• Irina Rish is a research staff member at the Biometaphorical Computing Department, IBM T. J. Watson Research Center. She received an M.S. in applied mathematics from Moscow Gubkin Institute, Russia, and a Ph.D. in computer science from the University of California, Irvine. Dr. Rish's primary research interests are in the areas of probabilistic inference, machine learning, and information theory. In particular, she has been working on probabilistic inference with Bayesian networks, approximation algorithms, Bayesian learning, active learning, sparse regression and sparse matrix factorization with applications to autonomic computing, including problem diagnosis and performance management of distributed computer systems and networks, as well as collaborative prediction, and has over 40 conference and journal publications on the above topics. Her current research focus is on applyng machine-learning techniques to neuroscience, and particularly on statistical analysis of fMRI data using sparse regression and dimensionality reduction. Dr. Rish taught several machine learning courses at the Electrical Engineering and Computer Science departments of Columbia University as an adjunct professor, and co-organized several machine-learning workshops, including ICML workshop in 2000, NIPS workshops in 2003, 2005 and 2006, and ECML workshop in 2006.

  • Guillermo Cecchi received an education in Physics (MSc, University of La Plata, Argentina, 1991), Physics and Biology (PhD, The Rockefeller University, 1994-1999), and Imaging in Psychiatry (Postdoctoral Fellow, Cornell University 2000-2001). In 2001 he joined IBM Research to be part of the Biometaphorical Computing project, where he has been working on computational approaches to brain function and systems biology. His research interests have covered diverse aspects of theoretical biology, including Brownian transport, molecular computation, spike reliability in neurons, song production and representation in songbirds, statistics of natural images and visual perception, statistics of natural language, and brain imaging. Recently, he has pioneered the use of statistical network theory for the analysis and modeling of functional brain networks.

  • Rajarshi Das is a Research Staff Member at IBM T.J. Watson Research Center in Hawthorne, New York, where he is actively involved in applying AI and machine learning techniques for self-managing computing systems. He has successfully co-organized three NIPS workshops: Robust Communication Dynamics in Complex Networks (2003), Value of Information in Inference, Learning and Decision-Making (2005), and Applications of Nonlinear Dimensionality Reduction (2006). Rajarshi received a B.Tech. in electrical engineering from the Indian Institute of Technology, a Ph.D. in computer science from the Colorado State University, Fort Collins, and was a post-doctoral fellow at Los Alamos National Laboratory. Rajarshi has published over 40 papers on various topics in AI and his research efforts have been detailed in publications such as The New York Times, The Washington Times and The Telegraph.

  • Tony Jebara is an Assistant Professor of Computer Science at Columbia University. He is Director of the Columbia Machine Learning Laboratory whose research focuses upon machine learning, computer vision and related application areas such as human-computer interaction. Jebara is also a Principal Investigator at Columbia's Vision and Graphics Center. He has published over 40 peer-reviewed papers in conferences and journals including NIPS, ICML, UAI, COLT, JMLR, CVPR, ICCV, and PAMI. He is the author of the book Machine Learning: Discriminative and Generative (Kluwer). Jebara is the recipient of the Career award from the National Science Foundation and has also received honors for his papers from the International Conference on Machine Learning and from the Pattern Recognition Society. He has served as chair, program committee member and reviewer for various conferences and workshops. Jebara's research has been featured on television (ABC, BBC, New York One, TechTV, etc.) as well as in the popular press (Wired Online, Scientific American, Newsweek, Science Photo Library, etc.). Jebara obtained his Bachelor's from McGill University (at the McGill Center for Intelligent Machines) in 1996. He obtained his Master's in 1998 and his PhD in 2002 both from the Massachusetts Institute of Technology (at the MIT Media Laboratory). He is currently a member of the IEEE, ACM and AAAI. Professor Jebara's research and laboratory are supported in part by the Central Intelligence Agency, Microsoft, Alpha Star Corporation and the National Science Foundation.

  • Gerry Tesauro is a Research Staff Member at the IBM TJ Watson Research Center. He is best known for developing TD-Gammon, a self-teaching program that learned to play backgammon at human world championship level. He has also worked on theoretical and applied machine learning in a wide variety of other settings, including multi-agent learning, dimensionality reduction, active learning, credit scoring, computer virus recognition, computer chess (Deep Blue), intelligent e-commerce agents and self-managing computing systems. He has extensive experience in conference and workshop organization, and has organized several workshops in the last several years at ICML, NIPS, IJCAI and ECML. Tesauro has a PhD in theoretical physics from Princeton University and is a member of the NIPS Foundation Board of Directors.Gerry Tesauro

  • Martin Wainwright joined the faculty at University of California at Berkeley in Fall 2004, with a joint appointment between the Department of Statistics and the Department of Electrical Engineering and Computer Sciences. He received his Bachelor's degree in Mathematics from University of Waterloo, and his Ph.D. degree in Electrical Engineering and Computer Science (EECS) from Massachusetts Institute of Technology (MIT), for which he was awarded the George M. Sprowls Prize from the MIT EECS department in 2002. He is interested in large-scale statistical models, and their applications to communication and coding, machine learning, and statistical signal and image processing. He has received an NSF-CAREER Award (2006), an Alfred P. Sloan Foundation Research Fellowship (2005), an Okawa Research Grant in Information and Telecommunications (2005), the 1967 Fellowship from the Natural Sciences and Engineering Research Council of Canada (1996--2000), and several outstanding conference paper awards.