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I am an econometrician in the OIT group at Stanford GSB. My current research focusses broadly on three related themes:

• High-dimensional and robust causal inference, including integrating machine learning into the econometric toolbox, causal inference with highly over-parametrized models, and robust estimation in panel data;
• Data-driven decision-making with conflicts of interest, including the design of pre-analysis plans and AI regulation;
• Human–AI interaction, including the fairness and optimal design of algorithmic recommendations.

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.