World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
8727
World Ranking
5937
National Ranking
93

Raja Giryes 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 Raja Giryes 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: 201 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Raja Giryes 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 Raja Giryes 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Raja Giryes is a researcher affiliated with Tel Aviv University in Israel, specializing in the field of Computer Science. Their work encompasses a range of subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Radiology, Nuclear Medicine and Imaging, and Computer Graphics and Computer-Aided Design.

Their research contributions cover multiple topics within these domains, focusing extensively on Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, 3D Shape Modeling and Analysis, Computer Graphics and Visualization Techniques, Advanced Vision and Imaging, Advanced Neural Network Applications, and Generative Adversarial Networks and Image Synthesis.

Raja Giryes has published papers in several notable venues, frequently appearing in:

  • arXiv (Cornell University)
  • IEEE Open Journal of Signal Processing
  • ACM Transactions on Graphics
  • Computer Graphics Forum
  • IEEE Signal Processing Magazine

Some recent publications by Raja Giryes include:

  • "Autoencoders", 2020, arXiv (Cornell University)
  • "Baby steps towards few-shot learning with multiple semantics", 2022, Pattern Recognition Letters
  • "Orienting point clouds with dipole propagation", 2021, ACM Transactions on Graphics
  • "SPAGHETTI", 2022, ACM Transactions on Graphics
  • "AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder", 2023, arXiv (Cornell University)

The researcher has collaborated frequently with a number of coauthors including Eli Schwartz, Daniel Cohen-Or, Leonid Karlinsky, Rogério Feris, and Sivan Doveh.

Best Publications

  • MeshCNN: a network with an edge

    Rana Hanocka;Amir Hertz;Noa Fish;Raja Giryes

  • Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures

    Unknown

  • RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection

    Leonid Karlinsky;Joseph Shtok;Sivan Harary;Eli Schwartz

  • Point2Mesh: a self-prior for deformable meshes

    Rana Hanocka;Gal Metzer;Raja Giryes;Daniel Cohen-Or

  • Robust Large Margin Deep Neural Networks

    Jure Sokolic;Raja Giryes;Guillermo Sapiro;Miguel R. D. Rodrigues

  • DeepISP: Toward Learning an End-to-End Image Processing Pipeline

    Eli Schwartz;Raja Giryes;Alex M. Bronstein

  • TEXTure: Text-Guided Texturing of 3D Shapes

    Unknown

  • Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?

    Raja Giryes;Guillermo Sapiro;Alex M. Bronstein

  • Image Restoration by Iterative Denoising and Backward Projections

    Tom Tirer;Raja Giryes

  • Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization

    Daniel Jakubovitz;Raja Giryes

  • Delta-encoder: an effective sample synthesis method for few-shot object recognition

    Eli Schwartz;Leonid Karlinsky;Joseph Shtok;Sivan Harary

  • ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning

    Rana Hanocka;Noa Fish;Zhenhua Wang;Raja Giryes

  • Generalization Error in Deep Learning

    Daniel Jakubovitz;Raja Giryes;Miguel R. D. Rodrigues

  • The projected GSURE for automatic parameter tuning in iterative shrinkage methods

    Raja Giryes;Michael Elad;Yonina C. Eldar

  • UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks

    Chaim Baskin;Eli Schwartz;Evgenii Zheltonozhskii;Natan Liss

  • Mathematics of Deep Learning

    Rene Vidal;Joan Bruna;Raja Giryes;Stefano Soatto

  • Poisson inverse problems by the Plug-and-Play scheme

    Arie Rond;Raja Giryes;Michael Elad

  • Depth Estimation From a Single Image Using Deep Learned Phase Coded Mask

    Harel Haim;Shay Elmalem;Raja Giryes;Alex M. Bronstein

  • Greedy-like algorithms for the cosparse analysis model

    Raja Giryes;Sangnam Nam;Michael Elad;Rémi Gribonval

  • Delta-encoder: an effective sample synthesis method for few-shot object recognition

    Eli Schwartz;Leonid Karlinsky;Joseph Shtok;Sivan Harary

  • TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.

    Moran Rubin;Omer Stein;Nir A. Turko;Yoav Nygate

  • Learned Convolutional Sparse Coding

    Hillel Sreter;Raja Giryes

  • Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors

    Gilad Cohen;Guillermo Sapiro;Raja Giryes

  • Margin Preservation of Deep Neural Networks.

    Jure Sokolic;Raja Giryes;Guillermo Sapiro;Miguel R. D. Rodrigues

Frequent Co-Authors

Alexander M. Bronstein
Alexander M. Bronstein Technion – Israel Institute of Technology
Michael Elad
Michael Elad Technion – Israel Institute of Technology
Daniel Cohen-Or
Daniel Cohen-Or Tel Aviv University
Rogerio Feris
Rogerio Feris IBM (United States)
Guillermo Sapiro
Guillermo Sapiro Princeton University
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science
Miguel R. D. Rodrigues
Miguel R. D. Rodrigues University College London
Shai Avidan
Shai Avidan Tel Aviv University
Alfred M. Bruckstein
Alfred M. Bruckstein Technion – Israel Institute of Technology
David Mendlovic
David Mendlovic Tel Aviv University

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