IST Colloquium, Jul 20, 2006, 01:30PM – 03:00PM, Wachman 322
Theory, Algorithms and Applications of Link Analysis
William M. Pottenger, Computer Science and Engineering Department, Lehigh University
Due to recent concerns with terrorism and security there has been an increasing focus on techniques that discover links and relations in data. Numerous efforts that employ data mining techniques have contributed to this field, and of these several focus on higher-order links, which can reveal hidden or indirect relationships in data. Few if any efforts, however, have studied the patterns or characteristics in sets of higher-order links to discover meaningful relationships. As noted, such links reveal hidden, or latent, information in the data and this latent information can be leveraged in a number of ways. In this talk I will focus on the theory, algorithms and applications of higher-order link analysis research being conducted in my lab. Applications range from abnormal event detection in Internet router communications to discovery of ‘novel nuggets’ in distributed association rule mining on law enforcement data.
William M. (Bill) Pottenger has received over $4M in competitive research funding from the NSF, NIJ, ARL, industry, etc., has over $2M in pending proposals, 40+ peer-reviewed publications, has served as editor and chair of several proceedings/symposia and made over 50 professional presentations/seminars. Bill is a member of ACM, IEEE, SIAM and has served as a program committee member/referee for numerous professional venues, journals, etc. Among other awards he is the recipient of the PC. Rossin Endowed Assistant Professorship (2001-2003) and a United States Air Force Certificate of Appreciation (2001). Bill is currently on the faculty of the Computer Science and Engineering Department at Lehigh University. Prior to coming to Lehigh, Bill completed his Ph.D. in Computer Science at the University of Illinois at Urbana-Champaign and worked as a Research Scientist at the National Center for Supercomputing Applications. Bill’s research interests include the fields of machine learning as applied in text/data mining.