ICMLA 2008 Keynote Speakers (alphabetic order)
Prof. Philip E. Bourne, University of California San Diego, Fellow AMIA
Phil Bourne is a Professor in the Skaggs School of Pharmacy and Pharmaceutical Sciences (SSPPS) and the Department of Pharmacology at the University of California San Diego (UCSD), Adjunct Professor at the Burnham Institute, and Associate Director of the RCSB Protein Data Bank (PDB). He is a Past President of the International Society for Computational Biology, an elected fellow of the American Medical Informatics Association and Founding Editor-in-Chief of the open access journal PLoS Computational Biology. His interest in machine learning comes from its application in studies of protein-protein interactions, protein-ligand interactions and the study of protein flexibility. Most recently he has become interested in the application of machine learning to the open access scientific corpus.
Prof. Michael Jordan, University of California, Berkeley, Fellow of IEEE, AAAI, IMS, AAAS
Michael Jordan is Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters from Arizona State University, and earned his PhD in 1985 from the University of California, San Diego. He was a professor at the Massachusetts Institute of Technology from 1988 to 1998. He has published over 250 research articles on topics in computer science, statistics, electrical engineering, molecular biology and cognitive neuroscience. His research in recent years has focused on probabilistic graphical models, kernel machines, nonparametric Bayesian methods and applications to problems in information retrieval, signal processing and bioinformatics. Prof. Jordan was named a Fellow of the American Association for the Advancement of Science (AAAS) in 2006. He is a Fellow of the IMS, a Fellow of the IEEE and a Fellow of the AAAI.
Prof. Andrew Moore, Carnegie Mellon University, Fellow of AAAI
Andrew Moore is director of Google’s newest engineering office in Pittsburgh. Prior to joining Google in January 2006, Andrew was a Professor of Robotics and Computer Science at the School of Computer Science, Carnegie Mellon University. Andrew has received three best paper awards in recent years and has been keynote speaker at four top international conferences in his field: International Conference on Machine Learning (ICML), Neural Information Processing Systems (NIPS) , Uncertainty in Artificial Intelligence (UAI) and Knowledge Discovery in Databases (KDD). Andrew serves on several editorial boards, and in industrial, government and academic advisory roles, and in 2003 jointly (with Mike Wagner of the University of Pittsburgh) briefed President Bush on data mining for bioterrorism detection. In 2005 he was elected as a Fellow of AAAI: the American Association for Artificial Intelligence.
Dan Roth, Professor, University of Illinois at Urbana-Champaign
Dan Roth is a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana-Champaign and a Willet Faculty Scholar of the College of Engineering. Roth has published broadly in machine learning natural language processing, knowledge representation and reasoning and has developed advanced machine learning based tools for natural language applications that are being used widely by the research community, including an award winning Semantic Parser. Among his paper awards are the best paper award in IJCAI-99 and the 2001 AAAI Innovative Applications of AI Award. Roth was the program chair of CoNLL'02 and of ACL'03, and has been on the editorial board of several journals in his research areas. He is currently an associate editor for the Journal of Artificial Intelligence Research and the Machine Learning Journal. Prof. Roth got his B.A Summa cum laude in Mathematics from the Technion, Israel and his Ph.D in Computer Science from Harvard University in 1995.
Prof. Jude Shavlik, University of Wisconsin, Madison, Fellow of AAAI
Jude Shavlik is a Professor of Computer Sciences and of Biostatistics and Medical Informatics at the University of Wisconsin - Madison, and is a Fellow of the American Association for Artificial Intelligence. He has been at Wisconsin since 1988, following the receipt of his PhD from the University of Illinois for his work on Explanation-Based Learning. His current research interests include machine learning and computational biology, with an emphasis on using rich sources of training information. He served for three years as editor-in-chief of the AI Magazine and serves on the editorial board of about a dozen journals. He chaired the 1998 International Conference on Machine Learning, co-chaired the First International Conference on Intelligent Systems for Molecular Biology in 1993, co-chaired the First International Conference on Knowledge Capture in 2001, was conference chair of the 2003 IEEE Conference on Data Mining, and co-chaired the 2007 International Conference on Inductive Logic Programming. He was a founding member of both the board of the International Machine Learning Society and the board of the International Society for Computational Biology.
Prof. Bin Yu, University of California, Berkeley, Fellow of IEEE, IMS, ASA
Bin Yu received her Ph.D. in Statistics from UC Berkeley in 1990. She is now Chancellor's Professor in the departments of Statistics and of Electrical Engineering & Computer Science at UC Berkeley. She is also a founding co-director of the Microsoft Lab on Statistics and Information Technology and a ChangJiang Chair Professor, both at Peking University. She was a Guggenheim Fellow in 2006, and is a Fellow of IEEE, the Institute of Mathematical Statistics, and the American Statistical Association. She was also a co-recipient of the 2006 Best Paper Award of IEEE Signal Processing Society. She is serving on many editorial boards including Journal of Machine Learning Research, Journal of American Statistical Association, Technometrics, and Statistica Sinica. She was the program chair of Graybill conference on Statistics and Information Technology in 2005, of IMS-CSSP joint meeting in Beijing in 2005, and of the Pao-Lu Hsu Statistics conference in 2007 on Machine Learning at Peking University. She is on the National Advisory Council (Scientific Advisory Board) of NSF's Statistical and Applied Mathematical Sciences Institute (SAMSI).