I work on quantitative methodology at the intersection of causal inference and machine learning, with an emphasis on the design and analysis of experiments. My research focuses on causal machine learning, adaptive and algorithmic experimental design, and the evaluation of complex digital systems like social media platforms and AI.

Before returning to academia, I spent four years as a research scientist at Facebook Core Data Science in New York, developing methods for adaptive experimentation in large-scale online systems. I was part of the US 2020 Facebook and Instagram Election Study, a landmark collaboration between independent academics and Meta that measured the effects of social media on political attitudes, beliefs and behavior during the 2020 US elections.

My research has appeared in machine learning venues including ICML, KDD and AISTATS, as well as general-interest journals such as Science, Nature and PNAS. I received my PhD in political methodology from New York University.