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.
Sid presented our work on Stability and Multigroup Fairness in Ranking with Uncertain Predictions at ICML 2024.
New paper in FAccT 2024 with Basi and John, Auditing for Racial Discrimination in the Delivery of Education Ads, proposes a black-box auditing method that can evaluate racial bias in the delivery of education ads. We apply our method to Meta and find evidence of racial discrimination.
Honored to receive the Sloan Research Fellowship in Computer Science. Deeply grateful to my incredible collaborators and students who made the research possible, and to my family, mentors and colleagues at Princeton and USC for their unwavering support.
I am teaching a new course aimed at policy students: Societal Impacts of Data, Algorithms and AI.
I spoke at Columbia Business School's Digital Future Initiative workshop on Challenges in Operationalizing Responsible AI.
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)
