World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
19455
World Ranking
6653
National Ranking
894

Bing Qin 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 Bing Qin 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: 237 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Bing Qin 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 Bing Qin 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: 46 D-Index — 53rd percentile

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

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

Overview

Bing Qin is a researcher affiliated with the Harbin Institute of Technology in China, specializing in computer science with a strong focus on artificial intelligence. Their body of work covers diverse subfields including artificial intelligence, computer vision and pattern recognition, cognitive neuroscience, molecular biology, and information systems.

Their research contributions span several principal topics such as topic modeling, natural language processing techniques, sentiment analysis and opinion mining, multimodal machine learning applications, advanced text analysis techniques, advanced graph neural networks, and text and document classification technologies.

Bing Qin has published extensively, with recent notable papers including the following:

  • "A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions" (2024), published in ACM Transactions on Information Systems
  • "CodeBERT: A Pre-Trained Model for Programming and Natural Languages" (2020), published on arXiv (Cornell University)
  • "HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge" (2023), published on arXiv (Cornell University)
  • "The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey" (2021), published on arXiv (Cornell University)
  • "Development of Polymer Composites in Radiation Shielding Applications: A Review" (2023), published in Journal of Inorganic and Organometallic Polymers and Materials

Bing Qin frequently collaborates with other researchers, including Xiaocheng Feng, Sendong Zhao, Yanyan Zhao, Ting Liu, and Xiachong Feng. These collaborations have resulted in numerous joint publications and research output.

Their work is regularly disseminated through a number of academic venues, mostly in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • International Journal of Machine Learning and Cybernetics
  • SSRN Electronic Journal
  • IEEE/ACM Transactions on Audio Speech and Language Processing

Within the field of computer science, Bing Qin has authored over 380 publications, with more than 320 of these focused on artificial intelligence. Their research spans theoretical and practical aspects of AI, including language models, summarization, and multimodal machine learning.

Best Publications

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

    Zhangyin Feng;Daya Guo;Duyu Tang;Nan Duan

  • SemEval-2016 task 5 : aspect based sentiment analysis

    Maria Pontiki;Dimitris Galanis;Haris Papageorgiou;Ion Androutsopoulos

  • Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

    Duyu Tang;Bing Qin;Ting Liu

  • Pre-Training with Whole Word Masking for Chinese BERT

    Yiming Cui;Wanxiang Che;Ting Liu;Bing Qin

  • Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

    Duyu Tang;Furu Wei;Nan Yang;Ming Zhou

  • Aspect Level Sentiment Classification with Deep Memory Network

    Duyu Tang;Bing Qin;Ting Liu

  • Revisiting Pre-Trained Models for Chinese Natural Language Processing

    Yiming Cui;Wanxiang Che;Ting Liu;Bing Qin

  • Effective LSTMs for Target-Dependent Sentiment Classification

    Duyu Tang;Bing Qin;Xiaocheng Feng;Ting Liu

  • Learning Semantic Representations of Users and Products for Document Level Sentiment Classification

    Duyu Tang;Bing Qin;Ting Liu

  • Sentiment Embeddings with Applications to Sentiment Analysis

    Duyu Tang;Furu Wei;Bing Qin;Nan Yang

  • Learning Semantic Hierarchies via Word Embeddings

    Ruiji Fu;Jiang Guo;Bing Qin;Wanxiang Che

  • Coooolll: A Deep Learning System for Twitter Sentiment Classification

    Duyu Tang;Furu Wei;Bing Qin;Ting Liu

  • A language-independent neural network for event detection

    Xiaocheng Feng;Bing Qin;Ting Liu

  • A Language-Independent Neural Network for Event Detection.

    Xiaocheng Feng;Lifu Huang;Duyu Tang;Heng Ji

  • Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach

    Duyu Tang;Furu Wei;Bing Qin;Ming Zhou

  • Deep learning for sentiment analysis: successful approaches and future challenges

    Duyu Tang;Bing Qin;Ting Liu

  • User modeling with neural network for review rating prediction

    Duyu Tang;Bing Qin;Ting Liu;Yuekui Yang

  • Document representation and feature combination for deceptive spam review detection

    Luyang Li;Bing Qin;Wenjing Ren;Ting Liu

  • Target-Dependent Sentiment Classification with Long Short Term Memory.

    Duyu Tang;Bing Qin;Xiaocheng Feng;Ting Liu

  • Effective Deep Memory Networks for Distant Supervised Relation Extraction

    Xiaocheng Feng;Jiang Guo;Bing Qin;Ting Liu

Frequent Co-Authors

Ting Liu
Ting Liu Harbin Institute of Technology
Duyu Tang
Duyu Tang Fudan University
Wanxiang Che
Wanxiang Che Harbin Institute of Technology
Ming Zhou
Ming Zhou Langboat Technology
Nan Duan
Nan Duan Microsoft Research Asia (China)
Meng Jiang
Meng Jiang University of Notre Dame
Furu Wei
Furu Wei Microsoft (United States)
Min-Yen Kan
Min-Yen Kan National University of Singapore
Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
Li Dong
Li Dong Microsoft (United States)

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