CIS Colloquium, Apr 16, 2013, 11:00AM – 12:00PM, Wachman 1015D
Machine Learning with Big Linked Data
Jun (Luke) Huan, University of Kansas
Abstract:
Graphs are widely used modeling tools that capture objects and their relation (links). Graph modeled data are found in diverse application areas including bioinformatics, cheminformatics, social networks, wireless sensor networks among many others. In this talk we will present our recent work on graph kernel functions and graph similarity search in the context of big data, focusing on scalable algorithmic approaches for graph data. Applications of graph modeling techniques in drug discovery and social network analysis will be touched at the end.
Bio:
Dr. Jun (Luke) Huan is an associate professor in the Electrical Engineering and Computer Science department at the University of Kansas. He directs the Bioinformatics and Computational Life Sciences Laboratory at the KU Information and Telecommunication Technology Center (ITTC). He holds courtesy appointments at the KU Bioinformatics Center and Bioengineering Program. Dr. Huan was a recipient of the National Science Foundation Faculty Early Career Development Award in 2009. He has published more than 80 peer-reviewed papers in leading conferences and journals. His group won the best student paper award in ICDM ’11 and the best paper award (runner-up) in CIKM’09. Dr. Huan served on the program committees of leading international conferences including ACM SIGKDD, ICML, IEEE ICDE, ACM CIKM, and IEEE ICDM.