World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
14918
World Ranking
3074
National Ranking
412

Nan Duan 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 Nan Duan 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: 219 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Nan Duan 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 Nan Duan 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: 61 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Overview

Nan Duan is affiliated with Microsoft Research Asia (China) and has contributed extensively to the field of computer science, particularly focusing on artificial intelligence and multimodal machine learning. Their research encompasses various subfields, including computer vision and pattern recognition, information systems, signal processing, and software engineering.

The scientist's publication record includes nearly 500 papers primarily in computer science. Their work often centers on topics such as topic modeling, natural language processing techniques, and multimodal machine learning applications. Additional areas of specialization include domain adaptation and few-shot learning, software engineering research, advanced image and video retrieval techniques, and video analysis and summarization.

Frequent co-authors collaborating with Nan Duan include:

  • Yeyun Gong
  • Daxin Jiang
  • Ming Zhou
  • Chenfei Wu
  • Weizhu Chen

The scientist's work has appeared in prominent publication venues with repeated contributions to:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Neurocomputing
  • Findings of the Association for Computational Linguistics: ACL 2022

Some recent papers showcasing the breadth of Nan Duan's research include:

  • Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training (2020), published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • scGPT: toward building a foundation model for single-cell multi-omics using generative AI (2024), published in Nature Methods
  • CLIP4Clip: An empirical study of CLIP for end to end video clip retrieval and captioning (2022), published in Neurocomputing
  • UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022), published in the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021), published in arXiv (Cornell University)

Best Publications

  • CodeBERT: A Pre-Trained Model for Programming and Natural Languages

    Zhangyin Feng;Daya Guo;Duyu Tang;Nan Duan

  • CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

    Unknown

  • Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training.

    Gen Li;Nan Duan;Yuejian Fang;Ming Gong

  • K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

    Ruize Wang;Duyu Tang;Nan Duan;zhongyu wei

  • GraphCodeBERT: Pre-training Code Representations with Data Flow

    Daya Guo;Shuo Ren;Shuai Lu;Zhangyin Feng

  • CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

    Shuai Lu;Daya Guo;Shuo Ren;Junjie Huang

  • ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training.

    Weizhen Qi;Yu Yan;Yeyun Gong;Dayiheng Liu

  • Question Generation for Question Answering

    Nan Duan;Duyu Tang;Peng Chen;Ming Zhou

  • XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation

    Yaobo Liang;Nan Duan;Yeyun Gong;Ning Wu

  • Progress in Neural NLP: Modeling, Learning, and Reasoning

    Ming Zhou;Nan Duan;Shujie Liu;Heung Yeung Shum

  • Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks

    Haoyang Huang;Yaobo Liang;Nan Duan;Ming Gong

  • Question Answering and Question Generation as Dual Tasks

    Duyu Tang;Nan Duan;Tao Qin;Zhao Yan

  • Constraint-Based Question Answering with Knowledge Graph

    Junwei Bao;Nan Duan;Zhao Yan;Ming Zhou

  • Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

    Shangwen Lv;Daya Guo;Jingjing Xu;Duyu Tang

  • Building Task-Oriented Dialogue Systems for Online Shopping.

    Zhao Yan;Nan Duan;Peng Chen;Ming Zhou

  • Reasoning Over Semantic-Level Graph for Fact Checking

    Wanjun Zhong;Jingjing Xu;Duyu Tang;Zenan Xu

  • Automating code review activities by large-scale pre-training

    Unknown

  • Knowledge-Based Question Answering as Machine Translation

    Junwei Bao;Nan Duan;Ming Zhou;Tiejun Zhao

  • AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

    Unknown

  • Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training

    Gen Li;Nan Duan;Yuejian Fang;Ming Gong

  • Visual Question Generation as Dual Task of Visual Question Answering

    Yikang Li;Nan Duan;Bolei Zhou;Xiao Chu

  • UniViLM: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation.

    Huaishao Luo;Lei Ji;Botian Shi;Haoyang Huang

  • XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation

    Yaobo Liang;Nan Duan;Yeyun Gong;Ning Wu

Frequent Co-Authors

Ming Zhou
Ming Zhou Langboat Technology
Duyu Tang
Duyu Tang Fudan University
Daxin Jiang
Daxin Jiang Microsoft (United States)
Shujie Liu
Shujie Liu Microsoft Research Asia (China)
Bing Qin
Bing Qin Harbin Institute of Technology
Houqiang Li
Houqiang Li University of Science and Technology of China
Xuanjing Huang
Xuanjing Huang Fudan University
Zhoujun Li
Zhoujun Li Beihang University
Ting Liu
Ting Liu Harbin Institute of Technology
Jianfeng Gao
Jianfeng Gao Microsoft (United States)

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