I am an associate professor of statistical science and computer science at Duke University. I work on causal inference in complex systems, with text, networks, and AI systems as recurring examples of settings where statistical methods need to respect the structure of the data-generating process. I am the Co-Director of two labs at Duke: (1) The Polarization Lab at Duke brings together scholars from the social sciences, statistics, and computer science to develop new technology to bridge America's partisan divide. (2) The Almost Matching Exactly Lab at Duke brings together statisticians, computer scientists, economists, and political scientists to develop tools for interpretable causal inference.

I am the past president of the New Researchers Group at the Institute of Mathematical Statistics which coordinates the Meeting of New Researchers in Statistics and Probability (supported by the National Science Foundation).

This year I am serving on the program committees for STAI-X '26: Statistics and Trustworthy AI for Cross-Domain Acceleration and the 2026 IISA Conference. If your work fits either venue, please consider submitting, applying, or encouraging students and collaborators to do so.

Our work is graciously supported by NIH, NSF, ARI, Facebook, Google, the Templeton Foundation and Duke University.