World's Best Scientists 2026 revealed!
Jascha Sohl-Dickstein

Jascha Sohl-Dickstein

D-Index & Metrics

Computer Science

D-Index
56
Citations
15901
World Ranking
4003
National Ranking
1907

Jascha Sohl-Dickstein 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 Jascha Sohl-Dickstein 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 143 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Jascha Sohl-Dickstein 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 Jascha Sohl-Dickstein sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Jascha Sohl-Dickstein is affiliated with Google in the United States. Their research spans primarily the field of Computer Science, with a focus on subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing, and Computational Theory and Mathematics.

The scientist's work addresses several key topics such as Neural Networks and Applications, Machine Learning and Data Classification, Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, Topic Modeling, Natural Language Processing Techniques, and Stochastic Gradient Optimization Techniques.

Jascha Sohl-Dickstein has contributed extensively to academic literature, with notable recent papers including:

  • "Score-Based Generative Modeling through Stochastic Differential Equations," 2020, arXiv (Cornell University)
  • "Rethink reporting of evaluation results in AI," 2023, Science
  • "The large learning rate phase of deep learning: the catapult mechanism," 2020, arXiv (Cornell University)
  • "Levels of AGI for Operationalizing Progress on the Path to AGI," 2023, arXiv (Cornell University)
  • "Finite Versus Infinite Neural Networks: an Empirical Study," 2020, arXiv (Cornell University)

The most frequent publication venues for Sohl-Dickstein include arXiv (Cornell University), with 45 publications, Nature Communications, Science, Northern European Journal of Language Technology, and the Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.

Jascha Sohl-Dickstein has collaborated on numerous research projects with co-authors such as Luke Metz, Roman Novak, Jaehoon Lee, Jeffrey Pennington, and Samuel S. Schoenholz. The number of collaborations ranges from 8 to 15 papers per co-author.

Best Publications

  • Density estimation using Real NVP

    Laurent Dinh;Jascha Sohl-Dickstein;Samy Bengio

  • Deep Unsupervised Learning using Nonequilibrium Thermodynamics

    Jascha Sohl-Dickstein;Eric Weiss;Niru Maheswaranathan;Surya Ganguli

  • Score-Based Generative Modeling through Stochastic Differential Equations

    Yang Song;Jascha Sohl-Dickstein;Diederik P Kingma;Abhishek Kumar

  • Deep knowledge tracing

    Chris Piech;Jonathan Bassen;Jonathan Huang;Surya Ganguli

  • Unrolled Generative Adversarial Networks

    Luke Metz;Ben Poole;David Pfau;Jascha Sohl-Dickstein

  • Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

    Jaehoon Lee;Lechao Xiao;Samuel S. Schoenholz;Yasaman Bahri

  • Deep Neural Networks as Gaussian Processes

    Jaehoon Lee;Yasaman Bahri;Roman Novak;Samuel S. Schoenholz

  • On the expressive power of deep neural networks

    Maithra Raghu;Ben Poole;Jon M. Kleinberg;Surya Ganguli

  • Exponential expressivity in deep neural networks through transient chaos

    Ben Poole;Subhaneil Lahiri;Maithreyi Raghu;Jascha Sohl-Dickstein

  • Mars Exploration Rover Athena Panoramic Camera (Pancam) investigation

    J.F. Bell;S. W. Squyres;Kenneth E. Herkenhoff;J.N. Maki

  • SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

    Maithra Raghu;Justin Gilmer;Jason Yosinski;Jascha Sohl-Dickstein

  • Deep Knowledge Tracing

    Chris Piech;Jonathan Spencer;Jonathan Huang;Surya Ganguli

  • Sensitivity and Generalization in Neural Networks: an Empirical Study

    Roman Novak;Yasaman Bahri;Daniel A. Abolafia;Jeffrey Pennington

  • Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

    Gamaleldin Fathy Elsayed;Shreya Shankar;Brian Cheung;Nicolas Papernot

  • Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

    Roman Novak;Lechao Xiao;Jaehoon Lee;Yasaman Bahri

  • Statistical Mechanics of Deep Learning

    Yasaman Bahri;Jonathan Kadmon;Jeffrey Pennington;Sam S. Schoenholz

  • Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

    Lechao Xiao;Yasaman Bahri;Jascha Sohl-Dickstein;Samuel S. Schoenholz

  • REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

    George Tucker;Andriy Mnih;Chris J. Maddison;Dieterich Lawson

  • Measuring the Effects of Data Parallelism on Neural Network Training

    Christopher J. Shallue;Jaehoon Lee;Joseph M. Antognini;Jascha Sohl-Dickstein

  • Deep Information Propagation

    Samuel S. Schoenholz;Justin Gilmer;Surya Ganguli;Jascha Sohl-Dickstein

  • Neural Tangents: Fast and Easy Infinite Neural Networks in Python

    Roman Novak;Lechao Xiao;Jiri Hron;Jaehoon Lee

Frequent Co-Authors

Surya Ganguli
Surya Ganguli Stanford University
Ben Poole
Ben Poole Google (United States)
James F. Bell
James F. Bell Arizona State University
Bruno A. Olshausen
Bruno A. Olshausen University of California, Berkeley
Alexander G. Hayes
Alexander G. Hayes Cornell University
Jeffrey R. Johnson
Jeffrey R. Johnson Johns Hopkins University Applied Physics Laboratory
Ian Goodfellow
Ian Goodfellow Google (United States)
Walter Goetz
Walter Goetz University of Göttingen
Justin N. Maki
Justin N. Maki Jet Propulsion Lab

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