World's Best Scientists 2026 revealed!
Phillip Isola

Phillip Isola

D-Index & Metrics

Computer Science

D-Index
48
Citations
75735
World Ranking
5988
National Ranking
2694

Phillip Isola 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 Phillip Isola 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: 104 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Phillip Isola 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 Phillip Isola 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Phillip Isola is affiliated with the Massachusetts Institute of Technology (MIT) in the United States. Their research primarily focuses on computer science, with a significant contribution to subfields such as computer vision and pattern recognition, artificial intelligence, aerospace engineering, biophysics, and cognitive neuroscience.

The scientist has published extensively, with a total of 162 publications in computer science and 88 specifically in computer vision and pattern recognition. Their work is also notable in areas including artificial intelligence and emerging interdisciplinary fields.

Phillip Isola's recent publications include the following:

  • What Makes for Good Views for Contrastive Learning?, 2020, arXiv (Cornell University)
  • Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere, 2020, arXiv (Cornell University)
  • iNeRF: Inverting Neural Radiance Fields for Pose Estimation, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Supervised Contrastive Learning, 2020, arXiv (Cornell University)

Their frequent publication venues include:

  • arXiv (Cornell University)
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2022 International Conference on Robotics and Automation (ICRA)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Phillip Isola collaborates frequently with several co-authors, including Tongzhou Wang, Lucy Chai, Antonio Torralba, Yonglong Tian, and Dilip Krishnan. These collaborations span a variety of topics and venues.

The scientist's main research topics encompass:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Image and Video Retrieval Techniques
  • Reinforcement Learning in Robotics
  • Advanced Vision and Imaging
  • Advanced Neural Network Applications

Best Publications

  • Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

    Jun-Yan Zhu;Taesung Park;Phillip Isola;Alexei A. Efros

  • Image-to-Image Translation with Conditional Adversarial Networks

    Phillip Isola;Jun-Yan Zhu;Tinghui Zhou;Alexei A. Efros

  • The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

    Richard Zhang;Phillip Isola;Phillip Isola;Alexei A. Efros;Eli Shechtman

  • Colorful Image Colorization

    Richard Yi Zhang;Phillip Isola;Alexei A. Efros

  • CyCADA: Cycle-Consistent Adversarial Domain Adaptation

    Judy Hoffman;Eric Tzeng;Taesung Park;Jun-Yan Zhu

  • Contrastive Multiview Coding

    Yonglong Tian;Dilip Krishnan;Phillip Isola

  • Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?

    Yonglong Tian;Yue Wang;Dilip Krishnan;Joshua B. Tenenbaum

  • What Makes a Visualization Memorable

    Michelle A. Borkin;Azalea A. Vo;Zoya Bylinskii;Phillip Isola

  • What Makes for Good Views for Contrastive Learning

    Yonglong Tian;Chen Sun;Ben Poole;Dilip Krishnan

  • Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

    Richard Zhang;Phillip Isola;Alexei A. Efros

  • Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

    Tongzhou Wang;Phillip Isola

  • Real-time user-guided image colorization with learned deep priors

    Richard Zhang;Jun-Yan Zhu;Phillip Isola;Xinyang Geng

  • Contrastive Representation Distillation

    Yonglong Tian;Dilip Krishnan;Phillip Isola

  • What makes an image memorable

    Phillip Isola;Jianxiong Xiao;Antonio Torralba;Aude Oliva

  • Supervised Contrastive Learning

    Prannay Khosla;Piotr Teterwak;Chen Wang;Aaron Sarna

  • What Makes a Photograph Memorable

    Phillip Isola;Jianxiong Xiao;Devi Parikh;Antonio Torralba

  • Visually Indicated Sounds

    Andrew Owens;Phillip Isola;Josh McDermott;Antonio Torralba

  • iNeRF: Inverting Neural Radiance Fields for Pose Estimation

    Lin Yen-Chen;Pete Florence;Jonathan T. Barron;Alberto Rodriguez

  • What Makes Fake Images Detectable? Understanding Properties that Generalize

    Lucy Chai;David Bau;Ser-Nam Lim;Phillip Isola

  • Intrinsic and extrinsic effects on image memorability

    Zoya Bylinskii;Phillip Isola;Constance Bainbridge;Antonio Torralba

  • Combining self-supervised learning and imitation for vision-based rope manipulation

    Ashvin Nair;Dian Chen;Pulkit Agrawal;Phillip Isola

  • GANalyze: Toward visual definitions of cognitive image properties

    Lore Goetschalckx;Lore Goetschalckx;Alex Andonian;Aude Oliva;Phillip Isola

Frequent Co-Authors

Alexei A. Efros
Alexei A. Efros University of California, Berkeley
Dilip Krishnan
Dilip Krishnan Google (United States)
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University
Igor Mordatch
Igor Mordatch Google (United States)
Michal Irani
Michal Irani Weizmann Institute of Science
Chuang Gan
Chuang Gan University of Massachusetts Amherst

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