World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
10011
World Ranking
9112
National Ranking
360

Xiaodan Zhu 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 Xiaodan Zhu 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: 105 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Xiaodan Zhu 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 Xiaodan Zhu 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Xiaodan Zhu is affiliated with Queen's University in Canada and specializes in the field of Computer Science, with a focus on Artificial Intelligence. Their research encompasses various subfields including Computer Vision and Pattern Recognition, Molecular Biology, Political Science and International Relations, and Materials Chemistry.

The primary topics addressed in their work are:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Multimodal Machine Learning Applications
  • Explainable Artificial Intelligence (XAI)
  • Artificial Intelligence in Law
  • Sentiment Analysis and Opinion Mining

Xiaodan Zhu has coauthored extensively with researchers such as Rohan Bhambhoria, Samuel Dahan, Quan Liu, Stephen Obadinma, and Zhen-Hua Ling.

The scientist's recent papers include:

  • Demand Forecasting with Supply-Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry (2021), published in Production and Operations Management
  • Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review (2020), published in IEEE Reviews in Biomedical Engineering
  • Learning Cross-Modal Context Graph for Visual Grounding (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • Optimal scheduling of park-level integrated energy system considering ladder-type carbon trading mechanism and flexible load (2023), published in Energy Reports
  • Mitochondrial dysfunction in hearing loss: Oxidative stress, autophagy and NLRP3 inflammasome (2023), published in Frontiers in Cell and Developmental Biology

The frequent publication venues where Xiaodan Zhu has contributed include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Physics Letters B

Xiaodan Zhu has also published books through Springer Science+Business Media, including Natural Language Processing and Chinese Computing and Advances in Artificial Intelligence, both released in 2020.

Best Publications

  • Enhanced LSTM for Natural Language Inference

    Qian Chen;Xiaodan Zhu;Zhen-Hua Ling;Si Wei

  • NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets

    Saif Mohammad;Svetlana Kiritchenko;Xiaodan Zhu

  • Sentiment analysis of short informal texts

    Svetlana Kiritchenko;Xiaodan Zhu;Saif M. Mohammad

  • SemEval-2016 Task 6: Detecting Stance in Tweets

    Saif Mohammad;Svetlana Kiritchenko;Parinaz Sobhani;Xiaodan Zhu

  • NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews

    Svetlana Kiritchenko;Xiaodan Zhu;Colin Cherry;Saif Mohammad

  • Long Short-Term Memory Over Recursive Structures

    Xiaodan Zhu;Parinaz Sobihani;Hongyu Guo

  • Sentiment, emotion, purpose, and style in electoral tweets

    Saif M. Mohammad;Xiaodan Zhu;Svetlana Kiritchenko;Joel Martin

  • Measuring academic influence: Not all citations are equal

    Xiaodan Zhu;Peter D. Turney;Daniel Lemire;André Vellino

  • Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010

    Berry de Bruijn;Colin Cherry;Svetlana Kiritchenko;Joel D. Martin

  • Neural Natural Language Inference Models Enhanced with External Knowledge

    Qian Chen;Xiaodan Zhu;Zhen-Hua Ling;Diana Inkpen

  • Induction Networks for Few-Shot Text Classification.

    Ruiying Geng;Binhua Li;Yongbin Li;Xiaodan Zhu

  • NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets

    Xiaodan Zhu;Svetlana Kiritchenko;Saif Mohammad

  • Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots

    Jia-Chen Gu;Tianda Li;Quan Liu;Zhen-Hua Ling

  • A Dataset for Multi-Target Stance Detection.

    Parinaz Sobhani;Diana Inkpen;Xiaodan Zhu

  • Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference.

    Qian Chen;Xiaodan Zhu;Zhen-Hua Ling;Si Wei

  • Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review

    Karly Kudrinko;Emile Flavin;Xiaodan Zhu;Qingguo Li

  • SemEval-2020 Task 4: Commonsense Validation and Explanation

    Cunxiang Wang;Shuailong Liang;Yili Jin;Yilong Wang

  • Analysis of polarity information in medical text.

    Yun Niu;Xiaodan Zhu;Jianhua Li;Graeme Hirst

  • Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts

    Preslav Nakov;Sara Rosenthal;Svetlana Kiritchenko;Saif M. Mohammad

  • Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

    Qian Chen;Xiaodan Zhu;Zhen-Hua Ling;Si Wei

  • A Dataset for Detecting Stance in Tweets.

    Saif Mohammad;Svetlana Kiritchenko;Parinaz Sobhani;Xiao-Dan Zhu

  • Improving Image Captioning with Better Use of Caption

    Zhan Shi;Xu Zhou;Xipeng Qiu;Xiaodan Zhu

Frequent Co-Authors

Zhen-Hua Ling
Zhen-Hua Ling University of Science and Technology of China
Saif M. Mohammad
Saif M. Mohammad National Research Council Canada
Hui Jiang
Hui Jiang York University
Svetlana Kiritchenko
Svetlana Kiritchenko National Research Council Canada
Colin Cherry
Colin Cherry Google (Canada)
Diana Inkpen
Diana Inkpen University of Ottawa
Gerald Penn
Gerald Penn University of Toronto
Luo Si
Luo Si Alibaba Group (China)
Graeme Hirst
Graeme Hirst University of Toronto
Stan Matwin
Stan Matwin Dalhousie University

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