Tomaso A. Poggio

Massachusetts Institute of Technology, Cambridge, MA, United States 
"Tomaso Poggio"

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.

Prof. 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.

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Mean distance: 11.88 (cluster 29)
Cross-listing: MathTree


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Antonio Borsellino grad student 1970 Università degli Studi di Genova
 (PhD Dissertation: On Holographic Models of Memory)
David Marr research scientist 1971-1981 MIT
Werner Reichardt research scientist 1971-1981 MPI Tuebingen


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G. Rodrigo Sigala A. research assistant MIT-Picower Institute for Learning and Memory
Angela J. Yu research assistant 1998-2000 Brain and Cognitive Science, MIT
Nikon A. Rasumov research assistant 2007-2007 Brain and Cognitive Science, MIT
Luke Arend research assistant 2017-2018 MIT
Emanuela Bricolo grad student
Theodoros Evgeniou grad student MIT (MathTree)
Lyle J. GRAHAM grad student CNRS, France
Leyla Isik grad student
Pawan Sinha grad student MIT
Stanley Michael Bileschi grad student 2000- MIT
Christof Koch grad student 1982 Universität Tübingen
Anya Hurlbert grad student 1989 MIT
Thomas M. Breuel grad student 1986-1992 MIT
Partha Niyogi grad student 1995 MIT
Max Riesenhuber grad student 2000 MIT
Sayan Mukherjee grad student 2001 MIT
Gene W. Yeo grad student 2004 MIT
Charles F. Cadieu grad student 2004-2005 MIT
Alexander Rakhlin grad student 2006 MIT
Sanmay Das grad student 2001-2006 MIT
Thomas Serre grad student 2001-2006 MIT
Ethan Meyers grad student 2004-2010 MIT
Ulf Knoblich grad student 2003-2011 MIT
Cheston Tan grad student 2006-2012 MIT
Joel Z. Leibo grad student 2013 MIT
Heinrich H. Buelthoff post-doc MIT
Martin A. Giese post-doc Brain and Cognitive Science, MIT
Gabriel Kreiman post-doc MIT
Lorenzo Rosasco post-doc MIT
Amnon Shashua post-doc MIT (Computer Science Tree)
Alessandro Verri post-doc MIT
Daphna Weinshall post-doc MIT
Thomas Serre post-doc 2006- MIT
Arturo Deza post-doc 2020- MIT
Christof Koch post-doc 1982-1984 MIT
Andrew Parker post-doc 1984-1985 MIT
James J. Little post-doc 1985-1988 MIT (Computer Science Tree)
Hanspeter A. Mallot post-doc 1986-1988 MIT
Shimon Edelman post-doc 1990-1991 MIT
Davide Zoccolan post-doc 2003-2006 MIT
Sang Wan Lee post-doc 2010-2011 MIT
Tony Ezzat research scientist MIT
Minjoon Kouh research scientist 2001-2007 MIT


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mark Stephen Alfano collaborator MIT
Francis Harry Compton Crick collaborator MIT
Robert Desimone collaborator MIT
Manfred Fahle collaborator MIT
David J. Freedman collaborator MIT
Nikos K. Logothetis collaborator MIT
Stephen Smale collaborator MIT
Vincent Torre collaborator MIT
Shimon Ullman collaborator MIT
Alessandro Verri collaborator MIT
Chou P. Hung collaborator 2002- MIT
David L. Sheinberg collaborator 2008- MIT
Dirk B. Walther collaborator 2000-2006 MIT
BETA: Related publications


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Han Y, Roig G, Geiger G, et al. (2020) Scale and translation-invariance for novel objects in human vision. Scientific Reports. 10: 1411
Han Y, Roig G, Geiger G, et al. (2019) Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks Journal of Vision. 19
Anselmi F, Evangelopoulos G, Rosasco L, et al. (2019) Symmetry-adapted representation learning Pattern Recognition. 86: 201-208
Tacchetti A, Isik L, Poggio TA. (2018) Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science
Tacchetti A, Isik L, Poggio T. (2017) Invariant recognition drives neural representations of action sequences. Plos Computational Biology. 13: e1005859
Isik L, Tacchetti A, Poggio TA. (2017) A fast, invariant representation for human action in the visual system. Journal of Neurophysiology. jn.00642.2017
Roig G, Chen F, Boix X, et al. (2017) Eccentricity Dependent Deep Neural Networks for Modeling Human Vision Journal of Vision. 17: 808-808
Han Y, Roig G, Geiger G, et al. (2017) On the Human Visual System Invariance to Translation and Scale Journal of Vision. 17: 471-471
Poggio TA, Mhaskar H, Rosasco L, et al. (2017) Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review International Journal of Automation and Computing. 14: 503-519
Leibo JZ, Liao Q, Anselmi F, et al. (2016) View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology : Cb
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