学术报告:Broad Learning via Fusion of Heterogeneous Information

题目:Broad Learning via Fusion of Heterogeneous Information

主讲人:Professor Philip S. Yu, University of Illinois at Chicago

日期:2018年6月19日(星期二)

时间:15:00 - 16:00

地点:数据科学与计算机学院 A101

主持:王昌栋副教授

摘要:In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a new type of learning task, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective fusion of relevant knowledge across different data sources, which depend upon not only the relatedness of these data sources, but also the target application problem. In this talk we examine how to fuse heterogeneous information to improve mining effectiveness over various applications, including social network, recommendation, mobile health (m-health) and Question Answering (QA).

个人介绍:Philip S. Yu is a Distinguished Professor in Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information Technology. Before joining UIC, Dr. Yu was with IBM, where he was manager of the Software Tools and Techniques group at the Watson Research Center. His research interest is on big data, including data mining, data stream, database and privacy. He has published more than 1,000 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents.

Dr. Yu is a Fellow of the ACM and the IEEE. He is on the steering committee of the ACM Conference on Information and Knowledge Management and was a member of the steering committee of IEEE Data Engineering and IEEE Conference on Data Mining. He was the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (2011-2017) and the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data”, and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. He also received the ICDM 2013 10-year Highest-Impact Paper Award, and the EDBT Test of Time Award (2014). He had received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 94th plateau of Invention Achievement Awards. He was an IBM Master Inventor. Dr. Yu received the B.S. Degree in E.E. from National Taiwan University, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University.