I lead the Statistics & Privacy team, an interdisciplinary (10+ PhD fields) set of research scientists. We deliver theoretically sound applied research that drives our business forward, with a focus on areas such as cryptography, privacy-preserving machine learning, experimentation platforms, and supervised learning (ranking, retrieval and recommendation systems) to tackle unrepresentative and mislabeled data. My personal research has focused on causal inference and efficient statistical estimators, motivated by applied challenges in areas such as privacy, advertising, and elections.
I joined Facebook in 2012 after my PhD program at Princeton and a year at Stanford. Prior to entering graduate school I had worked in DC, primarily on domestic policy.
Causal inference (experimentation platforms and observational frameworks), privacy-enhancing technologies, validation frameworks, supervised learning with missing or mislabeled data