Keynote Speech II

Broad Learning:  A New Perspective on Mining Big Data

Speaker: Dr. Philip S. Yu

Professor

Department of Computer Science

University of Illinois at Chicago

Chicago, IL 60607

psyu@uic.edu

 

Biography:

Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. 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. Dr. Yu has published more than 1,400 referred conference and journal papers cited more than 127,000 times with an H-index of 164. He has applied for more than 300 patents. Dr. Yu was the Editors-in-Chief of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).

Abstract:

In the era of big data, there is 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).