Philip S. Yu
Broad Learning on Big Data via Fusion of Heterogeneous Information
Biography
UIC Distinguished Professor and Wexler Chair in Information Technology University of Illinois at Chicago, Department of Computer Science.
Philip S. Yu’s main research interests include data mining, privacy preserving publishing and mining, data streams, database systems, Internet applications and technologies, multimedia systems, parallel and distributed processing, and performance modeling.
He is a Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information and Technology. He was manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. Dr. Yu has published more than 500 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 of the IEEE. He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery from Data.
He is on the steering committee of IEEE Conference on Data Mining and was a member of the IEEE Data Engineering steering committee. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004), an editor, advisory board member and also a guest co-editor of the special issue on mining of databases.
He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he was the program chair or co-chairs of the IEEE Workshop of Scalable Stream Processing Systems (SSPS��07), the IEEE Workshop on Mining Evolving and Streaming Data (2006), the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC’ 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE’ 06), the 11th IEEE Intl. Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, the 2nd IEEE Intl. Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE Intl. Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair or co-chairs of the 2006 ACM Conference on Information and Knowledge Management, the 14th IEEE Intl. Conference on Data Engineering, and the 2nd IEEE Intl. Conference on Data Mining. He had received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 93rd plateau of Invention Achievement Awards. He was an IBM Master Inventor. Dr. Yu received a Research Contributions Award from IEEE Intl. Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999.
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.
Abstract
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).
