World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
17624
World Ranking
10435
National Ranking
4356

Dilip Krishnan 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 Dilip Krishnan 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: 71 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Dilip Krishnan 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 Dilip Krishnan 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Dilip Krishnan is affiliated with Google in the United States, where their research primarily focuses on computer science. Their work encompasses multiple subfields, including computer vision and pattern recognition, artificial intelligence, molecular biology, radiology, nuclear medicine, imaging, as well as electrical and electronic engineering.

Their research topics cover a range of areas in machine learning and computer vision with emphasis on:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Adversarial Robustness in Machine Learning
  • Computational Drug Discovery Methods

Several recent papers co-authored by Dilip Krishnan illustrate their research focus and collaboration with other scholars. These include:

  • What Makes for Good Views for Contrastive Learning?, 2020, arXiv (Cornell University)
  • Muse: Text-To-Image Generation via Masked Generative Transformers, 2023, arXiv (Cornell University)
  • Supervised Contrastive Learning, 2020, arXiv (Cornell University)
  • Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?, 2020, arXiv (Cornell University)
  • Simplified Transfer Learning for Chest Radiography Models Using Less Data, 2022, Radiology

Dilip Krishnan frequently collaborates with several researchers, with notable coauthors including Yonglong Tian, Phillip Isola, Huiwen Chang, Dina Katabi, and Aaron Sarna. Their publication record is principally concentrated in venues such as arXiv (Cornell University), with 20 publications, followed by contributions to Bioinformatics, Radiology, the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and SSRN Electronic Journal.

Best Publications

  • Contrastive Multiview Coding

    Yonglong Tian;Dilip Krishnan;Phillip Isola

  • Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

    Konstantinos Bousmalis;Nathan Silberman;David Dohan;Dumitru Erhan

  • Deconvolutional networks

    Matthew D. Zeiler;Dilip Krishnan;Graham W. Taylor;Rob Fergus

  • Fast Image Deconvolution using Hyper-Laplacian Priors

    Dilip Krishnan;Rob Fergus

  • Blind deconvolution using a normalized sparsity measure

    Dilip Krishnan;Terence Tay;Rob Fergus

  • Domain separation networks

    Konstantinos Bousmalis;George Trigeorgis;Nathan Silberman;Dilip Krishnan

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

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

  • What Makes for Good Views for Contrastive Learning

    Yonglong Tian;Chen Sun;Ben Poole;Dilip Krishnan

  • Contrastive Representation Distillation

    Yonglong Tian;Dilip Krishnan;Phillip Isola

  • Supervised Contrastive Learning

    Prannay Khosla;Piotr Teterwak;Chen Wang;Aaron Sarna

  • Restoring an Image Taken through a Window Covered with Dirt or Rain

    David Eigen;Dilip Krishnan;Rob Fergus

  • Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow

    Kanit Wongsuphasawat;Daniel Smilkov;James Wexler;Jimbo Wilson

  • Reflection removal using ghosting cues

    YiChang Shih;Dilip Krishnan;Fredo Durand;William T. Freeman

  • Fantastic Generalization Measures and Where to Find Them

    Yiding Jiang;Behnam Neyshabur;Hossein Mobahi;Dilip Krishnan

  • Crisp Boundary Detection Using Pointwise Mutual Information

    Phillip Isola;Daniel Zoran;Dilip Krishnan;Edward H. Adelson

  • Adversarial Robustness through Local Linearization

    Chongli Qin;James Martens;Sven Gowal;Dilip Krishnan

  • Learning Ordinal Relationships for Mid-Level Vision

    Daniel Zoran;Phillip Isola;Dilip Krishnan;William T. Freeman

  • Synthesizing Normalized Faces from Facial Identity Features

    Forrester Cole;David Belanger;Dilip Krishnan;Aaron Sarna

  • Dark flash photography

    Dilip Krishnan;Rob Fergus

  • Fantastic Generalization Measures and Where to Find Them

    Yiding Jiang;Behnam Neyshabur;Dilip Krishnan;Hossein Mobahi

Frequent Co-Authors

Rob Fergus
Rob Fergus New York University
Samy Bengio
Samy Bengio Apple (United States)
Ce Liu
Ce Liu Microsoft (United States)
Dumitru Erhan
Dumitru Erhan Google (United States)
Chen Sun
Chen Sun Google (United States)
Ben Poole
Ben Poole Google (United States)
Cordelia Schmid
Cordelia Schmid French Institute for Research in Computer Science and Automation - INRIA
Chuang Gan
Chuang Gan University of Massachusetts Amherst

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