I am a Senior Research Scientist at Upstart in Seattle, where I focus on causal inference, uplift modeling, and online and offline evaluation for advertising channels. I hold a Ph.D. in Biostatistics & Bioinformatics from Duke University, where my research centered on leveraging machine learning for interpretable causal inference under the co-advisorship of Cynthia Rudin, David Page, and Alexander Volfovsky.

Prior to Upstart, I interned as a Research Scientist at Meta, where I developed methods for offline counterfactual evaluation of large-scale ad-ranking models, and worked as a Data Scientist at Optum. I earned my B.S. in Applied Mathematics from Loyola Marymount University, advised by Thomas Laurent.