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.
The Lawyers’ Committee for Civil Rights Under Law filed a lawsuit against Meta for its discriminatory ad delivery practices in education. The lawsuit cites research from our FAccT 2024 paper in its complaint.
Honored to receive the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Biden.
Grateful to my collaborators, mentors, and my family for their support in developing the research vision, and to the NSF SaTC team for their nomination and belief in my work.
Our work accepted to AIES 2024 shows the lack of effectiveness of user-facing controls in AI-mediated ad targeting systems. Congratulations to Jane on her first publication!
Sid presented our work on Stability and Multigroup Fairness in Ranking with Uncertain Predictions at ICML 2024.
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.
Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026).
Selected for oral presentation at the Special Track on AI for Social Impact.
Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025).
Proceedings of ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025).
In Privacy Law Scholars Conference (PLSC 2025).
Best Paper Award (FAccT 2025).
