I work on integrating techniques and insights from machine learning into the econometric toolbox. My research brings together microeconometric methods, statistical decision theory, and mechanism design to clarify the use of flexible prediction algorithms in causal inference and data-driven decision-making. I am particularly interested in the role of human and machine decisions in replicable and robust inferences from big data.
I hold a PhD in economics from Harvard University. Previously, I obtained a master’s degree in public policy from the Harvard Kennedy School. My background is in mathematics with a focus on probability theory and combinatorics, which I studied at the University of Cambridge (Part III of the Mathematical Tripos) and the Technical University of Munich. I also studied and worked in Hangzhou, China and Ouagadougou, Burkina Faso.