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Tomaso Poggio

Tomaso Poggio

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Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
145
Citations
119092
World Ranking
44
National Ranking
25

Tomaso Poggio publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tomaso Poggio sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 597 publications — 96th percentile

96% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Tomaso Poggio D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tomaso Poggio sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 145 D-Index — 100th percentile

100% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

  • Hierarchical models of object recognition in cortex

    Maximilian Riesenhuber;Tomaso Poggio

  • Face recognition: features versus templates

    R. Brunelli;T. Poggio

  • HMDB: A large video database for human motion recognition

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

  • A Computational Theory of Human Stereo Vision

    D. Marr;T. Poggio

  • Example-based learning for view-based human face detection

    K.-K. Sung;T. Poggio

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

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

  • Multiclass cancer diagnosis using tumor gene expression signatures

    Sridhar Ramaswamy;Pablo Tamayo;Ryan Rifkin;Sayan Mukherjee

  • Cooperative computation of stereo disparity

    D. Marr;T. Poggio

  • A general framework for object detection

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

  • Computational vision and regularization theory

    Tomaso Poggio;Vincent Torre;Christof Koch

  • Robust Object Recognition with Cortex-Like Mechanisms

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

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

    James M. Hutchinson;Andrew Lo;Tomaso Poggio

  • Regularization theory and neural networks architectures

    Federico Girosi;Michael Jones;Tomaso Poggio

  • A Trainable System for Object Detection

    Constantine Papageorgiou;Tomaso Poggio

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

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

  • Incremental and Decremental Support Vector Machine Learning

    Gert Cauwenberghs;Tomaso Poggio

  • Regularization Networks and Support Vector Machines

    Theodoros Evgeniou;Massimiliano Pontil;Tomaso A. Poggio

  • A network that learns to recognize three-dimensional objects.

    T. Poggio;S. Edelman

  • Feature Selection for SVMs

    Jason Weston;Sayan Mukherjee;Olivier Chapelle;Massimiliano Pontil

Frequent Co-Authors

Thomas Serre
Thomas Serre Brown University
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Maximilian Riesenhuber
Maximilian Riesenhuber Georgetown University Medical Center
Christof Koch
Christof Koch Allen Institute for Brain Science
Heinrich H. Bülthoff
Heinrich H. Bülthoff Max Planck Institute for Biological Cybernetics
Hrushikesh N. Mhaskar
Hrushikesh N. Mhaskar Claremont Graduate University
Massimiliano Pontil
Massimiliano Pontil Italian Institute of Technology
Thomas Vetter
Thomas Vetter University of Basel

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