World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
17054
World Ranking
6999
National Ranking
3067

Thomas Serre 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 Thomas Serre 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: 187 publications — 41st percentile

41% of scientists in this discipline score the same or lower.

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

Thomas Serre 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 Thomas Serre 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Thomas Serre is a researcher affiliated with Brown University in the United States. Their work primarily spans the field of computer science, with a focus on artificial intelligence, computer vision and pattern recognition, cognitive neuroscience, biophysics, and ecology, evolution, behavior, and systematics.

The scientist's research topics include visual attention and saliency detection, cell image analysis techniques, neural dynamics and brain function, visual perception and processing mechanisms, explainable artificial intelligence (XAI), machine learning in materials science, and domain adaptation and few-shot learning.

Among the recent papers authored by Thomas Serre are:

  • "Beyond the feedforward sweep: feedback computations in the visual cortex" (2020), published in Annals of the New York Academy of Sciences
  • "Same-different conceptualization: a machine vision perspective" (2020), published in Current Opinion in Behavioral Sciences
  • "An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning" (2021), published in PhytoKeys
  • "How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks" (2022), published in the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • "Superhuman cell death detection with biomarker-optimized neural networks" (2021), published in Science Advances

Thomas Serre frequently collaborates with several coauthors including Drew Linsley, Thomas Fel, Rémi Cadène, Lakshmi Narasimhan Govindarajan, and Alekh Karkada Ashok.

Their publications appear regularly in various venues such as arXiv (Cornell University), Journal of Vision, bioRxiv (Cold Spring Harbor Laboratory), the 2022 Conference on Cognitive Computational Neuroscience, and the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Best Publications

  • HMDB: A large video database for human motion recognition

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

  • Robust Object Recognition with Cortex-Like Mechanisms

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

  • Object recognition with features inspired by visual cortex

    T. Serre;L. Wolf;T. Poggio

  • A feedforward architecture accounts for rapid categorization

    Thomas Serre;Aude Oliva;Tomaso Poggio

  • A Biologically Inspired System for Action Recognition

    H. Jhuang;T. Serre;L. Wolf;T. Poggio

  • The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities

    Hilde Kuehne;Ali Arslan;Thomas Serre

  • A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex

    T. Serre;M. Kouh;C. Cadieu;U. Knoblich

  • Reading the mind's eye: Decoding category information during mental imagery

    Leila Reddy;Leila Reddy;Leila Reddy;Naotsugu Tsuchiya;Naotsugu Tsuchiya;Thomas Serre;Thomas Serre

  • Hierarchical classification and feature reduction for fast face detection with support vector machines

    Bernd Heisele;Bernd Heisele;Thomas Serre;Sam Prentice;Tomaso A. Poggio

  • Automated home-cage behavioural phenotyping of mice

    Huei-Han Jhuang;Estibaliz Garrote;Xinlin Yu;Vinita Khilnani

  • A quantitative theory of immediate visual recognition.

    Thomas Serre;Gabriel Kreiman;Minjoon Kouh;Charles Cadieu

  • Component-based face detection

    B. Heiselet;T. Serre;M. Pontil;T. Poggio

  • A Component-based Framework for Face Detection and Identification

    Bernd Heisele;Thomas Serre;T. Poggio

  • What and where: a Bayesian inference theory of attention.

    Sharat Chikkerur;Thomas Serre;Cheston Tan;Tomaso Poggio

  • Deep Learning: The Good, the Bad, and the Ugly.

    Thomas Serre

  • Object decoding with attention in inferior temporal cortex

    Ying Zhang;Ethan M. Meyers;Narcisse Pascal Bichot;Thomas R. Serre

  • An end-to-end generative framework for video segmentation and recognition

    Hilde Kuehne;Juergen Gall;Thomas Serre

  • Categorization by Learning and Combining Object Parts

    Bernd Heisele;Thomas Serre;Massimiliano Pontil;Thomas Vetter

  • Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex

    Thomas Serre;Maximilian Riesenhuber

  • Computer vision cracks the leaf code

    Peter Wilf;Shengping Zhang;Shengping Zhang;Sharat Chikkerur;Stefan A. Little;Stefan A. Little

  • Learning long-range spatial dependencies with horizontal gated-recurrent units

    Drew Linsley;Junkyung Kim;Vijay Veerabadran;Thomas Serre

Frequent Co-Authors

Gabriel Kreiman
Gabriel Kreiman Harvard University
Rufin VanRullen
Rufin VanRullen Centre national de la recherche scientifique, CNRS
Lior Wolf
Lior Wolf Tel Aviv University
Martin A. Giese
Martin A. Giese University of Tübingen
Maximilian Riesenhuber
Maximilian Riesenhuber Georgetown University Medical Center
Lisa M. Saksida
Lisa M. Saksida University of Western Ontario
Timothy J. Bussey
Timothy J. Bussey University of Western Ontario
Michael P. Coleman
Michael P. Coleman University of Cambridge
Joseph R. Madsen
Joseph R. Madsen Boston Children's Hospital

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