. in Applied Mathematics from Loyola Marymount University where I was advised by Thomas Laurent.