World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
77
Citations
37412
World Ranking
1234
National Ranking
653

Rahul Sukthankar 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 Rahul Sukthankar 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: 227 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Rahul Sukthankar 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 Rahul Sukthankar 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: 77 D-Index — 91st percentile

91% 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

  • 2018 - IEEE Fellow For contributions to video understanding

Overview

Rahul Sukthankar is affiliated with Google in the United States and has contributed extensively to the fields of computer science and engineering. Their work primarily focuses on computer vision and pattern recognition, with additional research in control and systems engineering and artificial intelligence.

Their main research topics include:

  • Human Pose and Action Recognition
  • Advanced Vision and Imaging
  • Human Motion and Animation
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Visual Attention and Saliency Detection
  • Multimodal Machine Learning Applications

Recent significant publications by Rahul Sukthankar cover various aspects of video understanding, pose estimation, and vision transformer techniques. Notable papers include:

  • Learning Video Representations from Textual Web Supervision, 2020, arXiv (Cornell University)
  • Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows, 2020, Lecture Notes in Computer Science
  • The End-of-End-to-End: A Video Understanding Pentathlon Challenge, 2020, arXiv (Cornell University)
  • Discrete Representations Strengthen Vision Transformer Robustness, 2021, arXiv (Cornell University)
  • HSPACE: Synthetic Parametric Humans Animated in Complex Environments, 2021, arXiv (Cornell University)

Frequent collaborators in their research include:

  • Andrei Zanfir
  • Eduard Gabriel Băzăvan
  • Cristian Sminchisescu
  • Marius Leordeanu
  • Dragoş Costea

Their publications are often found in venues such as:

  • arXiv (Cornell University)
  • Lecture Notes in Computer Science
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 International Conference on Robotics and Automation (ICRA)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

In recognition of their contributions to video understanding, Rahul Sukthankar was named an IEEE Fellow in 2018.

Best Publications

  • Large-Scale Video Classification with Convolutional Neural Networks

    Andrej Karpathy;George Toderici;Sanketh Shetty;Thomas Leung

  • PCA-SIFT: a more distinctive representation for local image descriptors

    Yan Ke;R. Sukthankar

  • Large-scale Video Classification with Convolutional Neural Networks

    Andrej Karpathy;George Toderici;Sanketh Shetty;Thomas Leung

  • AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions

    Chunhui Gu;Chen Sun;David A. Ross;Carl Vondrick

  • MatchNet: Unifying feature and metric learning for patch-based matching

    Xufeng Han;Thomas Leung;Yangqing Jia;Rahul Sukthankar

  • Efficient visual event detection using volumetric features

    Yan Ke;R. Sukthankar;M. Hebert

  • Rethinking the Faster R-CNN Architecture for Temporal Action Localization

    Yu-Wei Chao;Sudheendra Vijayanarasimhan;Bryan Seybold;David A. Ross

  • Cognitive Mapping and Planning for Visual Navigation

    Saurabh Gupta;James Davidson;Sergey Levine;Rahul Sukthankar

  • Violence detection in video using computer vision techniques

    Enrique Bermejo Nievas;Oscar Deniz Suarez;Gloria Bueno García;Rahul Sukthankar

  • The THUMOS challenge on action recognition for videos “in the wild”

    Haroon Idrees;Amir Roshan Zamir;Yu-Gang Jiang;Alex Gorban

  • Event Detection in Crowded Videos

    Yan Ke;R. Sukthankar;M. Hebert

  • Brief paper: Decentralized estimation and control of graph connectivity for mobile sensor networks

    P. Yang;R. A. Freeman;G. J. Gordon;K. M. Lynch

  • Robust adversarial reinforcement learning

    Lerrel Pinto;James Davidson;Rahul Sukthankar;Abhinav Gupta

  • SfM-Net: Learning of Structure and Motion from Video

    Sudheendra Vijayanarasimhan;Susanna Ricco;Cordelia Schmid;Rahul Sukthankar

  • Smarter presentations: exploiting homography in camera-projector systems

    R. Sukthankar;R.G. Stockton;M.D. Mullin

  • Beyond Skip Connections: Top-Down Modulation for Object Detection

    Abhinav Shrivastava;Rahul Sukthankar;Jitendra Malik;Abhinav Gupta

  • Variable Rate Image Compression with Recurrent Neural Networks

    George Toderici;Sean M. O'Malley;Sung Jin Hwang;Damien Vincent

  • An Integer Projected Fixed Point Method for Graph Matching and MAP Inference

    Marius Leordeanu;Martial Hebert;Rahul Sukthankar

  • An efficient parts-based near-duplicate and sub-image retrieval system

    Yan Ke;Rahul Sukthankar;Larry Huston

  • A framework for photo-quality assessment and enhancement based on visual aesthetics

    Subhabrata Bhattacharya;Rahul Sukthankar;Mubarak Shah

  • Cognitive Mapping and Planning for Visual Navigation

    Saurabh Gupta;Saurabh Gupta;Varun Tolani;James Davidson;Sergey Levine;Sergey Levine

Frequent Co-Authors

Martial Hebert
Martial Hebert Carnegie Mellon University
Shumeet Baluja
Shumeet Baluja Google (United States)
Chen Sun
Chen Sun Google (United States)
Mubarak Shah
Mubarak Shah University of Central Florida
Marius Leordeanu
Marius Leordeanu Romanian Academy
Rong Jin
Rong Jin Alibaba Group (China)
Jitendra Malik
Jitendra Malik University of California, Berkeley
Charles E. Thorpe
Charles E. Thorpe Carnegie Mellon University
Mahadev Satyanarayanan
Mahadev Satyanarayanan Carnegie Mellon University

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