World's Best Scientists 2026 revealed!
Song-Chun Zhu

Song-Chun Zhu

Award Badge
Computer Science
China
2026

D-Index & Metrics

Computer Science

D-Index
98
Citations
39484
World Ranking
405
National Ranking
52

Song-Chun Zhu 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 Song-Chun Zhu 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: 530 publications — 94th percentile

94% of scientists in this discipline score the same or lower.

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

Song-Chun Zhu 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 Song-Chun Zhu 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: 98 D-Index — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in China Leader Award
  • 2025 - Research.com Computer Science in China Leader Award
  • 2022 - Research.com Computer Science in China Leader Award

Overview

Song-Chun Zhu is affiliated with Peking University in China, contributing to the field of Computer Science with a focus on Artificial Intelligence and related subfields. Their research spans several areas including Computer Vision and Pattern Recognition, Control and Systems Engineering, Cognitive Neuroscience, and Aerospace Engineering.

Their publication record highlights a concentration in Artificial Intelligence, with notable exploration in topics such as Multimodal Machine Learning Applications, Topic Modeling, Natural Language Processing Techniques, Human Pose and Action Recognition, Domain Adaptation and Few-Shot Learning, Robot Manipulation and Learning, and Generative Adversarial Networks and Image Synthesis.

Frequent co-authors of Song-Chun Zhu include:

  • Yixin Zhu
  • Siyuan Huang
  • Ying Wu
  • Hangxin Liu
  • Ziyuan Jiao

The venues where Song-Chun Zhu most commonly publishes research are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Engineering
  • IEEE Robotics and Automation Letters

Recent papers authored or co-authored by Song-Chun Zhu include:

  • Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering (2022, arXiv (Cornell University))
  • Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds (2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV))
  • Cascaded Parsing of Human-Object Interaction Recognition (2021, IEEE Transactions on Pattern Analysis and Machine Intelligence)
  • Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, Engineering)
  • Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models (2023, arXiv (Cornell University))

Song-Chun Zhu's body of work is situated predominantly within computer science, emphasizing areas that intersect with machine learning, vision, and cognitive modeling techniques. The diversity of topics and frequent collaboration with multiple researchers underlines an interdisciplinary approach to artificial intelligence research.

Best Publications

  • Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation

    Song Chun Zhu;A. Yuille

  • Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation

    S.C. Zhu;T.S. Lee;A.L. Yuille

  • Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling

    Song Chun Zhu;Yingnian Wu;David Mumford

  • Visual interpretability for deep learning: a survey

    Quan-shi Zhang;Song-chun Zhu

  • Image parsing : Unifying segmentation, detection, and recognition

    Zhuowen Tu;Xiangrong Chen;Alan L. Yuille;Song Chun Zhu

  • Image segmentation by data-driven Markov chain Monte Carlo

    Zhuowen Tu;Song-Chun Zhu

  • Image parsing: unifying segmentation, detection, and recognition

    Zhuowen Tu;Xiangrong Chen;Yuille;Zhu

  • Interpretable Convolutional Neural Networks

    Quanshi Zhang;Ying Nian Wu;Song-Chun Zhu

  • On Advances in Statistical Modeling of Natural Images

    A. Srivastava;A. B. Lee;E. P. Simoncelli;S.-C. Zhu

  • A Stochastic Grammar of Images

    Song-Chun Zhu;David Mumford

  • Cross-View Action Modeling, Learning, and Recognition

    Jiang Wang;Xiaohan Nie;Yin Xia;Ying Wu

  • Minimax Entropy Principle and Its Application to Texture Modeling

    Song Chun Zhu;Ying Nian Wu;David Mumford

  • Learning Human-Object Interactions by Graph Parsing Neural Networks

    Siyuan Qi;Wenguan Wang;Baoxiong Jia;Jianbing Shen

  • Prior learning and Gibbs reaction-diffusion

    Song Chun Zhu;D. Mumford

  • Statistical edge detection: learning and evaluating edge cues

    S. Konishi;A.L. Yuille;J.M. Coughlan;Song Chun Zhu

  • FORMS: a flexible object recognition and modeling system

    Song Chun Zhu;Alan L. Yuille

  • I2T: Image Parsing to Text Description

    Benjamin Z Yao;Xiong Yang;Liang Lin;Mun Wai Lee

  • Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

    Unknown

  • Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation

    Hao-Shu Fang;Yuanlu Xu;Wenguan Wang;Xiaobai Liu

  • A Compositional and Dynamic Model for Face Aging

    Jinli Suo;Song-Chun Zhu;Shiguang Shan;Xilin Chen

  • FORMS: a flexible object recognition and modelling system

    S.C. Zhu;A.L. Yuille

Frequent Co-Authors

Ying Nian Wu
Ying Nian Wu University of California, Los Angeles
Yixin Zhu
Yixin Zhu Peking University
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Wenguan Wang
Wenguan Wang Zhejiang University
Yizhou Wang
Yizhou Wang Peking University
Sinisa Todorovic
Sinisa Todorovic Oregon State University
Zhuowen Tu
Zhuowen Tu University of California, San Diego
Liang Lin
Liang Lin Sun Yat-sen University
Caiming Xiong
Caiming Xiong Salesforce (United States)
Nanning Zheng
Nanning Zheng Xi'an Jiaotong University

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