World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
11939
World Ranking
7054
National Ranking
940

Kam-Fai Wong 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 Kam-Fai Wong 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: 316 publications — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Kam-Fai Wong 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 Kam-Fai Wong 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Kam-Fai Wong is affiliated with the Chinese University of Hong Kong in China and has produced extensive research mainly within the domain of computer science. Their work spans several subfields, including artificial intelligence, computer vision and pattern recognition, information systems, sociology and political science, and statistical and nonlinear physics.

The scientist's research topics cover a wide range of areas, with primary focus on:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Multimodal Machine Learning Applications
  • Recommender Systems and Techniques
  • Sentiment Analysis and Opinion Mining
  • Semantic Web and Ontologies

Kam-Fai Wong has contributed to academic literature through numerous publications, including the following recent papers:

  • "Quotation Recommendation for Multi-party Online Conversations Based on Semantic and Topic Fusion," 2023, ACM Transactions on Information Systems
  • "An Attention-based Rumor Detection Model with Tree-structured Recursive Neural Networks," 2020, ACM Transactions on Intelligent Systems and Technology
  • "Improving Rumor Detection by Promoting Information Campaigns With Transformer-Based Generative Adversarial Learning," 2021, IEEE Transactions on Knowledge and Data Engineering
  • "A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning," 2023, Machine Intelligence Research
  • "Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception," 2024, arXiv (Cornell University)

Frequent co-authors working alongside Kam-Fai Wong include:

  • Hongru Wang
  • Lingzhi Wang
  • Xingshan Zeng
  • Wai-Chung Kwan
  • Fei Mi

This scholar has also published repeatedly in particular academic venues, such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Knowledge and Data Engineering
  • ACM Transactions on Information Systems
  • Gastroenterology

Kam-Fai Wong's research presence is primarily grounded in computer science, with a significant concentration on artificial intelligence applications and methodologies. Their work involves both theoretical and applied aspects, including studies on dialogue systems, machine learning approaches, and complex information modeling.

Best Publications

  • Interpreting TF-IDF term weights as making relevance decisions

    Ho Chung Wu;Robert Wing Pong Luk;Kam Fai Wong;Kui Lam Kwok

  • Detecting rumors from microblogs with recurrent neural networks

    Jing Ma;Wei Gao;Prasenjit Mitra;Sejeong Kwon

  • Detect Rumors Using Time Series of Social Context Information on Microblogging Websites

    Jing Ma;Wei Gao;Zhongyu Wei;Yueming Lu

  • Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

    Jing Ma;Wei Gao;Kam Fai Wong

  • Rumor Detection on Twitter with Tree-structured Recursive Neural Networks

    Jing Ma;Wei Gao;Kam-Fai Wong

  • Extractive Summarization Using Supervised and Semi-Supervised Learning

    Kam-Fai Wong;Mingli Wu;Wenjie Li

  • Component-based software engineering: technologies, development frameworks, and quality assurance schemes

    Xia Cai;M.R. Lyu;Kam-Fai Wong;Roy Ko

  • Detect Rumor and Stance Jointly by Neural Multi-task Learning

    Jing Ma;Wei Gao;Kam-Fai Wong

  • Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning

    Jing Ma;Wei Gao;Kam-Fai Wong

  • Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

    Baolin Peng;Xiujun Li;Jianfeng Gao;Jingjing Liu

  • Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

    Baolin Peng;Xiujun Li;Lihong Li;Jianfeng Gao

  • Task-oriented Dialogue System for Automatic Diagnosis

    Zhongyu Wei;Qianlong Liu;Baolin Peng;Huaixiao Tou

  • Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions

    Raymond Y. K. Lau;Stephen S. Y. Liao;K. F. Wong;Dickson K. W. Chiu

  • A genetic algorithm-based clustering approach for database partitioning

    Chun-Hung Cheng;Wing-Kin Lee;Kam-Fai Wong

  • A TSP-based heuristic for forming machine groups and part families

    C. H. Cheng;Y.P. Gupta;W.H. Lee;K.F. Wong

  • FACOPT: a user friendly FACility layout OPTimization system

    Jaydeep Balakrishnan;Chun Hung Cheng;Kam-Fai Wong

  • Natural Language Processing – IJCNLP 2005

    Robert Dale;Kam-Fai Wong;Jian Su;Oi Yee Kwong

  • Sentence-level evidence embedding for claim verification with hierarchical attention networks

    Jing Ma;Wei Gao;Shafiq R. Joty;Kam-Fai Wong

  • Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities

    Lanjun Zhou;Binyang Li;Wei Gao;Zhongyu Wei

  • Introduction to Chinese Natural Language Processing

    Kam-Fai Wong;Wenji Li;Ruifeng Xu;Zheng-sheng Zhang

  • Integrating planning for task-completion dialogue policy learning.

    Baolin Peng;Xiujun Li;Jianfeng Gao;Jingjing Liu

  • Detect rumors using time series of social context information on microblogging

    Jing Ma;Wei Gao;Zhongyu Wei;Yueming Lu

Frequent Co-Authors

Wenjie Li
Wenjie Li Hong Kong Polytechnic University
Zhongyu Wei
Zhongyu Wei Fudan University
Baolin Peng
Baolin Peng Microsoft (United States)
Ruifeng Xu
Ruifeng Xu Harbin Institute of Technology
Chun Hung Cheng
Chun Hung Cheng Chinese University of Hong Kong
Qin Lu
Qin Lu Hong Kong Polytechnic University
Peter Bruza
Peter Bruza Queensland University of Technology
S. L. Ho
S. L. Ho Hong Kong Polytechnic University
Dawei Song
Dawei Song The Open University
Jeffrey Xu Yu
Jeffrey Xu Yu Chinese University of Hong Kong

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