World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
9427
World Ranking
5383
National Ranking
85

Daphna Weinshall 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 Daphna Weinshall 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: 200 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Daphna Weinshall 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 Daphna Weinshall 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Overview

Daphna Weinshall is affiliated with the Hebrew University of Jerusalem in Israel. Their research focuses primarily within the field of Computer Science, with specific interest in Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience, and Cultural Studies.

Their scholarly contributions include work on the following topics:

  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Data Classification
  • Neural Networks and Applications
  • Advanced Neural Network Applications
  • Multimodal Machine Learning Applications
  • Machine Learning and Algorithms
  • COVID-19 diagnosis using AI

Weinshall's publication record includes numerous papers primarily disseminated through arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, and the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

Selected recent papers are as follows:

  • Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets, 2022, arXiv (Cornell University)
  • Active Learning Through a Covering Lens, 2022, arXiv (Cornell University)
  • The Grammar-Learning Trajectories of Neural Language Models, 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • United We Stand: Using Epoch-Wise Agreement of Ensembles to Combat Overfit, 2024, Proceedings of the AAAI Conference on Artificial Intelligence
  • Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks, 2021, arXiv (Cornell University)

Their frequent collaborators include the following researchers:

  • Guy Hacohen
  • Uri Stern
  • Daniel Shwartz
  • Avihu Dekel
  • Leshem Choshen

Weinshall has published extensively in the domain of computer science with a total of 46 publications, out of which 32 focus on artificial intelligence and 12 on computer vision and pattern recognition. Their work also spans medical imaging fields and interdisciplinary areas such as cognitive neuroscience and cultural studies.

Best Publications

  • Learning a Mahalanobis Metric from Equivalence Constraints

    Aharon Bar-Hillel;Tomer Hertz;Noam Shental;Daphna Weinshall

  • Learning distance functions using equivalence relations

    Aharon Bar-Hillel;Tomer Hertz;Noam Shental;Daphna Weinshall

  • Computing Gaussian Mixture Models with EM Using Equivalence Constraints

    Noam Shental;Aharon Bar-hillel;Tomer Hertz;Daphna Weinshall

  • Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes

    Y. Gdalyahu;D. Weinshall

  • Adjustment Learning and Relevant Component Analysis

    Noam Shental;Tomer Hertz;Daphna Weinshall;Misha Pavel

  • Classification with nonmetric distances: image retrieval and class representation

    D.W. Jacobs;D. Weinshall;Y. Gdalyahu

  • Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization

    Y. Gdalyahu;D. Weinshall;M. Werman

  • Cognitive authentication schemes safe against spyware

    D. Weinshall

  • On The Power of Curriculum Learning in Training Deep Networks.

    Guy Hacohen;Daphna Weinshall

  • Mosaicing new views: the Crossed-Slits projection

    A. Zomet;D. Feldman;S. Peleg;D. Weinshall

  • A self-organizing multiple-view representation of 3D objects

    Daphna Weinshall;Shimon Edelman;Heinrich H. Bülthoff

  • Computer vision-based wireless pointing system

    Antonio J. Colmenarez;Eric Cohen-Solal;Daphna Weinshall;Mi-Suen Lee

  • Passwords you'll never forget, but can't recall

    Daphna Weinshall;Scott Kirkpatrick

  • Mechanisms of generalization in perceptual learning.

    Zili Liu;Daphna Weinshall

  • Exploiting Object Hierarchy: Combining Models from Different Category Levels

    A. Zweig;D. Weinshall

  • Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models.

    Haim Dubossarsky;Daphna Weinshall;Eitan Grossman

  • Linear and incremental acquisition of invariant shape models from image sequences

    D. Weinshall;C. Tomasi

  • Linear and incremental acquisition of invariant shape models from image sequences

    D. Weinshall;C. Tomas

  • Similarity and affine invariant distances between 2D point sets

    M. Werman;D. Weinshall

  • Learning distance functions for image retrieval

    T. Hertz;A. Bar-Hillel;D. Weinshall

Frequent Co-Authors

Michael Werman
Michael Werman Hebrew University of Jerusalem
Shaul Hochstein
Shaul Hochstein Hebrew University of Jerusalem
Shimon Edelman
Shimon Edelman Cornell University
Heinrich H. Bülthoff
Heinrich H. Bülthoff Max Planck Institute for Biological Cybernetics
Shmuel Peleg
Shmuel Peleg Hebrew University of Jerusalem
Frank W. Ohl
Frank W. Ohl Leibniz Institute for Neurobiology
Miroslav Trajkovic
Miroslav Trajkovic Zebra Technologies (United States)
Misha Pavel
Misha Pavel Northeastern University
David W. Jacobs
David W. Jacobs University of Maryland, College Park

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