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, algorithmic fairness, swing voting, election polls, voter fraud, filter bubbles, and online privacy.
I'm the executive director of the Stanford Computational Policy Lab, a team of data scientists, engineers, researchers, 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 general-audience pieces 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); police stops (in Slate and The Huffington Post); election polls (in the New York Times); and claims of voter fraud (in Slate, and also an extended interview with This American Life).
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.