Please note: This seminar will take place in DC 1304.
Lydia Zakynthinou, FODSI Postdoctoral Research Fellow
Simons Institute for the Theory of Computing, UC Berkeley
Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them vulnerable to data poisoning attacks, prone to leaking sensitive information, or susceptible to overfitting. Establishing fundamental principles for designing algorithms that are both stable—to mitigate these risks—and efficient in their use of resources is essential for enabling trustworthy data-driven systems.
In this talk, I will focus on statistical estimation under differential privacy—a rigorous framework that ensures data-driven system outputs do not reveal sensitive information about individuals in their input. I will present algorithmic techniques that take advantage of beneficial structure in the data to achieve optimal error for several multivariate tasks without requiring any prior information about the data, by building on robustness against data poisoning attacks. Lastly, I will highlight the deeper connection between differential privacy and robustness that underpins these results.
Bio: Lydia Zakynthinou is a FODSI postdoctoral research fellow in the Simons Institute for the Theory of Computing at UC Berkeley, hosted by Michael I. Jordan. She earned her Ph.D. in Computer Science from Northeastern University under the supervision of Jonathan Ullman and Huy Nguyen. Her research lies in trustworthy machine learning and statistics, with a focus on data privacy and generalization, and has been recognized with a Meta PhD fellowship and a Khoury PhD Research Award. She holds a diploma in Electrical and Computer Engineering from NTUA and a MSc in Logic, Algorithms, and Theory of Computation from NKUA in Greece.