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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Cross-listing: Neurotree


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


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


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mark Stephen Alfano collaborator MIT (Neurotree)
Francis Harry Compton Crick collaborator MIT (Neurotree)
Robert Desimone collaborator MIT (Neurotree)
Manfred Fahle collaborator MIT (Neurotree)
David J. Freedman collaborator MIT (Neurotree)
Nikos K. Logothetis collaborator MIT (Neurotree)
Stephen Smale collaborator MIT
Vincent Torre collaborator MIT
Shimon Ullman collaborator MIT (Neurotree)
Alessandro Verri collaborator MIT (Neurotree)
Chou P. Hung collaborator 2002- MIT (Neurotree)
David L. Sheinberg collaborator 2008- MIT (Neurotree)
Dirk B. Walther collaborator 2000-2006 MIT (Neurotree)
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Poggio T, Banburski A, Liao Q. (2020) Theoretical issues in deep networks. Proceedings of the National Academy of Sciences of the United States of America
Poggio T, Liao Q, Banburski A. (2020) Complexity control by gradient descent in deep networks. Nature Communications. 11: 1027
Han Y, Roig G, Geiger G, et al. (2020) Scale and translation-invariance for novel objects in human vision. Scientific Reports. 10: 1411
Mhaskar H, Poggio TA. (2020) An analysis of training and generalization errors in shallow and deep networks. Neural Networks. 121: 229-241
Adler A, Araya-Polo M, Poggio T. (2019) Deep Recurrent Architectures for Seismic Tomography Arxiv: Geophysics. 2019: 1-5
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
Zhang J, Han Y, Poggio T, et al. (2019) Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance Journal of Vision. 19: 209-209
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
Poggio T, Liao Q. (2018) Theory II: Deep learning and optimization Bulletin of the Polish Academy of Sciences-Technical Sciences. 66: 775-787
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