Tomaso Poggio

July 11, 2009

Tomaso A. Poggio, is the Eugene McDermott Professor at the Department of Brain and Cognitive Sciences; Co-Director, Center for Biological and Computational Learning; Member for the last 27 years of the Computer Science and Artificial Intelligence Laboratory at MIT; since 2000, member of the faculty of the McGovern Institute for Brain Research. Born in Genoa, Italy in 1947 (and naturalized in 1994), he received his Doctor in Theoretical Physics from the University of Genoa in 1971 and was a Wissenschaftlicher Assistant, Max Planck Institut für Biologische Kybernetik, Tüebingen, Germany from 1972 until 1981 when he became Associate Professor at MIT. He is an honorary member of the Neuroscience Research Program, a member of the American Academy of Arts and Sciences and a Founding Fellow of AAAI. He received several awards such as the Otto-Hahn-Medaille Award of the Max-Planck-Society, the Max Planck Research Award (with M. Fahle), from the Alexander von Humboldt Foundation, the MIT 50K Entrepreneurship Competition Award, the Laurea Honoris Causa from the University of Pavia in 2000 (Volta Bicentennial) and the 2003 Gabor Award. He is one of the most cited computational neuroscientists (with a H-index greater than 70 – based on GoogleScholar).

Tomaso Poggio is a computational neuroscientist whose contributions range from the biophysical and behavioral studies of the visual system to the computational analyses of vision and learning in humans and machines. With W. Reichardt, Poggio characterized quantitatively the visuo-motor control system in the fly, deriving. equations that could predict the fly’s tracking and fixation behavior. He also modeled the fly’s neural circuitry underlying the detection of motion boundaries, connecting it to behaviour and physiology, pioneering normalization circuits, later used for visual cortex. With D. Marr, Poggio characterized necessary levels of analysis in computational neuroscience and developed stereo algorithms which served as the primary model of stereopsis and as exemplar for other vision algorithms in the field. At the biophysical level, Poggio and coworkers pioneered models suggesting that dendritic trees and synapses have a key computational role – a view now receiving experimental confirmation. At the level of computation, Poggio introduced regularization theory as a general framework to solve the ill-posed problems of vision.

His most cited papers describe seminal contributions to learning theory where Poggio developed the mathematics of Regularization Networks. He applied learning techniques to bioinformatics, to computer graphics, computer vision and to neuroscience e.g. to decrypt the neural code in IT. In the last decade he has worked on a hierarchical extension of learning developing a feedforward quantitative model of visual recognition in the visual cortex which has been a useful tool to drive and interpret several physiological experiments, is consistent with human performance in rapid categorization and suggests novel architectures to the field of computer vision, based on neuroscience of vision.

See essays by this author:
A Second Wave of Network Technologies