World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
95436
World Ranking
5186
National Ranking
2379

Jonathon Shlens 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 Jonathon Shlens 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: 97 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Jonathon Shlens 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 Jonathon Shlens 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

Jonathon Shlens is affiliated with Google in the United States. Their research focuses primarily on computer science and engineering, with a particular emphasis on subfields such as computer vision and pattern recognition, artificial intelligence, and automotive engineering. Additional work involves building and construction as well as signal processing.

The scientist's research covers a range of topics that include:

  • Advanced Neural Network Applications
  • Autonomous Vehicle Technology and Safety
  • Domain Adaptation and Few-Shot Learning
  • Adversarial Robustness in Machine Learning
  • Traffic Prediction and Management Techniques
  • Video Surveillance and Tracking Methods
  • Multimodal Machine Learning Applications

Shlens has contributed to multiple publication venues, with a strong presence on arXiv (Cornell University) where they have 19 publications. Other venues where their work has appeared include:

  • Nature Communications
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Lecture notes in computer science
  • Neuron

Among recent papers authored or co-authored by Shlens are:

  • Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset, 2021, published in the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Revisiting ResNets: Improved Training and Scaling Strategies, 2021, arXiv (Cornell University)
  • Learning Data Augmentation Strategies for Object Detection, 2020, Lecture notes in computer science
  • Capabilities of Gemini Models in Medicine, 2024, arXiv (Cornell University)
  • Scene Transformer: A unified architecture for predicting multiple agent trajectories, 2021, arXiv (Cornell University)

Shlens has collaborated frequently with a set of co-authors, which include:

  • Benjamin Caine (11 joint publications)
  • Jiquan Ngiam (10 joint publications)
  • Vijay Vasudevan (8 joint publications)
  • Shuyang Cheng (6 joint publications)
  • Dragomir Anguelov (6 joint publications)

Best Publications

  • Rethinking the Inception Architecture for Computer Vision

    Christian Szegedy;Vincent Vanhoucke;Sergey Ioffe;Jon Shlens

  • Explaining and Harnessing Adversarial Examples

    Ian J. Goodfellow;Jonathon Shlens;Christian Szegedy

  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • Learning Transferable Architectures for Scalable Image Recognition

    Barret Zoph;Vijay Vasudevan;Jonathon Shlens;Quoc V. Le

  • Conditional image synthesis with auxiliary classifier GANs

    Augustus Odena;Christopher Olah;Jonathon Shlens

  • Scalability in Perception for Autonomous Driving: Waymo Open Dataset

    Pei Sun;Henrik Kretzschmar;Xerxes Dotiwalla;Aurelien Chouard

  • RandAugment: Practical Automated Data Augmentation with a Reduced Search Space

    Ekin Dogus Cubuk;Barret Zoph;Jon Shlens;Quoc Le

  • A Tutorial on Principal Component Analysis.

    Jonathon Shlens

  • Progressive Neural Architecture Search

    Chenxi Liu;Barret Zoph;Maxim Neumann;Jonathon Shlens

  • Spatio-temporal correlations and visual signalling in a complete neuronal population

    Jonathan William Pillow;Jonathon Shlens;Liam Paninski;Alexander Sher

  • Do Better ImageNet Models Transfer Better

    Simon Kornblith;Jonathon Shlens;Quoc V. Le

  • Bottleneck Transformers for Visual Recognition

    Aravind Srinivas;Tsung-Yi Lin;Niki Parmar;Jonathon Shlens

  • Attention Augmented Convolutional Networks

    Irwan Bello;Barret Zoph;Quoc Le;Ashish Vaswani

  • A Learned Representation For Artistic Style

    Vincent Dumoulin;Jonathon Shlens;Manjunath Kudlur

  • Zero-Shot Learning by Convex Combination of Semantic Embeddings

    Mohammad Norouzi;Tomas Mikolov;Samy Bengio;Yoram Singer

  • The structure of multi-neuron firing patterns in primate retina

    Jonathon Shlens;Greg D. Field;Jeffrey L. Gauthier;Matthew I. Grivich

  • YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

    Esteban Real;Jonathon Shlens;Stefano Mazzocchi;Xin Pan

  • Net2Net: Accelerating Learning via Knowledge Transfer

    Tianqi Chen;Ian Goodfellow;Jonathon Shlens

  • Adversarial Autoencoders

    Alireza Makhzani;Jonathon Shlens;Navdeep Jaitly;Ian Goodfellow

  • Learning Data Augmentation Strategies for Object Detection

    Barret Zoph;Ekin D. Cubuk;Golnaz Ghiasi;Tsung-Yi Lin

  • Stand-Alone Self-Attention in Vision Models

    Prajit Ramachandran;Niki Parmar;Ashish Vaswani;Irwan Bello

Frequent Co-Authors

Barret Zoph
Barret Zoph Google (United States)
E. J. Chichilnisky
E. J. Chichilnisky Stanford University
Ekin D. Cubuk
Ekin D. Cubuk Google (United States)
Vijay K. Vasudevan
Vijay K. Vasudevan Google (United States)
Alexander Sher
Alexander Sher University of California, Santa Cruz
Zhifeng Chen
Zhifeng Chen Google (United States)
Quoc V. Le
Quoc V. Le Google (United States)
Ian Goodfellow
Ian Goodfellow Google (United States)
Hartwig Adam
Hartwig Adam Google (United States)

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