World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
31153
World Ranking
9456
National Ranking
590

Tero Karras 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 Tero Karras 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: 84 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

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

Tero Karras 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 Tero Karras 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

Tero Karras is a researcher affiliated with Nvidia in the United Kingdom. Their academic work primarily spans the field of Computer Science, with a focus on computer vision and computer graphics. The subfields covered in their publications include Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mechanics, Radiology, Nuclear Medicine and Imaging, and General Health Professions.

The research topics associated with Tero Karras encompass:

  • Generative Adversarial Networks and Image Synthesis
  • Advanced Vision and Imaging
  • Computer Graphics and Visualization Techniques
  • 3D Shape Modeling and Analysis
  • Digital Media Forensic Detection
  • Advanced Image Processing Techniques
  • Human Pose and Action Recognition

Tero Karras has contributed to multiple publication venues throughout their career. Frequent venues include:

  • arXiv (Cornell University)
  • Aaltodoc (Aalto University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • ACM Transactions on Graphics

Their recent publications reflect ongoing work in generative models and computer vision:

  • "Training Generative Adversarial Networks with Limited Data," 2020, arXiv (Cornell University)
  • "Efficient Geometry-aware 3D Generative Adversarial Networks," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Alias-Free Generative Adversarial Networks," 2021, arXiv (Cornell University)
  • "Elucidating the Design Space of Diffusion-Based Generative Models," 2022, arXiv (Cornell University)
  • "eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers," 2022, arXiv (Cornell University)

Tero Karras has collaborated frequently with several co-authors, including:

  • Timo Aila
  • Miika Aittala
  • Samuli Laine
  • Jaakko Lehtinen
  • Janne Hellsten

Best Publications

  • A Style-Based Generator Architecture for Generative Adversarial Networks

    Tero Karras;Samuli Laine;Timo Aila

  • Analyzing and Improving the Image Quality of StyleGAN

    Tero Karras;Samuli Laine;Miika Aittala;Janne Hellsten

  • Progressive Growing of GANs for Improved Quality, Stability, and Variation

    Tero Karras;Timo Aila;Samuli Laine;Jaakko Lehtinen

  • Efficient Geometry-aware 3D Generative Adversarial Networks

    Unknown

  • Pruning Convolutional Neural Networks for Resource Efficient Inference

    Pavlo Molchanov;Stephen Tyree;Tero Karras;Timo Aila

  • A Style-Based Generator Architecture for Generative Adversarial Networks

    Tero Karras;Samuli Laine;Timo Aila

  • Noise2Noise: Learning image restoration without clean data

    Jaakko Lehtinen;Jaakko Lehtinen;Jacob Munkberg;Jon Hasselgren;Samuli Laine

  • Training Generative Adversarial Networks with Limited Data

    Tero Karras;Miika Aittala;Janne Hellsten;Samuli Laine

  • Elucidating the Design Space of Diffusion-Based Generative Models

    Unknown

  • Alias-Free Generative Adversarial Networks

    Tero Karras;Miika Aittala;Samuli Laine;Erik Härkönen

  • Few-Shot Unsupervised Image-to-Image Translation

    Ming-Yu Liu;Xun Huang;Arun Mallya;Tero Karras

  • Audio-driven facial animation by joint end-to-end learning of pose and emotion

    Tero Karras;Timo Aila;Samuli Laine;Antti Herva

  • Efficient Sparse Voxel Octrees

    S Laine;T Karras

  • Improved Precision and Recall Metric for Assessing Generative Models

    Tuomas Kynkäänniemi;Tero Karras;Samuli Laine;Jaakko Lehtinen

  • Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning.

    Pavlo Molchanov;Stephen Tyree;Tero Karras;Timo Aila

  • Modular primitives for high-performance differentiable rendering

    Samuli Laine;Janne Hellsten;Tero Karras;Yeongho Seol

  • Maximizing parallelism in the construction of BVHs, octrees, and k-d trees

    Tero Karras

  • Fast parallel construction of high-quality bounding volume hierarchies

    Tero Karras;Timo Aila

  • Efficient sparse voxel octrees

    Samuli Laine;Tero Karras

  • High-Quality Self-Supervised Deep Image Denoising

    Samuli Laine;Tero Karras;Jaakko Lehtinen;Timo Aila

  • Megakernels considered harmful: wavefront path tracing on GPUs

    Samuli Laine;Tero Karras;Timo Aila

  • Architecture considerations for tracing incoherent rays

    Timo Aila;Tero Karras

Frequent Co-Authors

Samuli Laine
Samuli Laine Nvidia (United States)
Timo Aila
Timo Aila Aalto University
Jaakko Lehtinen
Jaakko Lehtinen Aalto University
Jan Kautz
Jan Kautz Nvidia (United States)
Ming-Yu Liu
Ming-Yu Liu Nvidia (United States)
David Luebke
David Luebke Nvidia (United States)
Alexander Keller
Alexander Keller Nvidia (United States)
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Animesh Garg
Animesh Garg University of Toronto
Hao Li
Hao Li University of California, Berkeley

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