Abstract: The main objective of this talk is to present recent developments in the applications of kernel methods and Support Vector Machines (SVMs) to severe weather prediction. I will also discuss how kernel methods can be used to uncover physically meaningful, predictive patterns in weather radar data that alert to severe weather before the severe weather occurs. Specific indices related to the analysis of imbalanced weather data (for example tornado data) using kernel methods will be also discussed. In addition a family of learning algorithms, motivated by Support Vector Machines, capable of replacing traditional methods for assimilating data and generating forecasts, without requiring the assumptions made by the assimilation methods (Kalman filters) and an application of kernel methods to processing the states of a Quasi-Geostrophic (QG) numerical model will be presented. Extensions of those techniques to other areas of applications will be investigated.

Brief Biography of the Speaker:
Theodore B. Trafalis, PhD, is a Professor in the School of Industrial Engineering at the University of Oklahoma, USA and adjunct professor in the School of meteorology. He earned his BS in mathematics from the University of Athens, Greece, his MS in Applied Mathematics, MSIE, and PhD in Operations Research from Purdue University. He is a member of INFORMS, SIAM, Hellenic Operational Society, International Society of Multiple Criteria Decision Making, and the International Society of Neural Networks. He has been listed in several Who’s Who biographies such as in the 1993/1994 edition of Who’s Who in the World. He was a visiting Assistant Professor at Purdue University (1989-1990), an invited Research Fellow at Delft University of Technology, Netherlands (1996), a visiting Associate Professor at Blaise Pascal University, France, and at the Technical University of Crete (1998). He was also an invited visiting Associate Professor at Akita Prefectural University, Japan (2001). The academic year 2006-2007 was on a sabbatical at the National Center for Scientific Research “Demokritos”, Institute of Informatics and Telecommunications, Computational Intelligence Laboratory (CIL), Athens, Greece. His research interests include: operations research/management science, mathematical programming, interior point methods, multiobjective optimization, control theory, artificial neural networks, kernel methods, evolutionary programming data mining, global optimization and weather applications. He has published more that one hundred articles in journals, conference proceedings, edited books, made over one hundred technical presentations, and received several awards for his papers. In 2004 he received the Regents Award at the University of Oklahoma for his research activities. He has been continuously funded through National Science Foundation (NSF) and received the NSF Research Initiation Award in 1991. In 2006 he was the editor of a special issue in Support Vector Machines for the journal of Computational Management Science. He also co-edited a special issue in “Learning from Data” for the same journal that is in press in 2008. Prof. Trafalis currently serves as chief editor of Intelligent Control and Automation and an associate editor for the Journal of Computational Management Science, the Journal of Heuristics, Technology and Investment and several other journals. In addition he has been on the Program Committee of several international conferences in the field of intelligent systems, data mining and optimization. He currently serves as chief editor of Intelligent Control and Automation and an associate editor for the Journal of Computational Management Science, the Journal of Heuristics, Technology and Investment. He was co-organizer of the International Conference on the Dynamics of Disasters, Athens, Greece, 2006.