Name:Tomaso A. Poggio
Professional Title and Affiliation:The Eugene McDermott Professor
Department of Brain and Cognitive Sciences
McGovern Institute
CSAIL (Computer Science and Artificial Intelligence Lab)
Massachusetts Institute of Technology
Business Address:Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Brain and Cognitive Sciences Department
M.I.T., 46-5177B,
43 Vassar Street
Cambridge, MA 02142
Business Phone:617 253 2530
Business Fax:617 253 2964
E-mail:
50-WORD STATEMENT
Tomaso Poggio is one of the founders of computational neuroscience. He pioneered models of the fly’s visual system and of human stereovision, introduced regularization theory to computational vision, made key contributions to the biophysics of computation and to learning theory, developed an influential model of recognition in the visual cortex and more recently a theory of invariant representations in sensory cortex.
0BCURRICULUM VITAE
Tomaso A. Poggio, is the Eugene McDermott Professor at the Department of Brain and Cognitive Sciences; Director, Center for Brains, Minds and Machines; Member 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 (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), the 2003 Gabor Award, the 2009 Okawa prize and 2009 Okawa prize and the American Association for the Advancement of Science (AAAS) Fellowship (2009). He is one of the most cited computational neuroscientists (with a h-index greater than 100ß – based on GoogleScholar). He is somewhat unique in having a significant impact in most areas of sciences (as remarked in the Via-academy list with the note 'most eclectic scientist' in Italy).
Alternate Bio
Tomaso A. Poggio, is the Eugene McDermott Professor in the Dept. of Brain & Cognitive Sciences at MIT and the director of the new NSF Center for Brains, Minds and Machines at MIT of which MIT and Harvard are members among other Institutions. He is a member of both the Computer Science and Artificial Intelligence Laboratory and of the McGovern Brain Institute. He is an honorary member of the Neuroscience Research Program, a member of the American Academy of Arts and Sciences, a Founding Fellow of AAAI and a founding member of the McGovern Institute for Brain Research. Among other honors he received the Laurea Honoris Causa from the University of Pavia for the Volta Bicentennial, the 2003 Gabor Award, the Okawa Prize 2009, and the AAAS Fellowship. He is one of the most cited computational scientists with contributions ranging 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 he characterized quantitatively the visuo-motor control system in the fly. With D. Marr, he introduced the seminal idea of levels of analysis in computational neuroscience. With C. Koch he developed the topic of Biophysics of Computation, attempting to connect biophysical mechanisms of neurons and synapses to computational functions of the brain. He introduced regularization as a mathematical framework to approach the ill-posed problems of vision and the key problem of learning from data. In the last decade he has developed an influential quantitative model of visual recognition in the visual cortex. The citation for the recent 2009 Okawa prize mentions his “…outstanding contributions to the establishment of computational neuroscience, and pioneering researches ranging from the biophysical and behavioral studies of the visual system to the computational analysis of vision and learning in humans and machines.” His research has always been interdisciplinary, between brains and computers. It is now focused on the mathematics of learning theory, the applications of learning techniques to computer vision and especially on computational neuroscience, in particular a theory of invariant representations in visual cortex.
1BSTATEMENT OF ACCOMPLISHMENTS
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 behavior 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, and is consistent with human performance in rapid categorization and suggests novel architectures to the field of computer vision, based on neuroscience of vision. The citation for the recent 2009 Okawa prize mentions his “…outstanding contributions to the establishment of computational neuroscience, and pioneering researches ranging from the biophysical and behavioral studies of the visual system to the computational analysis of vision and learning in humans and machines.”
PRINCIPAL CONTRIBUTIONS TO SCIENCE
Anselmi, F. J.Z. Leibo, L. Rosasco, J. Mutch, A. Tacchetti, and T. Poggio. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. CBMM Memo No. 001. arXiv:1311.4158v5. March 2014.
Isik, L., E.M. Meyers, J.Z. Leibo, T. Poggio, The dynamics of invariant object recognition in the human visual system, Journal of Neurophysiology, Oct 2, 2013 doi:10.1152/jn.00394.2013, PMID: 24089402
Liao, Q., J.Z. Leibo and T Poggio. Learning invariant representations and applications to face verification. Advances in Neural Information Processing Systems 26. (3057-3065). NIPS 2013. Lake Tahoe, Nevada. February 2014.
Poggio, T. (2014). “Tomaso Poggio” In L. R. Squire (Ed.), The History of Neuroscience in Autobiography, Volume 8. Oxford University Press, New York, New York
Anselmi F., J.Z. Leibo, L. Rosasco, J. Mutch, A. Tacchetti, and T. Poggio, "Magic Materials: a theory of deep hierarchical architectures for learning sensory representations", CBCL paper, Massachusetts Institute of Technology, Cambridge1, 2013, MA, April
Tan, C. and T. Poggio, "Faces as a Model Category" for Visual Object Recognition," MIT-CSAIL-TR-2013-004, CBCL-311, Massachusetts Institute of Technology, Cambridge, MA, March 18, 2013
Villa, S., L. Rosasco and T. Poggio, On Learnability, Complexity and Stability, arXiv 1303.5976 March 24, 2013
Canas G. D., T. Poggio, L. Rosasco, Learning Manifolds with K-Means and K-Flats., Advances in Neural Information Processing Systems, Lake Tahoe, December 2012
Isik, L.*, J.Z. Leibo* and T. Poggio, Learning and disrupting invariance in visual recognition with a temporal association rule. Front. Comput. Neurosci. 6:37. doi: 10.3389/fncom.2012.00037, June 25, 2012
*These authors contributed equally to this work.
Isik, L., E.M. Meyers, J.Z. Leibo, and T. Poggio, Preliminary MEG decoding results, MIT-CSAIL-TR-2012-010,CBCL-307, Massachusetts Institute of Technology, Cambridge, MA, April 20, 2012
Mroueh Y., T. Poggio, L. Rosasco, and J. J. Slotine Multi-class Learning with Simplex Coding., Advances in Neural Information Processing Systems, Lake Tahoe, December 2012
Poggio, T. The Levels of Understanding framework, revised. Perception, volume 41, pages 1017 - 1023, December 2012 doi:10.1068/p7299
Poggio T., J. Mutch, F. Anselmi, L. Rosasco, J.Z. Leibo, and A. Tacchetti, The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). MIT-CSAIL-TR-2012-035, Massachusetts Institute of Technology, Cambridge, MA, December 29, 2012.
Tan, C., J.Z. Leibo, and T. Poggio, Throwing Down the Visual Intelligence Gauntlet. Machine Learning for Computer Vision; eds Cipolla R., Battiato S., Farinella G.M., Springer: Studies in Computational Intelligence Vol. 411. July, 2012.
Chikkerur, S. and T. Poggio, Approximations in the HMAX Model, MIT-CSAIL-TR-2011-021/CBCL-298, Massachusetts Institute of Technology, Cambridge, MA, April 14, 2011
Dahan, E., A.J. Kim, A.W. Lo, T. Poggio, and N. Chan, Securities Trading of Concepts (STOC) Journal of Marketing Research: Vol. 48, No. 3, pp. 497-517., doi: 10.1509/jmkr.48.3.497, 2011
Isik, L., J.Z. Leibo, J. Mutch, S.W. Lee, and T Poggio, A hierarchical model of peripheral vision, MIT-CSAIL-TR-2011-031/CBCL-300, Massachusetts Institute of Technology, Cambridge, MA, June 2011
Isik, L., J.Z. Leibo and T. Poggio, Learning and disrupting invariance in visual recognition, MIT-CSAIL-TR-2011-040/CBCL-302, Massachusetts Institute of Technology, Cambridge, MA, September 10, 2011
Kuehne, T., H. Jhuang, E. Garrote, T. Poggio, and T. Serre, "HMDB: A Large Video Database for Human Motion Recognition," ICCV 2011, Click here for software documentation.
Leibo, J.Z., J. Mutch and T Poggio. How can cells in the anterior medial face patch be viewpoint invariant?, Presented at COSYNE 2011, Salt Lake City, UT. Available from Nature Precedings at dx.doi.org/10.1038/npre.2011.5845.1
Leibo, J.Z., J. Mutch andT Poggio, Learning to discount transformations as the computational goal of visual cortex, Presented at FGVC/CVPR 2011, Colorado Springs, CO. Available from Nature Precedings at dx.doi.org/10.1038/npre.2011.6078.1
Leibo, J.Z., Mutch J., and T. Poggio, Why The Brain Separates Face Recognition From Object Recognition, Advances in Neural Information Processing Systems, Granada Spain, December 2011
Mroueh, Y., T. Poggio and L. Rosasco, Regularization Predicts While Discovering Taxonomy, MIT-CSAIL-TR-2011-029/CBCL-299, Massachusetts Institute of Technology, Cambridge, MA, June 3, 2011
Poggio, T. and G. Geiger, Werner Reichardt: the man and his scientific legacy, MIT-CSAIL-TR-2011-011, CBCL-297, Massachusetts Institute of Technology, Cambridge, MA, March 4, 2011
Poggio, T., S. Voinea and L. Rosasco, Online Learning, Stability, and Stochastic Gradient Descent, Cornell University Library, arXiv:1105.4701v2 [cs.LG], May 25, 2011
Poggio, T. (sections with J. Mutch, J.Z. Leibo and L. Rosasco), The Computational Magic of the Ventral Stream: Towards a Theory, Nature Precedings, doi:10.1038/npre.2011.6117.1 July 16, 2011
Zhang Y.*, E. Meyers *, N. Bichot, T. Serre, T. Poggio, and R. Desimone, Object decoding with attention in inferior temporal cortex, PNAS Proceedings of the National Academy of Sciences, 108:8850-8855, 2011; Published online before print, May 9, 2011, doi: 10.1073/pnas.1100999108. *These authors contributed equally.
Chikkerur, S., T. Serre, C. Tan, and T. Poggio, "What and Where: A Bayesian inference theory of visual attention", Vision Research, [doi: 10.1016 /j.visres.2010.05.013], May 20, 2010
Jhuang, H., E. Garrote, J. Mutch, X. Yu, V. Khilnani, T. Poggio, A.D. Steele, and T. Serre."Automated home-cage behavioural phenotyping of mice. Nature Communications," 1, Article 68, [doi: 10.1038/ncomms1064], September 7, 2010. Click here for software documentation.
Smale, S., L. Rosasco, J. Bouvrie, A. Caponnetto, and T. Poggio, "Mathematics of the Neural Response", Foundations of Computational Mathematics,Vol. 10, 1, 67-91, June 2009 (online); February 2010 (print)
Kouh, M. and T. Poggio. “A Canonical Neural Circuit for Cortical Nonlinear Operations” Neural Computation, June 2008, Vol. 20, No. 6, Pages 1427-1451
Cadieu, C., M. Kouh, A. Pasupathy, C. Connor, M. Riesenhuber, and T. Poggio. HUA Model of V4 Shape Selectivity and InvarianceUH, Journal of Neurophysiology, Vol. 98, 1733-1750, June, 2007.
Serre, T., A. Oliva and T. Poggio. HUA Feedforward Architecture Accounts for Rapid CategorizationUH, Proceedings of the National Academy of Sciences (PNAS), Vol. 104, No. 15, 6424-6429, 2007.
Hung, C.P., G. Kreiman, T. Poggio and J.J. DiCarlo. HUFast Readout of Object Identity from Macaque Inferior Temporal CortexUH, Science, Vol. 310, 863-866, 2005.
Yeo, G., E. Van Nostrand, D. Holste, T. Poggio and C.B. Burge. Identification and Analysis of Alternative Splicing Events Conserved in Human and Mouse, Proceedings of the National Academy of Sciences (PNAS), 102, 8, 2850-2855, 2005.
Lampl, I., D. Ferster, T. Poggio and M. Riesenhuber. Intracellular Measurements of Spatial Integration and the MAX Operation in Complex Cells of the Cat Primary Visual Cortex, Journal of Neurophysiology, 92, 2704-2713, 2004.
Poggio, T. and E. Bizzi. HUGeneralization in Vision and Motor ControlUH, Nature, Vol. 431, 768-774, 2004.
Poggio, T., R. Rifkin, S. Mukherjee and P. Niyogi. HUGeneral Conditions for Predictivity in Learning TheoryUH, Nature, Vol. 428, 419-422, 2004.
Giese, M. and T. Poggio. Neural Mechanisms for the Recognition of Biological Movements, Nature Neuroscience Review, Vol. 4, 179-192, March 2003.
Poggio, T. and S. Smale. HUThe Mathematics of Learning: Dealing with DataUH, Notices of the American Mathematical Society (AMS), Vol. 50, No. 5, 537-544, 2003. (See journal issue at HUAMS NoticesUH.)
Ezzat, T., G. Geiger and T. Poggio. “HUTrainable Videorealistic Speech AnimationUH,” ACM SIGGRAPH 2002, San Antonio, TX, July 2002.
Freedman, D.J., M. Riesenhuber, T. Poggio and E.K. Miller. HUCategorical Representation of Visual Stimuli in the Primate Prefrontal CortexUH, Science, 291, 312-316, 2001.
Evgeniou, T., Pontil, M. and T. Poggio. HURegularization Networks and Support Vector MachinesUH, Advances in Computational Mathematics, 13, 1, 1-50, 2000.
Riesenhuber, M., and T. Poggio. Models of Object Recognition, Nature Neuroscience, 3 Supp., 1199-1204, 2000.
Riesenhuber, M. and T. Poggio. HUHierarchical Models of Object Recognition in CortexUH, Nature Neuroscience, 2, 1019-1025, 1999.
Sung, K.K. and T. Poggio. Example-Based Learning for View-Based Human Face Detection, IEEE PAMI, Vol. 20, No. 1, 39-51, 1998.
Beymer, D. and T. Poggio. HUImage Representation for Visual LearningUH, Science, 272, 1905-1909, 1996.
Sinha, P. and T. Poggio. Role of Learning in Three-dimensional Form Perception, Nature, Vol. 384, No. 6608, 460-463, 1996.
Logothetis, N.K, J. Pauls, and T. Poggio. HUShape Representation in the Inferior Temporal Cortex of MonkeysUH, Current Biology, Vol. 5, No. 5, 552-563, 1995.
Brunelli, R. and T. Poggio. HUFace Recognition: Features Versus TemplatesUH, IEEE PAMI, 15, 1042-1052, 1993.
Poggio, T., M. Fahle and S. Edelman. HUFast Perceptual Learning in Visual HyperacuityUH, Science, 256, 1018-1021, May 1992.
“A Theory of How the Brain Might Work,” (T. Poggio). In:Proceedings of Cold Spring Harbor Symposia on Quantitative Biology, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 4, 899-910, 1990.
Poggio, T. and F. Girosi. HUNetworks for Approximation and LearningUH, Proceedings of theIEEE (special issue: Neural Networks I: Theory and Modeling), Vol. 78, No. 9, 1481-1497, September 1990.
Edelman, S. and T. Poggio. HUA Network that Learns to Recognize 3D ObjectsUH, Nature, 343, 263-266, 1990.
Poggio, T. and F. Girosi. HURegularization Algorithms for Learning that are Equivalent to Multilayer NetworksUH, Science, 247, 978-982, 1990.
Bülthoff, H.H., J. Little and T. Poggio. A Parallel Algorithm for Real Time Computation of Optical Flow, Nature, 337, 549-553, 1989.
Bertero, M., T. Poggio and V. Torre. HUIll-posed Problems in Early VisionUH, Proceedings of the IEEE, 76, 869-889, 1988.
Poggio, T., E. Gamble and J. Little. Parallel Integration of Vision Modules,Science, 242, 436-440, 1988.
Voorhees, H. and T. Poggio. Computing Texture Boundaries from Images, Nature, 333, 364-367, 1988.
Hurlbert, A. and T. Poggio. Synthesizing a Color Algorithm from Examples, Science, 239, 482-485, 1988.
Poggio, T. and C. Koch. Synapses that Compute Motion, Scientific American, 256, 46-52, 1987.
Marroquin, J., S. Mitter and T. Poggio. Probabilistic Solution of Ill-posed Problems in Computational Vision, Journal of American Statistical Association, 82, 76-89, 1987
Hurlbert, A. and T. Poggio. Do Computers Need Attention?, Nature, 321, 651-652, 1986.
Poggio, T. Vision by Man and Machine, Scientific American, 250, 106-116, 1984.
Poggio, G. and T. Poggio. The Analysis of Stereopsis, Annual Review of Neuroscience, 7, 379-412, 1984.
Koch, C., T. Poggio and V. Torre. Nonlinear Interactions in a Dendritic Tree: Localization, Timing and Role in Information Processing, PNAS, 80, 2799-2802, 1983.
Poggio, T., V. Torre and C. Koch. HUComputational Vision and Regularization TheoryUH, Nature, 317, 314-319, 1985.
Nishihara, H.K. and T. Poggio. Hidden Cues in Random-line Stereograms, Nature, 300, 347-349, 1982.
Koch, C., T. Poggio and V. Torre. Retinal Ganglion Cells: A Functional Interpretation of Dendritic Morphology, Proceedings of the Royal Society London, 298, 227-264, 1982.
Fahle, M. and T. Poggio. Visual Hyperacuity: Spatiotemporal Interpolation in Human Vision,Proceedings of the Royal Society London B, 213, 451-477, 1981.
Poggio, T., W. Reichardt and W. Hausen. HUA Neuronal Circuitry for Relative Movement Discrimination by the Visual System of the FlUHy, Naturwissenschaften, 68, 9, 443-466, 1981.
Torre, V. and T. Poggio. HUA Synaptic Mechanism Possibly Underlying Directional Selectivity MotionUH, Proceedings of the Royal Society London B, 202, 409-416, 1978.
“From Understanding Computation to Understanding Neural Circuitry,” (D. Marr and T. Poggio). In: UNeuronal Mechanisms in Visual PerceptionU, E. Poppel, R. Held and J.E. Dowling (eds.), Neurosciences Res. Prog. Bull., 15, 470-488, 1977.
Marr, D., and T. Poggio. Cooperative Computation of Stereo Disparity,Science, 194, 283-287, 1976.
Wehrhahn, C. and T. Poggio. Real-time Delayed Tracking in Flies, Nature, 261, 43-44, 1976.
Reichardt, W. and T. Poggio. HUVisual Control of Orientation Behavior in the Fly. : A Quantitative AnalysisUH, Quarterly Review of Biophysics, 3, 311-375, 1976.
Marr, D., and T. Poggio. HUCooperative Computation of Stereo DisparityUH, Science, 194, 283-287, 1976.
Geiger, G. and T. Poggio. The Muller-Lyer Figure and the Fly, Science, 190, 479-480, 1975.