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I am an econometrician who works on integrating techniques and insights from machine learning into the econometric toolbox. I combine 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.