Abstract
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. This includes new concepts and methods inspired from cross-disciplinary perspectives such as statistics, optimization, and information theory.
Biography
Jie Ding (http://jding.org/) is an Assistant Professor at the School of Statistics, University of Minnesota, with graduate faculty appointments at the Department of Electrical and Computer Engineering and the Data Science Program. Jie is also a visiting scholar at Amazon Science in the area of distributed and privacy-preserving Alexa AI. Jie's research focus is on the foundations of machine learning, statistics, and signal processing. Jie's research has been generously supported by multiple government agencies and industry awards. 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 Duke University. Before that, Jie graduated from Tsinghua University in 2012, enrolled in Math & Physics program and the Electrical Engineering program.
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