World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
9270
World Ranking
12388
National Ranking
5020

Mario Lucic 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 Mario Lucic 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: 88 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Mario Lucic 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 Mario Lucic 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Mario Lucic is affiliated with Google in the United States and has a research focus primarily in Computer Science. Within this field, their work is concentrated on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Aerospace Engineering, and Signal Processing.

The scientist's research covers a range of topics. Notable areas include Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Multimodal Machine Learning Applications, Advanced Vision and Imaging, Adversarial Robustness in Machine Learning, Advanced Image and Video Retrieval Techniques, and Human Pose and Action Recognition.

Mario Lucic has authored many publications across distinguished venues. Frequent publication platforms include:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • ACM Transactions on Graphics
  • Proceedings of the AAAI Conference on Artificial Intelligence

Among their recent papers are:

  • MLP-Mixer: An all-MLP Architecture for Vision (2021, arXiv (Cornell University))
  • Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, arXiv (Cornell University))
  • Scaling Vision Transformers to 22 Billion Parameters (2023, arXiv (Cornell University))
  • Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations (2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
  • ViViT: A Video Vision Transformer (2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV))

Their research collaborations include frequent co-authors such as Neil Houlsby, Josip Djolonga, Anurag Arnab, Mostafa Dehghani, and Daniel Duckworth.

Best Publications

  • MLP-Mixer: An all-MLP Architecture for Vision

    Ilya Tolstikhin;Neil Houlsby;Alexander Kolesnikov;Lucas Beyer

  • MLP-Mixer: An all-MLP Architecture for Vision

    Ilya Tolstikhin;Neil Houlsby;Alexander Kolesnikov;Lucas Beyer

  • Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

    Francesco Locatello;Stefan Bauer;Mario Lučić;Gunnar Rätsch

  • Are GANs Created Equal? A Large-Scale Study

    Mario Lucic;Karol Kurach;Marcin Michalski;Sylvain Gelly

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning

    Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam

  • Recent Advances in Autoencoder-Based Representation Learning

    Michael Tschannen;Olivier Frederic Bachem;Mario Lučić

  • Assessing Generative Models via Precision and Recall

    Mehdi S. M. Sajjadi;Olivier Bachem;Mario Lucic;Olivier Bousquet

  • Self-Supervised GANs via Auxiliary Rotation Loss

    Ting Chen;Xiaohua Zhai;Marvin Ritter;Mario Lucic

  • Scaling Vision Transformers to 22 Billion Parameters

    Unknown

  • On Mutual Information Maximization for Representation Learning

    Michael Tschannen;Josip Djolonga;Paul K. Rubenstein;Sylvain Gelly

  • On Mutual Information Maximization for Representation Learning

    Michael Tschannen;Josip Djolonga;Paul K. Rubenstein;Sylvain Gelly

  • Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

    Francesco Locatello;Stefan Bauer;Mario Lucic;Gunnar Rätsch

  • A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

    Xiaohua Zhai;Joan Puigcerver;Alexander Kolesnikov;Pierre Ruyssen

  • Practical Coreset Constructions for Machine Learning

    Olivier Bachem;Mario Lucic;Andreas Krause

  • Approximate k-means++ in sublinear time

    Olivier Bachem;Mario Lucic;S. Hamed Hassani;Andreas Krause

  • Fast and Provably Good Seedings for k-Means

    Olivier Bachem;Mario Lucic;Hamed Hassani;Andreas Krause

  • The GAN Landscape: Losses, Architectures, Regularization, and Normalization

    Karol Kurach;Mario Lucic;Xiaohua Zhai;Marcin Michalski

  • High-Fidelity Image Generation With Fewer Labels

    Mario Lucic;Michael Tschannen;Marvin Ritter;Xiaohua Zhai

  • A Large-Scale Study on Regularization and Normalization in GANs

    Karol Kurach;Mario Lučić;Xiaohua Zhai;Marcin Michalski

  • On Self Modulation for Generative Adversarial Networks

    Ting Chen;Mario Lucic;Neil Houlsby;Sylvain Gelly

  • Scalable k -Means Clustering via Lightweight Coresets

    Olivier Bachem;Mario Lucic;Andreas Krause

  • Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations.

    Mehdi S. M. Sajjadi;Henning Meyer;Etienne Pot;Urs Bergmann

  • ViViT: A Video Vision Transformer

    Anurag Arnab;Mostafa Dehghani;Georg Heigold;Chen Sun

  • Coresets for Nonparametric Estimation - the Case of DP-Means

    Olivier Bachem;Mario Lucic;Andreas Krause

  • The Visual Task Adaptation Benchmark

    Xiaohua Zhai;Joan Puigcerver;Alexander Kolesnikov;Pierre Ruyssen

Frequent Co-Authors

Sylvain Gelly
Sylvain Gelly Google (United States)
Andreas Krause
Andreas Krause ETH Zurich
Xiaohua Zhai
Xiaohua Zhai Google (United States)
Olivier Bousquet
Olivier Bousquet Google (United States)
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Alexey Dosovitskiy
Alexey Dosovitskiy Google (United States)
Marco Cuturi
Marco Cuturi École Nationale de la Statistique et de l'Administration Économique

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens doors to a variety of related fields and flexible study options. Many students now opt for fully online degrees, providing greater accessibility and convenience, especially for those balancing work or family commitments.

If you’re looking for accelerated learning, a 1 year computer science degree online can help you quickly gain critical tech skills and enter the workforce faster. Those interested in engineering can pursue an environmental engineering bachelor's degree online that focuses on sustainability and the growing “green” job market.

For students concerned with affordability, consider the cheapest mechanical engineering degree online, which offers robust technical training without the steep cost. Lastly, those passionate about discovery and research may find a bachelor of science in physics online to be a rewarding choice with broad career prospects.

These pathways demonstrate the diversity of online STEM education in the USA, letting you tailor your learning journey to your goals, schedule, and budget.

Best Scientists Citing Mario Lucic

Trending Scientists

Recently Published Articles