Organizational Collaboration with Assisted Learning
Humans develop knowledge from individual studies and joint discussions with peers, even though each individual observes and thinks differently. Likewise, in many emerging application domains, collaborations among organizations or intelligent agents of heterogeneous nature (e.g., different institutes, commercial companies, and autonomous agents) are often essential to resolving challenging problems that are otherwise impossible to be dealt with by a single organization. However, to avoid leaking useful and possibly proprietary information, an organization typically enforces stringent security measures, significantly limiting such collaboration. This talk will introduce a new research direction named Assisted Learning that aims to enable organizations to assist each other in a decentralized, personalized, and private manner.
Jie Ding is an Assistant Professor in Statistics and a graduate faculty in ECE at the University of Minnesota. 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 post-doctoral fellow at Information Initiative at Duke University. Before that, Jie graduated from Tsinghua University in 2012, enrolled in the Math & Physics program and the Electrical Engineering program. Jie has broad research interests in machine learning, with a recent focus on collaborative learning and privacy.