World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
12977
World Ranking
9503
National Ranking
154

Daniel Soudry 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 Daniel Soudry 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: 95 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Daniel Soudry 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 Daniel Soudry 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

Daniel Soudry is affiliated with the Technion - Israel Institute of Technology in Israel. Their work primarily falls within the field of Computer Science, with a substantial focus on Artificial Intelligence and related subfields.

Their research contributions span several subfields, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Computational Mechanics

Within these subfields, the main topics covered by their publications include:

  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Stochastic Gradient Optimization Techniques
  • Adversarial Robustness in Machine Learning
  • Model Reduction and Neural Networks
  • Neural Networks and Applications
  • Machine Learning and Algorithms

Daniel Soudry's recent papers demonstrate ongoing engagement with neural network quantization, sparse training methods, and implicit bias in deep learning. Notable publications include:

  • Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming, 2020, arXiv (Cornell University)
  • Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks, 2021, arXiv (Cornell University)
  • Neural gradients are near-lognormal: improved quantized and sparse training, 2020, arXiv (Cornell University)
  • Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy, 2020, arXiv (Cornell University)
  • On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent, 2021, arXiv (Cornell University)

The majority of their publications have appeared in the venue arXiv (Cornell University), with 38 papers recorded there. Other publication venues include:

  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Semiconductor Science and Technology
  • Neural Computation

The researcher collaborates regularly with a group of co-authors, including:

  • Ron Banner
  • Nathan Srebro
  • Brian Chmiel
  • Itay Evron
  • Itay Hubara

Best Publications

  • Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

    Matthieu Courbariaux;Itay Hubara;Daniel Soudry;Ran El-Yaniv

  • Quantized neural networks: training neural networks with low precision weights and activations

    Itay Hubara;Matthieu Courbariaux;Daniel Soudry;Ran El-Yaniv

  • Binarized Neural Networks

    Itay Hubara;Matthieu Courbariaux;Daniel Soudry;Ran El-Yaniv

  • Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

    Eftychios A. Pnevmatikakis;Daniel Soudry;Yuanjun Gao;Timothy A. Machado

  • Train longer, generalize better: closing the generalization gap in large batch training of neural networks

    Elad Hoffer;Itay Hubara;Daniel Soudry

  • The implicit bias of gradient descent on separable data

    Daniel Soudry;Elad Hoffer;Mor Shpigel Nacson;Suriya Gunasekar

  • Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training

    Daniel Soudry;Dotan Di Castro;Asaf Gal;Avinoam Kolodny

  • Post training 4-bit quantization of convolutional networks for rapid-deployment

    Ron Banner;Yury Nahshan;Daniel Soudry

  • No bad local minima: Data independent training error guarantees for multilayer neural networks

    Daniel Soudry;Yair Carmon

  • Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights

    Daniel Soudry;Itay Hubara;Ron Meir

  • Scalable methods for 8-bit training of neural networks

    Ron Banner;Itay Hubara;Elad Hoffer;Daniel Soudry

  • Implicit Bias of Gradient Descent on Linear Convolutional Networks

    Suriya Gunasekar;Jason D. Lee;Daniel Soudry;Nathan Srebro

  • Characterizing Implicit Bias in Terms of Optimization Geometry

    Suriya Gunasekar;Jason D. Lee;Daniel Soudry;Nathan Srebro

  • Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

    Yedidyah Dordek;Daniel Soudry;Ron Meir;Dori Derdikman

  • Augment Your Batch: Improving Generalization Through Instance Repetition

    Elad Hoffer;Tal Ben-Nun;Itay Hubara;Niv Giladi

  • Post-training 4-bit quantization of convolution networks for rapid-deployment

    Ron Banner;Yury Nahshan;Elad Hoffer;Daniel Soudry

  • Norm matters: efficient and accurate normalization schemes in deep networks

    Elad Hoffer;Ron Banner;Itay Golan;Daniel Soudry

  • Kernel and Rich Regimes in Overparametrized Models

    Blake E. Woodworth;Suriya Gunasekar;Jason D. Lee;Edward Moroshko

  • Task Agnostic Continual Learning Using Online Variational Bayes

    Chen Zeno;Itay Golan;Elad Hoffer;Daniel Soudry

  • The Knowledge Within: Methods for Data-Free Model Compression

    Matan Haroush;Itay Hubara;Elad Hoffer;Daniel Soudry

  • A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case

    Greg Ongie;Rebecca Willett;Daniel Soudry;Nathan Srebro

  • The Implicit Bias of Gradient Descent on Separable Data

    Daniel Soudry;Elad Hoffer;Mor Shpigel Nacson;Nathan Srebro

  • The Implicit Bias of Gradient Descent on Separable Data

    Daniel Soudry;Elad Hoffer;Nathan Srebro

Frequent Co-Authors

Nathan Srebro
Nathan Srebro Toyota Technological Institute at Chicago
Ron Meir
Ron Meir Technion – Israel Institute of Technology
Jason D. Lee
Jason D. Lee Princeton University
Liam Paninski
Liam Paninski Columbia University
Shahar Kvatinsky
Shahar Kvatinsky Technion – Israel Institute of Technology
Ran El-Yaniv
Ran El-Yaniv Technion – Israel Institute of Technology
Rafael Yuste
Rafael Yuste Columbia University
Yoshua Bengio
Yoshua Bengio University of Montreal
Misha B. Ahrens
Misha B. Ahrens Howard Hughes Medical Institute

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