World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
15374
World Ranking
13322
National Ranking
5329

Amir Roshan Zamir 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 Amir Roshan Zamir 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: 77 publications — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Amir Roshan Zamir 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 Amir Roshan Zamir 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

Amir Roshan Zamir is affiliated with Stanford University in the United States and has contributed extensively to the field of computer science, with a strong focus on computer vision and artificial intelligence. Their research spans a variety of specialized areas including advanced neural network applications, domain adaptation and few-shot learning, multimodal machine learning applications, advanced vision and imaging, robotics and sensor-based localization, adversarial robustness in machine learning, and anomaly detection techniques and applications.

Their publication record includes significant papers such as "UCF-101: A dataset of 101 human actions classes from videos in the wild" (2024, arXiv, Cornell University), "Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans" (2021, 2021 IEEE/CVF International Conference on Computer Vision), "CLIPasso" (2022, ACM Transactions on Graphics), "3D Common Corruptions and Data Augmentation" (2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition), and "Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation" (2020, arXiv, Cornell University).

Amir Roshan Zamir collaborates frequently with numerous colleagues, including Roman Bachmann, Andrei Atanov, Oğuzhan Fatih Kar, Teresa Yeo, and Alexander F. Sax. These collaborations reflect a network of joint research efforts across multiple projects and publications.

Their research contributions have been published primarily in notable venues with regular appearances in arXiv (Cornell University), which accounts for 22 publications. Other venues include the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), ACM Transactions on Graphics, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and Lecture Notes in Computer Science.

Their work focuses on several intersecting subfields of study within computer science: computer vision and pattern recognition, artificial intelligence, aerospace engineering, computer graphics and computer-aided design, and computational mechanics. These subfields correspond to the range of topics addressed in their body of work.

Best Publications

  • UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

    Khurram Soomro;Amir Roshan Zamir;Mubarak Shah

  • 3D Semantic Parsing of Large-Scale Indoor Spaces

    Iro Armeni;Ozan Sener;Amir R. Zamir;Helen Jiang

  • Structural-RNN: Deep Learning on Spatio-Temporal Graphs

    Ashesh Jain;Amir R. Zamir;Silvio Savarese;Ashutosh Saxena

  • Taskonomy: Disentangling Task Transfer Learning

    Amir R. Zamir;Alexander Sax;William Shen;Leonidas Guibas

  • Joint 2D-3D-Semantic Data for Indoor Scene Understanding

    Iro Armeni;Sasha Sax;Amir Roshan Zamir;Silvio Savarese

  • Gibson Env: Real-World Perception for Embodied Agents

    Fei Xia;Amir R. Zamir;Zhiyang He;Alexander Sax

  • On Evaluation of Embodied Navigation Agents

    Peter Anderson;Angel X. Chang;Devendra Singh Chaplot;Alexey Dosovitskiy

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

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

  • GMCP-Tracker: global multi-object tracking using generalized minimum clique graphs

    Amir Roshan Zamir;Afshin Dehghan;Mubarak Shah

  • Accurate image localization based on google maps street view

    Amir Roshan Zamir;Mubarak Shah

  • Action Recognition in Realistic Sports Videos

    Khurram Soomro;Amir R. Zamir

  • Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs

    Amir Roshan Zamir;Mubarak Shah

  • Which Tasks Should Be Learned Together in Multi-task Learning?

    Trevor Standley;Amir Zamir;Dawn Chen;Leonidas Guibas

  • 3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera

    Iro Armeni;Zhi-Yang He;Amir Zamir;Junyoung Gwak

  • CLIPasso

    Unknown

  • Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets From 3D Scans

    Ainaz Eftekhar;Alexander Sax;Jitendra Malik;Amir Zamir

  • MultiMAE: Multi-modal Multi-task Masked Autoencoders

    Unknown

  • Taskonomy: Disentangling Task Transfer Learning.

    Amir Roshan Zamir;Amir Roshan Zamir;Alexander Sax;William B. Shen;Leonidas J. Guibas

  • Feedback Networks

    Amir R. Zamir;Te-Lin Wu;Lin Sun;William B. Shen

  • Feedback Networks

    Unknown

  • Taskonomy: Disentangling Task Transfer Learning

    Amir Zamir;Amir Zamir;Alexander Sax;William Shen;Leonidas Guibas

  • Unsupervised Semantic Parsing of Video Collections

    Ozan Sener;Amir R. Zamir;Silvio Savarese;Ashutosh Saxena

  • Generic 3D Representation via Pose Estimation and Matching

    Amir Roshan Zamir;Tilman Wekel;Pulkit Agrawal;Colin Wei

  • Semantic Cross-View Matching

    Francesco Castaldo;Amir Zamir;Roland Angst;Francesco Palmieri

  • An Information-Theoretic Approach to Transferability in Task Transfer Learning

    Yajie Bao;Yang Li;Shao-Lun Huang;Lin Zhang

  • Robust Learning Through Cross-Task Consistency

    Amir Zamir;Alexander Sax;Teresa Yeo;Oğuzhan Kar

  • 3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera

    Iro Armeni;Zhi-Yang He;JunYoung Gwak;Amir R. Zamir

Frequent Co-Authors

Silvio Savarese
Silvio Savarese Stanford University
Jitendra Malik
Jitendra Malik University of California, Berkeley
Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Mubarak Shah
Mubarak Shah University of Central Florida
Ashutosh Saxena
Ashutosh Saxena Cornell University
Martin Fischer
Martin Fischer Stanford University
Luc Van Gool
Luc Van Gool Institute for Computer Science, Artificial Intelligence and Technology (INSAIT)
Richard Szeliski
Richard Szeliski University of Washington
Rahul Sukthankar
Rahul Sukthankar Google (United States)
Yuke Zhu
Yuke Zhu The University of Texas at Austin

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