Monte Carlo Methods In Artificial Intelligence
March 18-22, 2013, Oregon State University - Corvallis, Oregon

Instructors

Tom Dietterich
Thomas G. Dietterich (AB Oberlin College 1977; M.S. University of Illinois 1979; Ph.D. Stanford University 1984) is one of the founders of the field of Machine Learning. Among his research contributions was the application of error-correcting output coding to multiclass classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models (including conditional random fields and latent variable models). Among his writings are Chapter XIV (Learning and Inductive Inference) of the Handbook of Artificial Intelligence, the book Readings in Machine Learning (co-edited with Jude Shavlik), and his frequently-cited review articles Machine Learning Research: Four Current Directions and Ensemble Methods in Machine Learning.
He served as Executive Editor of Machine Learning (1992-98) and helped co-found the Journal of Machine Learning Research. He is currently the editor of the MIT Press series on Adaptive Computation and Machine Learning. He also served as co-editor of the Morgan-Claypool Synthesis Series on Artificial Intelligence and Machine Learning. He has organized several conferences and workshops including serving as Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90), Technical Program Chair of the Neural Information Processing Systems (NIPS-2000) and General Chair of NIPS-2001 He is a Fellow of the ACM, AAAI, and AAAS. He served as founding President of the International Machine Learning Society, and he is currently a member of the Steering Committee of the Asian Conference on Machine Learning.
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Prasad Tadepalli
Prasad Tadepalli has an M. Tech in Computer Science from Indian Institute of Technology, Madras, India and a Ph.D. from Rutgers University, New Brunswick. He joined Oregon State University, Corvallis, as an assistant professor in 1989. He is now a professor in the School of Electrical Engineering and Computer Science of Oregon State University.
He co-authored over a hundred papers in Artificial Intelligence and Machine Learning in various journals, conferences, and workshops. He organized many workshops and tutorials and co-chaired the international conference on inductive logic programming in 2007. He was a member of many conference program committees and is currently an action editor for the Journal of Artificial Intelligence Research, and the Machine Learning journal.
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Alan Fern
Alan Fern is an Associate Professor of Computer Science at Oregon State University. He received his Ph.D (2004) and M.S (2000) in Computer Engineering from Purdue University, and his B.S (1997) in Electrical Engineering from the University of Maine. He received a National Science Foundation CAREER award in 2006 and currently serves on the editorial boards of the Journal of Artificial Intelligence Research and the Machine Learning Journal. His research interests span a range of topics in artificial intelligence, including machine learning and automated planning/control.
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Weng-Keen Wong
Weng-Keen Wong is an Associate Professor of Computer Science at Oregon State University. He received his Ph.D. (2004) and M.S. (2001) in Computer Science at Carnegie Mellon University, and his B.Sc. (1997) from the University of British Columbia. His research areas are in data mining and machine learning, with specific interests in anomaly detection, surveillance algorithms, and mining large scale datasets. His Ph.D. thesis was entitled "Data Mining Algorithms for the Early Detection of Disease Outbreaks" and he is involved in the field of disease outbreak surveillance.
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