Date(s) - 21 Feb 2014
10:00 AM - 11:30 AM
3043 ECpE Building Addition
Title: Learning and Mining with Big Graph Data
Speaker: Jun Huan, Associate Professor, 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 learning algorithms in the context of big data, focusing on scalable algorithmic approaches for big structured data. Applications of graph learning and mining techniques in drug discovery and social network analysis will be touched at the end.
Biography: Dr. Jun (Luke) Huan is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas. He directs the Bioinformatics and Computational Life Sciences Laboratory at KU Information and Telecommunication Technology Center (ITTC) and the Cheminformatics core at KU Specialized Chemistry Center, funded by NIH. He holds courtesy appointments at the KU Bioinformatics Center, the KU Bioengineering Program, and a visiting professorship from GlaxoSmithKline plc.. Dr. Huan received his Ph.D. in Computer Science from the University of North Carolina.
Dr. Huan works on machine learning, data mining, big data, and interdisciplinary topics including bioinformatics. He has published more than 90 peer-reviewed papers in leading conferences and journals and has graduated more than ten graduate students including six PhDs. Dr. Huan was a recipient of the National Science Foundation Faculty Early Career Development Award in 2009. His group won the Best Student Paper Award at the IEEE International Conference on Data Mining in 2011 and the Best Paper Award (runner-up) at the ACM International Conference on Information and Knowledge Management in 2009. Dr. Huan serves the editorial board of several international journals including the Springer Journal of Big Data, the AIMS International Journal of Big Data and Information Analytics, and the International Journal of Data Mining and Bioinformatics. He regularly serves the program committee of top-tier international conferences on machine learning, data mining, big data, and bioinformatics.
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