Aleksandra profile picture

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 and machine learning, 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.

News and updates


New paper in FAccT 2024, 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.


Two papers accepted for presentation at FORC 2024.


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.


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.

Read Research Statement

Recent Publications

Auditing for Racial Discrimination in the Delivery of Education Ads
Basileal Imana, Aleksandra Korolova, John Heidemann

Proceedings of ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024).

In Privacy Law Scholars Conference (PLSC 2024).

Stability and Multigroup Fairness in Ranking with Uncertain Predictions
Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan

To appear at 41st International Conference on Machine Learning (ICML 2024).

Non-archival at Symposium on the Foundations of Responsible Computing (FORC 2024).

Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest
Basileal Imana, Aleksandra Korolova, John Heidemann

Non-archival at Symposium on the Foundations of Responsible Computing (FORC 2024).

Spotlight presentation at Privacy Regulation and Protection in Machine Learning Workshop (@ ICLR 2024).

Proceedings of The 26th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW 2023).

Privacy Law Scholars Conference (PLSC 2023).

Poster in Theory and Practice of Differential Privacy (TPDP 2022).

See All Publications




Prior to joining Princeton, I was the WiSE Gabilan Assistant Professor of Computer Science at USC, a Privacy Advisor at Snap and a Research Scientist at Google. 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 Ph.D. thesis in the Computer Science department at Stanford. 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 and was runner-up for best student paper award at the 2021 Web Conference.  I received the NSF CAREER Award in 2020, and a Sloan Research Fellowship in 2024.


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.