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Computer Science
China
2026

D-Index & Metrics

Computer Science

D-Index
106
Citations
45037
World Ranking
276
National Ranking
34

Ming Zhou 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 Ming Zhou 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: 528 publications — 94th percentile

94% of scientists in this discipline score the same or lower.

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

Ming Zhou 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 Ming Zhou 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: 106 D-Index — 98th percentile

98% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in China Leader Award
  • 2025 - Research.com Computer Science in China Leader Award
  • 2023 - Research.com Computer Science in China Leader Award
  • 2022 - Research.com Computer Science in China Leader Award

Overview

Ming Zhou is affiliated with Langboat Technology in China and has contributed extensively to the field of computer science, with a focus on artificial intelligence, computer vision and pattern recognition, and related subfields. Their research covers a broad range of topics including topic modeling, natural language processing techniques, and multimodal machine learning applications.

Their recent papers demonstrate active engagement in advancing language models and machine learning. Notable publications include:

  • "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))
  • "UniXcoder: Unified Cross-Modal Pre-training for Code Representation" (2022, 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, arXiv (Cornell University))
  • "Progress in Neural NLP: Modeling, Learning, and Reasoning" (2020, Engineering)

Ming Zhou frequently collaborates with other researchers in the field. Their most common coauthors include:

  • Nan Duan (42 joint publications)
  • Wanjun Zhong (16 joint publications)
  • Furu Wei (16 joint publications)
  • Duyu Tang (16 joint publications)
  • Daxin Jiang (14 joint publications)

Their work has been published predominantly in venues such as:

  • arXiv (Cornell University) with 60 publications
  • IEEE/ACM Transactions on Audio Speech and Language Processing (3 publications)
  • Proceedings of the AAAI Conference on Artificial Intelligence (2 publications)
  • Journal of Pathology Informatics (2 publications)
  • International Journal of Logistics Economics and Globalisation (2 publications)

Their research addresses several main topics within computer science, especially in natural language processing and machine learning, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Software Engineering Research
  • Advanced Text Analysis Techniques
  • Text Readability and Simplification
  • Advanced Image and Video Retrieval Techniques

Ming Zhou's scholarly contributions reflect a sustained focus on both foundational models and practical applications in artificial intelligence. Their career includes notable research advances in model pre-training, task-agnostic model compression, and cross-modal code representation.

Best Publications

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

    Zhangyin Feng;Daya Guo;Duyu Tang;Nan Duan

  • Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

    Duyu Tang;Furu Wei;Nan Yang;Ming Zhou

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

    Li Dong;Nan Yang;Wenhui Wang;Furu Wei

  • Target-dependent Twitter Sentiment Classification

    Long Jiang;Mo Yu;Ming Zhou;Xiaohua Liu

  • LayoutLM: Pre-training of Text and Layout for Document Image Understanding

    Yiheng Xu;Minghao Li;Lei Cui;Shaohan Huang

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

    Li Dong;Furu Wei;Chuanqi Tan;Duyu Tang

  • Gated Self-Matching Networks for Reading Comprehension and Question Answering

    Wenhui Wang;Nan Yang;Furu Wei;Baobao Chang

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

    Wenhui Wang;Furu Wei;Li Dong;Hangbo Bao

  • Achieving Human Parity on Automatic Chinese to English News Translation

    Hany Hassan;Anthony Aue;Chang Chen;Vishal Chowdhary

  • Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach

    Xiaolong Wang;Furu Wei;Xiaohua Liu;Ming Zhou

  • Recognizing Named Entities in Tweets

    Xiaohua Liu;Shaodian Zhang;Furu Wei;Ming Zhou

  • User-level sentiment analysis incorporating social networks

    Chenhao Tan;Lillian Lee;Jie Tang;Long Jiang

  • Low-Quality Product Review Detection in Opinion Summarization

    Jingjing Liu;Yunbo Cao;Chin-Yew Lin;Yalou Huang

  • Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots

    Yu Wu;Wei Wu;Chen Xing;Ming Zhou

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

    Ruize Wang;Duyu Tang;Nan Duan;zhongyu wei

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

    Li Dong;Furu Wei;Ming Zhou;Ke Xu

  • Topic Aware Neural Response Generation

    Chen Xing;Wei Wu;Yu Wu;Jie Liu

  • GraphCodeBERT: Pre-training Code Representations with Data Flow

    Daya Guo;Shuo Ren;Shuai Lu;Zhangyin Feng

  • HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization

    Xingxing Zhang;Furu Wei;Ming Zhou

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

    Shuai Lu;Daya Guo;Shuo Ren;Junjie Huang

  • MIND: A Large-scale Dataset for News Recommendation

    Fangzhao Wu;Ying Qiao;Jiun-Hung Chen;Chuhan Wu

  • UniXcoder: Unified Cross-Modal Pre-training for Code Representation

    Unknown

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

    Gen Li;Nan Duan;Yuejian Fang;Ming Gong

Frequent Co-Authors

Furu Wei
Furu Wei Microsoft (United States)
Mu Li
Mu Li Amazon (United States)
Nan Duan
Nan Duan Microsoft Research Asia (China)
Shujie Liu
Shujie Liu Microsoft Research Asia (China)
Zhoujun Li
Zhoujun Li Beihang University
Yu Wu
Yu Wu Microsoft Research Asia (China)
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Duyu Tang
Duyu Tang Fudan University
Li Dong
Li Dong Microsoft (United States)
Ting Liu
Ting Liu Harbin Institute of Technology

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