World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8788
World Ranking
10513
National Ranking
1294

Yanyan Lan 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 Yanyan Lan 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: 179 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Yanyan Lan 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 Yanyan Lan 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Yanyan Lan is affiliated with the Chinese Academy of Sciences in China. Their research primarily focuses on the field of Computer Science, with a specialized concentration in Artificial Intelligence. They have a substantial publication record spanning diverse subfields and topics within this domain.

Their work encompasses various research topics including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Computational Drug Discovery Methods
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning in Materials Science
  • Multimodal Machine Learning Applications
  • Advanced Text Analysis Techniques

Lan's publication record includes several recent papers. Among them are:

  • "Pre-trained models: Past, present and future" (2021), published in AI Open
  • "On Layer Normalization in the Transformer Architecture" (2020), published in arXiv (Cornell University)
  • "WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training" (2021), published in arXiv (Cornell University)
  • "Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers" (2023), published in Cell Reports Medicine
  • "Harnessing the potential of large language models in medical education: promise and pitfalls" (2024), published in Journal of the American Medical Informatics Association

Frequent coauthors collaborating with Lan include:

  • Xueqi Cheng (29 joint publications)
  • Liang Pang (21 joint publications)
  • Jiafeng Guo (19 joint publications)
  • Wei-Ying Ma (16 joint publications)
  • Yuyan Ni (13 joint publications)

The scientist's work is frequently published in several venues, notably:

  • arXiv (Cornell University) with 54 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 4 publications
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing with 3 publications
  • bioRxiv (Cold Spring Harbor Laboratory) with 3 publications
  • AI Open with 2 publications

Best Publications

  • A biterm topic model for short texts

    Xiaohui Yan;Jiafeng Guo;Yanyan Lan;Xueqi Cheng

  • Pre-Trained Models: Past, Present and Future

    Xu Han;Zhengyan Zhang;Ning Ding;Yuxian Gu

  • BTM: Topic Modeling over Short Texts

    Xueqi Cheng;Xiaohui Yan;Yanyan Lan;Jiafeng Guo

  • Text matching as image recognition

    Liang Pang;Yanyan Lan;Jiafeng Guo;Jun Xu

  • Learning Hierarchical Representation Model for NextBasket Recommendation

    Pengfei Wang;Jiafeng Guo;Yanyan Lan;Jun Xu

  • On Layer Normalization in the Transformer Architecture

    Ruibin Xiong;Yunchang Yang;Di He;Kai Zheng

  • A deep architecture for semantic matching with multiple positional sentence representations

    Shengxian Wan;Yanyan Lan;Jiafeng Guo;Jun Xu

  • DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval

    Liang Pang;Yanyan Lan;Jiafeng Guo;Jun Xu

  • On Layer Normalization in the Transformer Architecture

    Ruibin Xiong;Yunchang Yang;Di He;Kai Zheng

  • Ranking Measures and Loss Functions in Learning to Rank

    Wei Chen;Tie-yan Liu;Yanyan Lan;Zhi-ming Ma

  • Text Matching as Image Recognition

    Liang Pang;Yanyan Lan;Jiafeng Guo;Jun Xu

  • Match-SRNN: modeling the recursive matching structure with spatial RNN

    Shengxian Wan;Yanyan Lan;Jun Xu;Jiafeng Guo

  • ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation

    Hainan Zhang;Yanyan Lan;Liang Pang;Jiafeng Guo

  • A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

    Shengxian Wan;Yanyan Lan;Jiafeng Guo;Jun Xu

  • A Study of MatchPyramid Models on Ad-hoc Retrieval.

    Liang Pang;Yanyan Lan;Jiafeng Guo;Jun Xu

  • Learning for search result diversification

    Yadong Zhu;Yanyan Lan;Jiafeng Guo;Xueqi Cheng

  • SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

    Liang Pang;Jun Xu;Qingyao Ai;Yanyan Lan

  • Reinforcement Learning to Rank with Markov Decision Process

    Zeng Wei;Jun Xu;Yanyan Lan;Jiafeng Guo

  • A probabilistic model for bursty topic discovery in microblogs

    Xiaohui Yan;Jiafeng Guo;Yanyan Lan;Jun Xu

  • Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

    Shengxian Wan;Yanyan Lan;Jun Xu;Jiafeng Guo

  • Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures

    Long Xia;Jun Xu;Yanyan Lan;Jiafeng Guo

  • Learning to Control the Specificity in Neural Response Generation

    Ruqing Zhang;Jiafeng Guo;Yixing Fan;Yanyan Lan

  • Modeling Diverse Relevance Patterns in Ad-hoc Retrieval

    Yixing Fan;Jiafeng Guo;Yanyan Lan;Jun Xu

  • Modeling Diverse Relevance Patterns in Ad-hoc Retrieval

    Yixing Fan;Jiafeng Guo;Yanyan Lan;Jun Xu

  • Sparse word embeddings using l 1 regularized online learning

    Fei Sun;Jiafeng Guo;Yanyan Lan;Jun Xu

Frequent Co-Authors

Xueqi Cheng
Xueqi Cheng Chinese Academy of Sciences
Jiafeng Guo
Jiafeng Guo Chinese Academy of Sciences
Jun Xu
Jun Xu Renmin University of China
Tie-Yan Liu
Tie-Yan Liu Microsoft (United States)
Fei Sun
Fei Sun Institute Of Computing Technology
Dawei Yin
Dawei Yin Baidu (China)
Huawei Shen
Huawei Shen Chinese Academy of Sciences
Hang Li
Hang Li ByteDance
Wayne Xin Zhao
Wayne Xin Zhao Renmin University of China
Peng Jiang
Peng Jiang University of Florida

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