I am an Assistant Professor of Computer Science and Public Affairs at Princeton University. I'm also excited to be part of Princeton's Center for Information Technology Policy.
I study societal impacts of algorithms, machine learning and AI, and develop and deploy algorithms and technologies that enable data-driven innovations while preserving privacy, fairness and robustness. I also design and perform AI audits.
Reach out by email if you would like to collaborate.
Prospective Ph.D. students should apply to the Ph.D. program in the Department of Computer Science or in the School of Public and International Affairs and indicate an interest in working with me in your statement.
Prospective postdocs should apply to CITP's Fellows Program and reach out to me directly.
I joined Princeton's Department of Computer Science and Princeton's School of Public and International Affairs as an Assistant Professor of Computer Science and Public Affairs.
Presented at Simons Institute. See video of presentation.
Recipient of the NSF 2020 CAREER Award.
Selected as a VMware Research Fellow for “Data Privacy: Foundations and Applications” at the Simons Institute (see my recent talk on "Societal Concerns in Targeted Advertising").
Received a Security and Privacy Research Award for research on differential privacy from Google, a Mozilla Research grant for studying ad preference controls, an NSF SaTC Medium for "Understanding the Privacy and Societal Risks of Advanced Advertising Targeting and Tracking", and an NSF SaTC Frontiers for "Protecting Personal Data Flow on the Internet".
Privacy, algorithmic fairness, accountability and transparency are currently at the center of key debates across academia, industry and policy. My research sits at the intersection of these topics and aims to leverage algorithmic thinking in order to provide new solution spaces that allow for a better balance between individual interests, societal goals, and technical innovation.
I develop algorithmic and systems advances that can enable data-driven innovations while preserving individual privacy, defined in the paradigm of differential privacy.
I work to understand how opaque AI systems (including generative AI) may be affecting individuals and society, and to develop algorithmic techniques for mitigating their negative consequences.
43rd International Conference on Machine Learning (ICML 2026)
