Andrew Blake

William T. Freeman

Zoubin Ghahramani

David Haussler

Geoffrey Hinton

Lawrence Saul

Unfortunately, due to illness, Tommi Jaakkola was unable to attend the workshop.

Biographies of Invited Speakers

Andrew Blake

Andrew Blake graduated in 1977 from Trinity College, Cambridge with a B.A. in Mathematics and Electrical Sciences. After a year as a Kennedy Scholar at MIT and two years in the defence electronics industry, he studied for a doctorate at the University of Edinburgh which was awarded in 1983. Until 1987 he was on the faculty of the department of Computer Science at the University of Edinburgh and a Royal Society Research Fellow. From 1987 to 1999, he was on the faculty of the Department of Engineering Science in the University of Oxford, where he ran the Visual Dynamics Research Group, became a Professor in 1996, and and was a Royal Society Senior Research Fellow for 1998-9. In 1999 he moved to Microsoft Research Cambridge as Senior Researcher working in Machine Learning and Perception, while continuing to be associated with the University of Oxford as Visiting Professor of Engineering.

His main research activities are in computer vision. He has published several books including "Visual Reconstruction" with A.Zisserman (MIT press), "Active Vision" with Alan Yuille (MIT Press) and "Active Contours" with Michael Isard (Springer-Verlag). He has twice won the prize of the European Conference on Computer Vision, with R. Cipolla in 1992 and with M. Isard in 1996, and was awarded the IEEE David Marr Prize (jointly with K. Toyama) in 2001. He has served as programme chairman for the International Conference on Computer Vision in 1995 and 1999, and is on the editorial boards of the journals "Image and Vision Computing", the "International Journal of Computer Vision" and "Computer Vision and Image Understanding". He was elected a Fellow of the Royal Academy of Engineering in 1998.

William T. Freeman

William T. Freeman is an Associate Professor of Electrical Engineering and Computer Science at the Artificial Intelligence Laboratory at MIT, joining the faculty in September, 2001. From 1992 - 2001 he worked at Mitsubishi Electric Research Labs (MERL), in Cambridge, MA, most recently as Sr. Research Scientist and Associate Director. He studied computer vision for his PhD in 1992 from the Massachussetts Institute of Technology, and received a BS in physics and MS in electrical engineering from Stanford in 1979, and an MS in applied physics from Cornell in 1981.

His current research interests include machine learning applied to computer vision, Bayesian models of visual perception, and interactive applications of computer vision. In 1997, he received the Outstanding Paper prize at the Conference on Computer Vision and Pattern Recognition for work on applying bilinear models to "separating style and content". Previous research topics include steerable filters and pyramids, the generic viewpoint assumption, color constancy, and computer vision for computer games. He holds 15 patents. From 1981 - 1987, he worked at the Polaroid Corporation . There he co-developed an electronic printer (Polaroid Palette) , and developed algorithms for color image reconstruction which are used in Polaroid's electronic camera . In 1987-88, Dr. Freeman was a Foreign Expert at the Taiyuan University of Technology , P. R. of China.

Dr. Freeman is an Associate Editor of IEEE Trans. on Pattern Analysis and Machine Intelligence (IEEE-PAMI), and is a member of the IEEE PAMI TC Awards Committee. He is active in the program or organizing committees of the International Conference on Face and Gesture Recognition, Computer Vision and Pattern Recognition (CVPR), the International Conference on Computer Vision (ICCV), Neural Information Processing Systems (NIPS), and SIGGRAPH.

Zoubin Ghahramani

My early childhood was spent in the former Soviet Union and Iran. I then moved to Spain where I studied at the American School of Madrid for 10 years. I attended the University of Pennsylvania where I was given the Dean's Scholar Award and obtained a BA degree in Cognitive Science and a BSEng degree in Computer Science in 1990. In 1995, I obtained my PhD in Cognitive Neuroscience from the Massachusetts Institute of Technology with a Fellowship from the McDonnell-Pew Foundation. My dissertation is titled "Computation and Psychophysics of Sensorimotor Integration" and was supervised by Prof Michael Jordan and Prof Tomaso Poggio.

I moved to the University of Toronto in 1995 where I was an ITRC Postdoctoral Fellow in the Artificial Intelligence Lab of the Department of Computer Science. In the autumn of 1998, I became a Lecturer in the Gatsby Computational Neuroscience Unit at University College London. I hold appointments as Honorary Lecturer in the Department of Computer Science and in the Department of Psychology at UCL. My current research interests include studying how the brain controls movement and integrates information from different senses, developing computational theories of learning in biological and artificial systems, and applying probabilistic approaches to computer intelligence.

David Haussler

B.A., Mathematics, Connecticut College
M.S., Applied Mathematics, California Polytechnic State University, San Luis Obispo
Ph.D., Computer Science, University of Colorado at Boulder

David Haussler is an investigator for the Howard Hughes Medical Institute, he holds the UC Presidential Chair in Computer Science at the Santa Cruz campus, he is a consulting professor for the Stanford Medical School and the University of California San Francisco Biopharmaceutical Sciences Department, a fellow of the American Association for the Advancement of Science (AAAS) and the American Association for Artificial Intelligence (AAAI), and a member of the nominating committee for the International Society for Computational Biology. He is a past chairman of the Steering Committee for the Computational Learning Theory Conferences (COLT), an Associate Editor for the Journal of Computational Biology, and was an action editor for the journal Machine Learning. He is currently Director of the Center for Biomolecular Science & Engineering at UCSC and scientific co-director of the multi-campus Institute for Bioengineering, Biotechnolgy and Quantitative Biomedical Research at USCF, UCB and UCSC.

His research interests are in several areas, including: genomics, bioinformatics, machine learning, statistical decision theory, pattern recognition, neural networks, algorithms and complexity. He is a member of the ACM, IMS, AAAS, and the IEEE.

Geoffrey E. Hinton

Geoffrey Hinton received his BA in experimental psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to Toronto.

Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the American Association for Artificial Intelligence and a former president of the Cognitive Science Society. He received an honorary doctorate from the University of Edinburgh in 2001. He was awarded the first David E. Rumelhart prize (2001), the IEEE Neural Network Pioneer award (1998) and the ITAC/NSERC award for contributions to information technology (1992).

A simple introduction to Geoffrey Hinton's research can be found in his articles in Scientific American in September 1992 and October 1993. He investigates ways of using neural networks for learning, memory, perception and symbol processing and has over 150 publications in these areas. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, Helmholtz machines and products of experts. His current main interest is in unsupervised learning procedures for neural networks with rich sensory input.

Tommi Jaakkola

Prof. Jaakkola received his MS degree in theoretical physics from Helsinki University of Technology, 1992, and his PhD in computational neuroscience from Massachusetts Institute of Technology, 1997. He held a Sloan postdoctoral fellowship in computational biology from 1997 until late 1998 when he joined the MIT EECS faculty. He is now an associate professor of Electrical Engineering and Computer Science, and a recipient of the Sloan Research Fellow award. Prof. Jaakkola leads a machine learning group at MIT, focusing on the theory, algorithms, and applications of statistical inference and machine learning.

Lawrence Saul

Dr. Lawrence Saul joined the faculty of the Department of Computer and Information Science at the University of Pennsylvania in January 2002. Previously, he worked as a member of technical staff in the Speech and Image Processing Center at AT&T Labs and as a postdoctoral fellow in the Center for Biological and Computational Learning at M.I.T. He received his Ph.D. in physics from M.I.T. He is currently a member of the editorial board for the Journal of Machine Learning Research. He served previously on the editorial board of Machine Learning and on several program committees for NIPS and AI-STATS. His current research interests include dimensionality reduction of sensory inputs, variational methods for supervised and unsupervised learning, and real time voice processing with audiovisual feedback.