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Broad Learning: A New Perspective on Mining Big Data
浏览次数:日期:2020-11-23编辑:信科院 科研办

报告题目:Broad Learning: A New Perspective on Mining Big Data

报告人:Philip S. Yu教授,IEEE FellowACM Fellow,美国伊利诺伊大学芝加哥分校计算机科学系特聘教授

报告时间:20201124日 上午900

报告地点:Zoom在线会议

https://us02web.zoom.us/j/88171222260?pwd=eVZvQmVuZUtVR2N0bUo3ZEpUeHQrZz09

Meeting ID: 881 7122 2260

Passcode: HNU2020

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报告摘要: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).

  

报告人简介: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 1,200 referred conference and journal papers cited more than 130,000 times with an H-index of 167. 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).

 

Philip S. Yu(俞士纶)教授是美国伊利诺伊大学芝加哥分校(UIC)计算机科学系著名教授、信息技术领域Wexler讲座教授;加入UIC之前,曾在美国IBM Watson研究中心工作多年,创建了世界知名的数据挖掘及数据管理部门;是ACMIEEE院士(Fellow)。Yu教授因在大数据挖掘、融合以及匿名化方面深具影响力的研究和科学贡献,获得2016ACM SIGKDD创新奖;因在大数据的可扩展性索引、查询、搜索、挖掘以及匿名化问题上开创性和基础性的创新贡献,获得2013IEEE CS技术成就奖;因在数据挖掘领域开创性的贡献,获得2003IEEE ICDM研究贡献奖。Yu教授先后在国际著名学术期刊及会议上发表论文1100余篇,论文被引用超过130644次,H-index(高引用指数)高达167,申请专利300余项,是名列全球计算机科学领域高引作者前十的华人

 

邀请人:李克勤

 

联系人:陈建国