New Directions in Privacy-Preserving Machine Learning
School of Statistics
College of Liberal Arts
University of Minnesota
Thursday, January 21, 2021
Online via zoom
In a number of emerging AI tasks, collaborations among different organizations or agents (e.g., human and robots, mobile units, and smart devices) are often essential to resolving challenging problems that are otherwise impossible to be dealt with by a single agent. However, to avoid leaking useful and possibly proprietary information, agents typically enforce stringent security measures, which significantly limits such kinds of collaboration. This talk will introduce new research directions in privacy-preserving learning beyond state of the art. A particular focus is on a new learning paradigm named Assisted Learning to enable agents to assist each other in a decentralized, personalized, and private manner. The talk will also introduce a new data privacy framework and a vista of future privacy-preserving machine learning.
Jie Ding is an Assistant Professor in Statistics at the University of Minnesota, also a graduate faculty of the ECE Department and the Data Science program. Before joining the University of Minnesota in 2018, he received a Ph.D. in Engineering Sciences in 2017 from Harvard University and worked as a postdoctoral fellow at Information Initiative at Duke University. Jie's recent research interests are in new principles and methodologies in machine learning, with a particular focus on collaborative AI, privacy, and streaming data.