I look at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary social issues. Some topics I’ve recently worked on are: policing practices, including statistical tests for discrimination; fair machine learning, including in automated speech recognition; and democratic governance, including swing voting, polling errors, voter fraud, and political polarization.
Before joining Harvard, I was on the faculty at Stanford University, with appointments in management science & engineering, computer science, sociology, and the law school. At Stanford, I was the founding director of the Computational Policy Lab. The lab is comprised of researchers, data scientists, and journalists who work to address policy problems through technical innovation. For example, we deployed a “blind charging” platform in San Francisco to mitigate racial bias in prosecutorial decisions. We also collected, released, and analyzed data on over 100 million traffic stops as part of our Open Policing Project.
I often write essays and engage in public discussions on policy issues from a statistical perspective. These include examinations of algorithms in the courts (in the New York Times and the Boston Globe); algorithmic fairness (in the Washington Post and on the Moral Science Podcast); 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 on This American Life); and affirmative action (in Boston Review).
I hold a bachelor’s degree in mathematics from the University of Chicago, as well as a master’s degree in computer science and a doctorate in applied mathematics from Cornell University. After finishing graduate school, I completed postdoctoral fellowships in the math departments at Stanford and the University of Southern California, and then worked as a research scientist at Yahoo and Microsoft before returning to academia.