Thu, 13-Jan-2022 / 4:00pm / Packard 101
How can researchers use sensitive data for statistical estimation without compromising the privacy of the individuals who contributed their data? In this talk, I will describe my work on the foundations of statistical estimation in a rigorous privacy framework called differential privacy. Using fundamental examples like mean and covariance estimation, I’ll discuss a range of issues like the minimax error rate of private estimation and practical tools for achieving differential privacy.
Jonathan Ullman is an Associate Professor in the Khoury College of Computer Sciences at Northeastern University. His research centers on privacy for machine learning and statistics, and its surprising connections to topics like statistical validity, robustness, cryptography, and fairness. He has been recognized with an NSF CAREER award and the Ruth and Joel Spira Outstanding Teacher Award.
Packard Building
Electrical Engineering Department
350 Jane Stanford Way
Stanford, CA 94305-9515
Thursdays, 4pm, Packard 202
