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.
Honored to be elected as a member of the Research committee of the IASEAI Council for a 2 year term. Excited about CITP's strong representation on the Council.
Congratulations to Bohdan on a successful presentation at ICML 2026.
Excited to share the first Preliminary Report of the United Nations Independent International Scientific Panel on AI.
Congratulations to Jane on her graduation with a Master's in Computer Science.
Honored to be selected to serve on the United Nations Independent Scientific Panel on AI. I look forward to working with the other panelists for the benefit of all nations.
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)
