World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
74
Citations
45827
World Ranking
1445
National Ranking
195

Xiaodong He 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 Xiaodong He 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: 342 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Xiaodong He 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 Xiaodong He 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: 74 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Overview

Xiaodong He is a researcher affiliated with the Chinese Academy of Sciences in China. Their primary field of study is computer science, with a focus on artificial intelligence and its related subfields.

The scientist's research encompasses a range of topics including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Advanced Graph Neural Networks
  • Speech and Dialogue Systems
  • Advanced Text Analysis Techniques
  • Speech Recognition and Synthesis

Their publication record consists of 198 works, predominantly in computer science, with significant contributions to artificial intelligence (131 publications) and computer vision and pattern recognition (45 publications). Additional subfields include information systems, signal processing, and computer graphics and computer-aided design.

Xiaodong He has published articles in several key academic venues, including:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • IEEE Journal of Selected Topics in Signal Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing

Some notable recent papers include:

  • "Rule Learning over Knowledge Graphs: A Review", 2023, published by Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Multimodal Intelligence: Representation Learning, Information Fusion, and Applications", 2020, IEEE Journal of Selected Topics in Signal Processing
  • "Select, Answer and Explain: Interpretable Multi-Hop Reading Comprehension over Multiple Documents", 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Keywords-Guided Abstractive Sentence Summarization", 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Aspect-Aware Multimodal Summarization for Chinese E-Commerce Products", 2020, Proceedings of the AAAI Conference on Artificial Intelligence

Xiaodong He has collaborated frequently with several co-authors, including:

  • Youzheng Wu
  • Bowen Zhou
  • Qi Wu
  • Peng Wang
  • Xin Wang

Best Publications

  • Hierarchical Attention Networks for Document Classification

    Zichao Yang;Diyi Yang;Chris Dyer;Xiaodong He

  • Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

    Peter Anderson;Xiaodong He;Chris Buehler;Damien Teney

  • Embedding Entities and Relations for Learning and Inference in Knowledge Bases

    Bishan Yang;Wen-tau Yih;Xiaodong He;Jianfeng Gao

  • Stacked Attention Networks for Image Question Answering

    Zichao Yang;Xiaodong He;Jianfeng Gao;Li Deng

  • Learning deep structured semantic models for web search using clickthrough data

    Po-Sen Huang;Xiaodong He;Jianfeng Gao;Li Deng

  • MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

    Yandong Guo;Lei Zhang;Yuxiao Hu;Xiaodong He

  • AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

    Tao Xu;Pengchuan Zhang;Qiuyuan Huang;Han Zhang

  • From captions to visual concepts and back

    Hao Fang;Saurabh Gupta;Forrest Iandola;Rupesh K. Srivastava

  • Stacked Cross Attention for Image-Text Matching

    Kuang-Huei Lee;Xi Chen;Gang Hua;Houdong Hu

  • Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval

    Hamid Palangi;Li Deng;Yelong Shen;Jianfeng Gao

  • Recent advances in deep learning for speech research at Microsoft

    Li Deng;Jinyu Li;Jui-Ting Huang;Kaisheng Yao

  • Learning semantic representations using convolutional neural networks for web search

    Yelong Shen;Xiaodong He;Jianfeng Gao;Li Deng

  • A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

    Yelong Shen;Xiaodong He;Jianfeng Gao;Li Deng

  • Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

    Wen-tau Yih;Ming-Wei Chang;Xiaodong He;Jianfeng Gao

  • A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

    Ali Mamdouh Elkahky;Yang Song;Xiaodong He

  • From Eliza to XiaoIce: challenges and opportunities with social chatbots

    Heung-yeung Shum;Xiao-dong He;Di Li

  • Using recurrent neural networks for slot filling in spoken language understanding

    Grégoire Mesnil;Yann Dauphin;Kaisheng Yao;Yoshua Bengio

  • A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories

    Nasrin Mostafazadeh;Nathanael Chambers;Xiaodong He;Devi Parikh

  • Domain Adaptation via Pseudo In-Domain Data Selection

    Amittai Axelrod;Xiaodong He;Jianfeng Gao

  • Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding.

    Grégoire Mesnil;Xiaodong He;Li Deng;Yoshua Bengio

  • Deep Learning with Low Precision by Half-Wave Gaussian Quantization

    Zhaowei Cai;Xiaodong He;Jian Sun;Nuno Vasconcelos

Frequent Co-Authors

Yibin Li
Yibin Li Harbin Institute of Technology
Rongguo Wang
Rongguo Wang Harbin Institute of Technology
Chao Wang
Chao Wang Soochow University
Shanyi Du
Shanyi Du Harbin Institute of Technology
Bowen Zhou
Bowen Zhou IBM (United States)
Anyuan Cao
Anyuan Cao Peking University
Liyong Tong
Liyong Tong University of Sydney
Jiecai Han
Jiecai Han Harbin Institute of Technology
Lin Ye
Lin Ye Southern University of Science and Technology
Sam Zhang
Sam Zhang Nanyang Technological University

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