• 中文 (中国)
  • English
  • 美国伊利诺伊大学芝加哥分校教授 Philip S. Yu:Broad Learning on Big Data via Fusion of Heterogeneous Information

    philip-s-yu

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

    时间:2019年5月21日上午10点-11点30分

    地点:新主楼G座849室

    主办单位:大数据科学与脑机智能高精尖创新中心

    题目:Broad Learning on Big Data via Fusion of Heterogeneous Information


    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).


    简介:

    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 1,100 referred conference and journal papers cited more than 103,000 times with an H-index of 152. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).