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 and fairness. I also design and perform algorithm and AI audits, including for generative AI.
I am accepting Ph.D. students and postdoctoral fellows for Fall 2025.
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 the Center for Information Technology Policy Fellows program and signal an interest in collaborating on research with me.
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.
This Fall I am teaching COS 350: Ethics of Computing.
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.
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.
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 machine learning systems may be affecting individuals and society, and to develop techniques for mitigating their negative consequences.
Proceedings of the 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024).
Proceedings of 7th AAAI Conference on AI, Ethics, and Society (AIES 2024).
I received my Ph.D. in Computer Science from Stanford, where I was a Cisco Systems Stanford Graduate Fellow advised by Prof. Rajeev Motwani (RIP, Rajeev) and Prof. Ashish Goel. My Ph.D. thesis focused on protecting privacy when mining and sharing user data, and has been recognized by 2011-2012 Arthur L. Samuel Thesis Award for the best Computer Science Ph.D. thesis. While at Stanford, I was fortunate to intern at Microsoft Research, Facebook, Yahoo! Research, and PARC. I am a co-winner of the 2011 PET Award for exposing privacy violations of microtargeted advertising and a runner-up for the 2015 PET Award for RAPPOR, the first commercial deployment of differential privacy. My most recent research on discrimination in ad delivery, received an honorable mention and recognition of contribution to diversity and inclusion at CSCW in 2019 and was runner-up for Best Student Paper Award at the 2021 Web Conference. I received the NSF CAREER Award in 2020, a Sloan Research Fellowship in 2024, and the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2025. Prior to joining Princeton, I was a Research Scientist at Google, a WiSE Gabilan Assistant Professor of Computer Science at USC, and a Privacy Advisor at Snap.
I grew up in Latvia and graduated from Riga secondary school #40, spending fun weekends preparing for math olympiads at NMS and learning algorithms at Progmeistars. I am indebted for the many opportunities I have had to my family, amazing teachers at the above institutions, and to the George Soros Foundation. My outstanding high school mathematics teacher, Viktor Glukhov, now teaches and organizes math circles online.
I loved spending my college years at MIT, and especially enjoyed the classes taught by Prof. Patrick Winston. I first tried doing research in Dan Spielman's error-correcting codes class and Joe Gallian's Duluth REU.
In my free time, I enjoy spending time with my family, traveling, skiing and playing tennis.
I proudly support MEET, MIT, EFF, and The Markup.
