- Our Work
Several faculty members have expressed interest in potentially mentoring Emerging Scholars. Admitted scholars will be matched to a mentor based on alignment of interests.
If you have any questions, please contact either the faculty director Arvind Narayanan () or the program director Tithi Chattopadhyay (). Due to the volume of inquires, please direct questions to the above email addresses rather than contacting faculty mentors directly.
Mihir Kshirsagar joins CITP to run our first-of-its-kind interdisciplinary technology policy clinic that gives students and scholars an opportunity to engage directly in the policy process. Most recently, he served in the New York Attorney General’s Bureau of Internet & Technology as the lead trial counsel in cutting edge matters concerning consumer protection law and technology and obtained one of the largest consumer payouts in the State’s history. Previously, he worked for Cravath, Swaine & Moore LLP and Cahill Gordon Reindel LLP in New York City on a variety of antitrust, securities and commercial disputes involving emerging and traditional industries. Before law school he was a policy analyst at the Electronic Privacy Information Center in Washington, D.C., educating policy makers about the civil liberties implications of new surveillance technologies. Mihir attended Deep Springs College and received an A.B. from Harvard College in 2000 and a law degree from the University of Pennsylvania in 2006.
Jonathan Mayer is an assistant professor of computer science and public affairs at Princeton University. Before joining the Princeton faculty, Jonathan served as the technology law and policy advisor to United States Senator Kamala Harris and as the chief technologist of the Federal Communications Commission Enforcement Bureau. Jonathan’s research centers on the intersection of technology and law, with emphasis on national security, criminal procedure, and consumer privacy. Jonathan is both a computer scientist and a lawyer, and he holds a Ph.D. in computer science from Stanford University and a J.D. from Stanford Law School.
Arvind Narayanan is an associate professor of computer science at Princeton. He leads the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. Narayanan co-created a Massive Open Online Course and textbook on Bitcoin and cryptocurrency technologies which has been used in over 150 courses worldwide. His recent work has shown how machine learning reflects cultural stereotypes, and his doctoral research showed the fundamental limits of de-identification. Narayanan is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), twice recipient of the Privacy Enhancing Technologies Award, and thrice recipient of the Privacy Papers for Policy Makers Award.
Olga Russakovsky is an assistant professor in the Department of Computer Science at Princeton University. Her research is in computer vision, closely integrated with machine learning and human-computer interaction. She completed her Ph.D. at Stanford University and her postdoctoral fellowship at Carnegie Mellon University. She has served as a senior program committee member for WACV’16, CVPR’18 and CVPR’19, has organized 8 workshops and tutorials on large-scale recognition, and has given more than 50 invited talks at universities, companies, workshops and conferences. She was awarded the PAMI Everingham Prize in 2016 as one of the leaders of the ImageNet Large Scale Visual Recognition Challenge, the MIT Technology Review’s 35-under-35 Innovator award in 2017 and was named one of Foreign Policy Magazine’s 100 Leading Global Thinkers in 2015. In addition to her research, she co-founded and continues to serve on the Board of Directors of the AI4ALL foundation dedicated to increasing diversity and inclusion in AI. She co-founded the Stanford AI4ALL camp teaching AI for social good to high school girls (formerly “SAILORS”) and the Princeton AI4ALL camp teaching AI technology and policy to underrepresented minority high school students.
Matt Salganik is a professor of sociology who has pioneered uses of data and digital technologies in social research. He was appointed interim director of Princeton University’s Center for Information Technology Policy on July 1, 2019, and then director of CITP for a two-year term beginning July 1, 2020.
Matt is affiliated with several other Princeton’s interdisciplinary research centers, including: the Office for Population Research, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. He is the author of Bit by Bit: Social Research in the Digital Age.
Salganik’s research has been published in journals such as Science, PNAS, Sociological Methodology, and Journal of the American Statistical Association. His papers have won the Outstanding Article Award from the Mathematical Sociology Section of the American Sociological Association and the Outstanding Statistical Application Award from the American Statistical Association. Popular accounts of his work have appeared in the New York Times, Wall Street Journal, Economist, and New Yorker. Salganik is currently on the Board of Directors of Mathematica Policy Research. Salganik’s research has been funded by the National Science Foundation, National Institutes of Health, Joint United Nations Program for HIV/AIDS (UNAIDS), Russell Sage Foundation, Alfred P. Sloan Foundation, Facebook, and Google. During sabbaticals from Princeton, he has been a Visiting Professor at Cornell Tech and a Senior Research at Microsoft Research. During the 2018-19 academic year, he was a professor in residence at the New York Times.
Brandon Stewart is an assistant professor in the Department of Sociology and is also affiliated with the Department of Politics and the Office of Population Research. He develops new quantitative statistical methods for applications across the social sciences. Methodologically his focus is in tools which facilitate automated text analysis and model complex heterogeneity in regression. Many recent applications of these methods have centered on using large corpora of text to better understand propaganda in contemporary China. His research has been published in journals such as American Journal of Political Science, Political Analysis and the Proceedings of the Association of Computational Linguistics. His work has won the Edward R Chase Dissertation Prize, the Gosnell Prize for Excellence in Political Methodology, and the Political Analysis Editor’s Choice Award.