Assistant Professor of Computer Science
- Doctoral Degree, Computer Science, Stanford University
- Master's Degree, Computer Science, Stanford University
- Bachelor's Degree, Mathematics with Computer Science, Massachusetts Institute of Technology
Research homepage: https://www.korolova.com
I am a WiSE Gabilan Assistant Professor of Computer Science at USC. My research studies societal consequences of algorithms and AI, and aims to develop and deploy algorithms and technologies that enable data-driven innovations while preserving privacy and fairness.
Previously, I was a Privacy Advisor at Snap and a Research Scientist at Google. I received my Ph.D. in Computer Science from Stanford University, where I was a Cisco Systems Stanford Graduate Fellow advised by Prof. Rajeev Motwani 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. I am also 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. I received the NSF CAREER Award in 2020.
Developed differentially private algorithms for common data mining tasks such as search log release and malware detection in the local model of privacy, leading to the first commercial deployment of differential privacy.
Identified privacy shortcomings in microtargeted advertising, Apple's deployment of differential privacy, and treatment of Bluetooth permissions by mobile OSes, leading to changes in practices and greater transparency.
Demonstrated that Facebook's ad delivery optimization algorithms can direct job and housing ads to audiences skewed by race & gender even when advertisers target large, inclusive audiences. Furthermore, showed that Facebook's ad delivery optimization algorithms limit political advertisers' ability to reach audiences that do not share their political views and can create informational filter bubbles. Finally, developed a new auditing methodology to distinguish between skew introduced by job ad delivery algorithms that may be explainable by differences in qualifications (permissible by law) from skew due to factors such as the ad platform's optimization for its business objectives, demonstrating that Facebook's job ad delivery algorithms are not merely biased but discriminatory.
Showed how automated analyses of users' privacy-related actions can be used to improve product privacy features.
- 2021 Runner-up for the WWW Best Student Paper Award
- 2020 NSF CAREER Award
- 2019 VMware Research Fellow at the Simons Institute for the Theory of Computing
- 2019 CSCW Recognition of Contribution to Diversity and Inclusion
- 2019 Honorable Mention at the 22nd ACM Conference on Computer Supported Cooperative Work (CSCW)
- 2017 Google Security and Privacy Research Award
- 2015 Runner-up for the PET Award for Outstanding Research in Privacy Enhancing Technologies
- 2012 Stanford University Arthur L. Samuel Thesis Award
- 2011 Co-Winner of the PET Award for Outstanding Research in Privacy Enhancing Technologies