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2025

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Rising Stars

D-Index
59
Citations
21081
World Ranking
170
National Ranking
20

Li Dong 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 Li Dong 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+

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

Li Dong 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 Li Dong 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+

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Li Dong is affiliated with Microsoft in the United States and specializes primarily in computer science with a focus on artificial intelligence. Their research encompasses several critical areas within the field, including natural language processing techniques, topic modeling, and multimodal machine learning applications.

Li Dong has contributed extensively to research, publishing a significant number of papers, particularly in artificial intelligence. Their recent work includes the following notable papers:

  • Unified language model pre-training for natural language understanding and generation (2024, arXiv [Cornell University])
  • MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers (2020, arXiv [Cornell University])
  • UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training (2020, arXiv [Cornell University])
  • Self-Attention Attribution: Interpreting Information Interactions Inside Transformer (2021, Proceedings of the AAAI Conference on Artificial Intelligence)
  • Knowledge Neurons in Pretrained Transformers (2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics [Volume 1: Long Papers])

Their frequent collaborators include Furu Wei, Shaohan Huang, Saksham Singhal, Song Xia, and Shuming Ma, with whom they have co-authored several papers. The collaborative nature of their work has contributed to the development of various models and techniques in machine learning and language understanding.

Common venues for Li Dong's publications are well-known and include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • World Wide Web

Li Dong's work systematically addresses a variety of subfields within computer science, with notable contributions to artificial intelligence, computer vision and pattern recognition, and information systems, as well as electrical and electronic engineering and control and systems engineering.

The main topics of Li Dong's research include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Speech Recognition and Synthesis
  • Explainable Artificial Intelligence (XAI)
  • Reinforcement Learning in Robotics
  • Adversarial Robustness in Machine Learning

Best Publications

  • Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

    Xiujun Li;Xi Yin;Chunyuan Li;Pengchuan Zhang

  • Long Short-Term Memory-Networks for Machine Reading

    Jianpeng Cheng;Li Dong;Mirella Lapata

  • Unified Language Model Pre-training for Natural Language Understanding and Generation

    Li Dong;Nan Yang;Wenhui Wang;Furu Wei

  • Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification

    Li Dong;Furu Wei;Chuanqi Tan;Duyu Tang

  • BEiT: BERT Pre-Training of Image Transformers

    Hangbo Bao;Li Dong;Furu Wei

  • Language to Logical Form with Neural Attention

    Li Dong;Mirella Lapata

  • MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

    Wenhui Wang;Furu Wei;Li Dong;Hangbo Bao

  • Question Answering over Freebase with Multi-Column Convolutional Neural Networks

    Li Dong;Furu Wei;Ming Zhou;Ke Xu

  • Coarse-to-Fine Decoding for Neural Semantic Parsing

    Li Dong;Mirella Lapata

  • MoodLens: an emoticon-based sentiment analysis system for chinese tweets

    Jichang Zhao;Li Dong;Junjie Wu;Ke Xu

  • Ranking with recursive neural networks and its application to multi-document summarization

    Ziqiang Cao;Furu Wei;Li Dong;Sujian Li

  • InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training

    Zewen Chi;Li Dong;Furu Wei;Nan Yang

  • Data-to-Text Generation with Content Selection and Planning

    Ratish Puduppully;Li Dong;Mirella Lapata

  • Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

    Ursula Challita;Li Dong;Walid Saad

  • Learning to Paraphrase for Question Answering

    Li Dong;Jonathan Mallinson;Siva Reddy;Mirella Lapata

  • Visualizing and Understanding the Effectiveness of BERT.

    Yaru Hao;Li Dong;Furu Wei;Ke Xu

  • Learning to Generate Product Reviews from Attributes

    Li Dong;Shaohan Huang;Furu Wei;Mirella Lapata

  • VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts

    Wenhui Wang;Hangbo Bao;Li Dong;Furu Wei

  • MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers

    Wenhui Wang;Hangbo Bao;Shaohan Huang;Li Dong

  • Pseudo-Masked Language Models for Unified Language Model Pre-Training

    Hangbo Bao;Li Dong;Furu Wei;Wenhui Wang

Frequent Co-Authors

Furu Wei
Furu Wei Microsoft (United States)
Ming Zhou
Ming Zhou Langboat Technology
Ke Xu
Ke Xu Beihang University
Mirella Lapata
Mirella Lapata University of Edinburgh
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Hsiao-Wuen Hon
Hsiao-Wuen Hon Microsoft Research Asia (China)
Ting Liu
Ting Liu Harbin Institute of Technology
Bing Qin
Bing Qin Harbin Institute of Technology
Wanxiang Che
Wanxiang Che Harbin Institute of Technology
Walid Saad
Walid Saad Virginia Tech

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