4月3日电工系学术汇报信息-Mining the academic social network

发布时间:2015-03-31 
报告题目:Mining the academic social network
报告时间:2015年04月03日下午3:00~4:00
报告地点:物理楼521会议室
主讲人:Dah Ming Chiu 教授(香港中文大学讯息工程系主任、IEEE Fellow)
联系人:徐跃东

摘要:Academic publication metadate is routinely used for performance assessment, for example in tenure/promotion and university rankings. But actually there is a myriad of other interesting questions we can study using the data, in particular some "social" aspects of academic research. For example, we can try to find out how collaboration (co-authorship) is correlated to research excellence, how top researchers get to the top, how does the research community population changed over time and how it affects research performance, how have the top research topics changed over time, and what else changed in the way we do research, and so on. In this talk, I will report some projects we have done, as well as some on-going work, and discuss the challenges and opportunities in this line of research.

简介:Prof. Dah Ming Chiu received his first degree from Imperial College London and his Ph.D. degree from Harvard University. He worked in industry for several hightech companies of his time: Bell Labs, DEC and Sun Microsystem Labs. He returned to academia in 2002 to become a professor in the Department of Information Engineering at the Chinese University of Hong Kong. He is currently the department chairman. Dah Ming is an IEEE Fellow. He served as an associate editor for IEEE/ACM Transaction on Networking from 2006 to 2011, and served on the TPC for many networking conferences. He is the general co-chair of ACM Sigcomm 2013, held in Hong Kong with record attendance.
Prof. Chiu is one of the early contributors on Internet congestion control. His work on fair and efficient network resource allocation has been cited by many. His recent research interest includes resource allocation in wireless networks, network content distribution, and cloud services, based on measurement and data analysis. His pet project at the moment is on analyzing and modeling academic social networks.