Hello! I’m an assistant professor at Stanford in the Department of Management Science & Engineering, in the School of Engineering. I also have courtesy appointments in Computer Science, Sociology, and the Law School.

My primary area of research is computational social science, an emerging discipline at the intersection of computer science, statistics, and the social sciences. I’m particularly interested in applying modern computational and statistical techniques to understand and improve public policy. Some topics I’ve recently worked on are: stop-and-frisk, tests for racial bias, fair machine learning, swing voting, election polls, voter fraud, filter bubbles, and online privacy.

I'm the founder and executive director of the Stanford Computational Policy Lab, a team of researchers, data scientists, and journalists that addresses policy problems through technical innovation. In collaboration with the Computational Journalism Lab, we created the Stanford Open Policing Project, a repository of data on over 100 million traffic stops across the United States.

I often write essays about contemporary policy issues from a statistical perspective. These include discussions of algorithms in the courts (in the New York Times and the Washington Post); policing (in Slate and The Huffington Post); election polls (in the New York Times); claims of voter fraud (in Slate, and also an extended interview with This American Life); and affirmative action (in Boston Review).

I studied at the University of Chicago (B.S. in Mathematics) and at Cornell (M.S. in Computer Science; Ph.D. in Applied Mathematics). Before joining the Stanford faculty, I worked at Microsoft Research in New York City.

If you would like to chat, please stop by my office (Huang 251), or send me an .