World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
29706
World Ranking
5195
National Ranking
2384

Ming-Yu Liu 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 Ming-Yu Liu 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: 142 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Ming-Yu Liu 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 Ming-Yu Liu 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

Ming-Yu Liu is affiliated with Nvidia in the United States and has contributed extensively to the field of computer science, with a notable focus on computer vision and pattern recognition. Their body of work spans various subfields including artificial intelligence, computer networks and communications, computer graphics and computer-aided design, and information systems.

The scientist's recent publications reflect a broad engagement with topics such as generative adversarial networks and image synthesis, advanced vision and imaging, natural language processing techniques, multimodal machine learning applications, computer graphics and visualization techniques, handwritten text recognition techniques, and topic modeling.

  • eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers (2022) - arXiv (Cornell University)
  • Video-to-Video Synthesis (2025) - arXiv (Cornell University)
  • GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds (2021) - 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Lens aberration compensation in interference microscopy (2020) - Optics and Lasers in Engineering
  • Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation (2022) - ACM Transactions on Graphics

Their research has been predominantly disseminated through the following venues:

  • arXiv (Cornell University)
  • Neurocomputing
  • SSRN Electronic Journal
  • Zenodo (CERN European Organization for Nuclear Research)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Ming-Yu Liu frequently collaborates with several other researchers in the field. Among the most frequent co-authors are Ting-Chun Wang, Arun Mallya, Jan Kautz, Li Pan, and Shijun Liu.

  • Ting-Chun Wang
  • Arun Mallya
  • Jan Kautz
  • Li Pan
  • Shijun Liu

Their contributions to computer science include a substantial number of publications centered on computer vision and artificial intelligence topics, indicating an emphasis on both theoretical aspects and practical applications of advanced imaging, synthesis, and machine learning techniques.

Best Publications

  • High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

    Ting-Chun Wang;Ming-Yu Liu;Jun-Yan Zhu;Andrew Tao

  • Semantic Image Synthesis With Spatially-Adaptive Normalization

    Taesung Park;Ming-Yu Liu;Ting-Chun Wang;Jun-Yan Zhu

  • Multimodal Unsupervised Image-to-Image Translation

    Xun Huang;Ming-Yu Liu;Serge J. Belongie;Jan Kautz

  • PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

    Deqing Sun;Xiaodong Yang;Ming-Yu Liu;Jan Kautz

  • Unsupervised Image-to-Image Translation Networks

    Ming-Yu Liu;Thomas M. Breuel;Jan Kautz

  • Coupled Generative Adversarial Networks

    Ming-Yu Liu;Oncel Tuzel

  • Entropy rate superpixel segmentation

    Ming-Yu Liu;Oncel Tuzel;Srikumar Ramalingam;Rama Chellappa

  • Magic3D: High-Resolution Text-to-3D Content Creation

    Unknown

  • MoCoGAN: Decomposing Motion and Content for Video Generation

    Sergey Tulyakov;Ming-Yu Liu;Xiaodong Yang;Jan Kautz

  • Few-Shot Unsupervised Image-to-Image Translation

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

  • PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows

    Guandao Yang;Xun Huang;Zekun Hao;Ming-Yu Liu

  • Video-to-Video Synthesis

    Ting-Chun Wang;Ming-Yu Liu;Jun-Yan Zhu;Guilin Liu

  • CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

    Zheng Tang;Milind Naphade;Ming-Yu Liu;Xiaodong Yang

  • A Closed-Form Solution to Photorealistic Image Stylization

    Yijun Li;Ming Yu Liu;Xueting Li;Ming Hsuan Yang

  • One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing

    Ting-Chun Wang;Arun Mallya;Ming-Yu Liu

  • R-CNN for Small Object Detection

    Chenyi Chen;Ming-Yu Liu;Oncel Tuzel;Jianxiong Xiao

  • Tactics of Adversarial Attack on Deep Reinforcement Learning Agents.

    Yen-Chen Lin;Zhang-Wei Hong;Yuan-Hong Liao;Meng-Li Shih

  • CASENet: Deep Category-Aware Semantic Edge Detection

    Zhiding Yu;Chen Feng;Ming-Yu Liu;Srikumar Ramalingam

  • Superpixel sampling networks

    Varun Jampani;Deqing Sun;Ming Yu Liu;Ming Hsuan Yang

  • Joint Geodesic Upsampling of Depth Images

    Ming-Yu Liu;Oncel Tuzel;Yuichi Taguchi

  • Fast directional chamfer matching

    Ming-Yu Liu;Oncel Tuzel;Ashok Veeraraghavan;Rama Chellappa

  • Few-shot Video-to-Video Synthesis

    Ting-Chun Wang;Ming-Yu Liu;Andrew Tao;Guilin Liu

Frequent Co-Authors

Jan Kautz
Jan Kautz Nvidia (United States)
Oncel Tuzel
Oncel Tuzel Apple (United States)
Xiaodong Yang
Xiaodong Yang Nvidia (United Kingdom)
Ming-Hsuan Yang
Ming-Hsuan Yang University of California, Merced
Deqing Sun
Deqing Sun Google (United States)
Rama Chellappa
Rama Chellappa Johns Hopkins University
Min Sun
Min Sun National Tsing Hua University
Jinwei Gu
Jinwei Gu Chinese University of Hong Kong
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University

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