2026 Tomaso Poggio: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	145	44	41	25	23	597	119092

Research.com Recognitions

2026 - Research.com Computer Science in United States Leader Award
2025 - Research.com Computer Science in United States Leader Award
2023 - Research.com Computer Science in United States Leader Award
2022 - Research.com Computer Science in United States Leader Award
2014 - Swartz Prize for Theoretical and Computational Neuroscience
2009 - Fellow of the American Association for the Advancement of Science (AAAS)
1997 - Fellow of the American Academy of Arts and Sciences
1990 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI)

Overview

Tomaso Poggio is a researcher affiliated with MIT in the United States, specializing in computer science with a focus on artificial intelligence, cognitive neuroscience, and computer vision. Their work spans various subfields including computational mechanics and electrical engineering.

The research topics covered by Poggio include:

Sparse and Compressive Sensing Techniques
Neural Networks and Applications
Stochastic Gradient Optimization Techniques
Face Recognition and Perception
Machine Learning and Extreme Learning Machines (ELM)
Neural dynamics and brain function
Face and Expression Recognition

Poggio's contributions can be seen in a number of publications, with recent papers including:

"Theoretical issues in deep networks," 2020, Proceedings of the National Academy of Sciences
"Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows," 2021, IEEE Signal Processing Magazine
"Scale and translation-invariance for novel objects in human vision," 2020, Scientific Reports
"Complexity control by gradient descent in deep networks," 2020, Nature Communications
"Representation Learning in Sensory Cortex: A Theory," 2022, IEEE Access

The venues where Tomaso Poggio has frequently published include arXiv (Cornell University), Journal of Vision, Proceedings of the National Academy of Sciences, IEEE Signal Processing Magazine, and Scientific Reports.

Throughout their career, Poggio has collaborated frequently with the following co-authors:

Andrzej Banburski
Qianli Liao
Liu Ziyin
Tomer Galanti
Akshay Rangamani

Awards received by Poggio encompass prestigious recognitions such as:

Swartz Prize for Theoretical and Computational Neuroscience, 2014
Fellow of the American Association for the Advancement of Science (AAAS), 2009
Fellow of the American Academy of Arts and Sciences, 1997
Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 1990

Best Publications

Networks for approximation and learning

T. Poggio;F. Girosi

4866
Citations
Hierarchical models of object recognition in cortex

Maximilian Riesenhuber;Tomaso Poggio

4375
Citations
Face recognition: features versus templates

R. Brunelli;T. Poggio

4363
Citations
HMDB: A large video database for human motion recognition

H. Kuehne;H. Jhuang;E. Garrote;T. Poggio

4178
Citations
A Computational Theory of Human Stereo Vision

D. Marr;T. Poggio

3102
Citations
Example-based learning for view-based human face detection

K.-K. Sung;T. Poggio

3020
Citations
Prediction of central nervous system embryonal tumour outcome based on gene expression

Scott L. Pomeroy;Pablo Tamayo;Michelle Gaasenbeek;Lisa M. Sturla

2923
Citations
Multiclass cancer diagnosis using tumor gene expression signatures

Sridhar Ramaswamy;Pablo Tamayo;Ryan Rifkin;Sayan Mukherjee

2659
Citations
Cooperative computation of stereo disparity

D. Marr;T. Poggio

2470
Citations
A general framework for object detection

C.P. Papageorgiou;M. Oren;T. Poggio

2344
Citations
Computational vision and regularization theory

Tomaso Poggio;Vincent Torre;Christof Koch

2270
Citations
Robust Object Recognition with Cortex-Like Mechanisms

T. Serre;L. Wolf;S. Bileschi;M. Riesenhuber

2181
Citations
A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

James M. Hutchinson;Andrew Lo;Tomaso Poggio

2104
Citations
Regularization theory and neural networks architectures

Federico Girosi;Michael Jones;Tomaso Poggio

2008
Citations
A Trainable System for Object Detection

Constantine Papageorgiou;Tomaso Poggio

1933
Citations
Comparing support vector machines with Gaussian kernels to radial basis function classifiers

B. Scholkopf;Kah-Kay Sung;C.J.C. Burges;F. Girosi

1876
Citations
Incremental and Decremental Support Vector Machine Learning

Gert Cauwenberghs;Tomaso Poggio

1749
Citations
Regularization Networks and Support Vector Machines

Theodoros Evgeniou;Massimiliano Pontil;Tomaso A. Poggio

1659
Citations
A network that learns to recognize three-dimensional objects.

T. Poggio;S. Edelman

1533
Citations
Feature Selection for SVMs

Jason Weston;Sayan Mukherjee;Olivier Chapelle;Massimiliano Pontil

1498
Citations

Frequent Co-Authors

Lorenzo Rosasco MIT

Thomas Serre Brown University

Joel Z. Leibo DeepMind (United Kingdom)

Maximilian Riesenhuber Georgetown University Medical Center

Christof Koch Allen Institute for Brain Science

Heinrich H. Bülthoff Max Planck Institute for Biological Cybernetics

Hrushikesh N. Mhaskar Claremont Graduate University

Massimiliano Pontil Italian Institute of Technology

Thomas Vetter University of Basel

Michael Jones Mitsubishi Electric (United States)

External Links

Wikipedia of Tomaso Poggio Personal website of Tomaso Poggio Google Scholar page

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