World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
13146
World Ranking
5522
National Ranking
2521

Daxin Jiang 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 Daxin Jiang 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: 169 publications — 34th percentile

34% of scientists in this discipline score the same or lower.

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

Daxin Jiang 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 Daxin Jiang 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Daxin Jiang is affiliated with Microsoft in the United States and has an extensive publication record primarily in the field of Computer Science. Their research contributions encompass several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing, and General Health Professions.

The scientist's work covers a range of main topics such as:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Speech and Dialogue Systems
  • Speech Recognition and Synthesis
  • Advanced Text Analysis Techniques

Among Daxin Jiang's recent papers are:

  • "Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" (2021), published on arXiv (Cornell University)
  • "CodeBERT: A Pre-Trained Model for Programming and Natural Languages" (2020), published on arXiv (Cornell University)
  • "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "GraphCodeBERT: Pre-training Code Representations with Data Flow" (2020), published on arXiv (Cornell University)

Daxin Jiang frequently publishes in venues that include:

  • 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)
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The scientist has collaborated extensively with several coauthors, including:

  • Linjun Shou
  • Nan Duan
  • Xiubo Geng
  • Chongyang Tao
  • Ming Gong

Best Publications

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

    Zhangyin Feng;Daya Guo;Duyu Tang;Nan Duan

  • Cluster analysis for gene expression data: a survey

    Daxin Jiang;Chun Tang;Aidong Zhang

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

    Gen Li;Nan Duan;Yuejian Fang;Ming Gong

  • Context-aware query suggestion by mining click-through and session data

    Huanhuan Cao;Daxin Jiang;Jian Pei;Qi He

  • 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

  • On mining cross-graph quasi-cliques

    Jian Pei;Daxin Jiang;Aidong Zhang

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

    Yaobo Liang;Nan Duan;Yeyun Gong;Ning Wu

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

    Haoyang Huang;Yaobo Liang;Nan Duan;Ming Gong

  • Context-aware query classification

    Huanhuan Cao;Derek Hao Hu;Dou Shen;Daxin Jiang

  • DHC: a density-based hierarchical clustering method for time series gene expression data

    Daxin Jiang;Jian Pei;Aidong Zhang

  • Context-aware ranking in web search

    Biao Xiang;Daxin Jiang;Jian Pei;Xiaohui Sun

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

    Shangwen Lv;Daya Guo;Jingjing Xu;Duyu Tang

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

    Gen Li;Nan Duan;Yuejian Fang;Ming Gong

  • Web Query Recommendation via Sequential Query Prediction

    Qi He;Daxin Jiang;Zhen Liao;Steven C. H. Hoi

  • Towards context-aware search by learning a very large variable length hidden markov model from search logs

    Huanhuan Cao;Daxin Jiang;Jian Pei;Enhong Chen

  • NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion

    Chenfei Wu;Jian Liang;Lei Ji;Fan Yang

  • Mining frequent cross-graph quasi-cliques

    Daxin Jiang;Jian Pei

  • Mining coherent gene clusters from gene-sample-time microarray data

    Daxin Jiang;Jian Pei;Murali Ramanathan;Chun Tang

  • Joint Type Inference on Entities and Relations via Graph Convolutional Networks.

    Changzhi Sun;Yeyun Gong;Yuanbin Wu;Ming Gong

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

    Yaobo Liang;Nan Duan;Yeyun Gong;Ning Wu

Frequent Co-Authors

Nan Duan
Nan Duan Microsoft Research Asia (China)
Jian Pei
Jian Pei Duke University
Duyu Tang
Duyu Tang Fudan University
Ming Zhou
Ming Zhou Langboat Technology
Hang Li
Hang Li ByteDance
Aidong Zhang
Aidong Zhang University of Virginia
Shujie Liu
Shujie Liu Microsoft Research Asia (China)
Enhong Chen
Enhong Chen University of Science and Technology of China
Guodong Long
Guodong Long University of Technology Sydney
Bing Qin
Bing Qin Harbin Institute of Technology

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