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, a field that lies 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: policing practices, including statistical tests for discrimination; fair machine learning, including in automated speech recognition; elections, including swing voting, polling errors, and voter fraud; and political polarization.

I started and direct the Stanford Computational Policy Lab. We're a team of researchers, data scientists, and journalists that addresses policy problems through technical innovation. For example, we recently deployed a “blind charging” platform in San Francisco to mitigate racial bias in prosecutorial decisions.

Sometimes I 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); mass incarceration (in the Washington 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 .