World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
7949
World Ranking
9201
National Ranking
117

See-Kiong Ng 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 See-Kiong Ng 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: 198 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

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

See-Kiong Ng 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 See-Kiong Ng 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

See-Kiong Ng is affiliated with the National University of Singapore, where their research primarily focuses on computer science. Their expertise spans several subfields including artificial intelligence, computer vision and pattern recognition, information systems, control and systems engineering, and computer networks and communications.

The scientist has contributed extensively to topics such as topic modeling, natural language processing techniques, multimodal machine learning applications, speech and dialogue systems, recommender systems and techniques, video analysis and summarization, and human pose and action recognition.

See-Kiong Ng's recent published papers include:

  • GPTScore: Evaluate as You Desire (2023), arXiv (Cornell University)
  • Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation (2022), Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
  • An Enhanced GAN Model for Automatic Satellite-to-Map Image Conversion (2020), IEEE Access
  • Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery (2022), Proceedings of the 30th ACM International Conference on Multimedia
  • MAD-SGCN: Multivariate Anomaly Detection with Self-learning Graph Convolutional Networks (2022), 2022 IEEE 38th International Conference on Data Engineering (ICDE)

Frequent co-authors collaborating with See-Kiong Ng include:

  • Tat-Seng Chua
  • Bryan Kian Hsiang Low
  • Xiaobao Wu
  • Bryan Hooi
  • Anh Tuan Luu

The scientist's publications appear regularly in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Actuators
  • SSRN Electronic Journal
  • Mathematics

In addition to articles, See-Kiong Ng has contributed to book publications with Springer Science+Business Media, including works titled Advances in Knowledge Discovery and Data Mining, published in 2020.

Best Publications

  • MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

    Dan Li;Dacheng Chen;Lei Shi;Baihong Jin

  • A core-attachment based method to detect protein complexes in PPI networks

    Min Wu;Xiaoli Li;Chee Keong Kwoh;See-Kiong Ng

  • Computational approaches for detecting protein complexes from protein interaction networks: a survey

    Xiaoli Li;Min Wu;Chee-Keong Kwoh;See-Kiong Ng

  • Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

    Dan Li;Dacheng Chen;Jonathan Goh;See-kiong Ng

  • Integrative approach for computationally inferring protein domain interactions.

    See-Kiong Ng;Zhuo Zhang;Soon-Heng Tan

  • Positive-unlabeled learning for disease gene identification

    Peng Yang;Xiao-Li Li;Jian-Ping Mei;Chee-Keong Kwoh

  • MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

    Dan Li;Dacheng Chen;Baihong Jin;Lei Shi

  • Toward Routine Automatic Pathway Discovery from On-line Scientific Text Abstracts.

    See-Kiong Ng;Marie Wong

  • InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes

    See-Kiong Ng;Zhuo Zhang;Soon-Heng Tan;Kui Lin

  • STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation

    Nicholas Lim;Bryan Hooi;See-Kiong Ng;Xueou Wang

  • GPTScore: Evaluate as You Desire

    Unknown

  • Interaction graph mining for protein complexes using local clique merging.

    Xiao-Li Li;Chuan-Sheng Foo;Chuan-Sheng Foo;Soon-Heng Tan;Soon-Heng Tan;See-Kiong Ng

  • Discovery of significant rules for classifying cancer diagnosis data

    Jinyan Li;Huiqing Liu;See-Kiong Ng;Limsoon Wong

  • NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs

    Jin Chen;Wynne Hsu;Mong Li Lee;See-Kiong Ng

  • Toward fully automated genotyping: genotyping microsatellite markers by deconvolution.

    M W Perlin;G Lancia;S K Ng

  • Positive Unlabeled Learning for Data Stream Classification.

    Xiao Li Li;Philip S. Yu;Bing Liu;See Kiong Ng

  • Integrated Oversampling for Imbalanced Time Series Classification

    Hong Cao;Xiao-Li Li;David Yew-Kwong Woon;See-Kiong Ng

  • Discovering protein complexes in dense reliable neighborhoods of protein interaction networks.

    Xiao-Li Li;Chuan-Sheng Foo;See-Kiong Ng

  • Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks

    Bo Zhang;Wei Li;Xiao-Li Li;See-Kiong Ng

  • A protein interaction extraction system

    See Kiong Ng;Lim Soon Wong

  • Increasing confidence of protein interactomes using network topological metrics

    Jin Chen;Wynne Hsu;Mong Li Lee;See-Kiong Ng

  • Ensemble Positive Unlabeled Learning for Disease Gene Identification

    Peng Yang;Xiaoli Li;Hon-Nian Chua;Chee-Keong Kwoh

Frequent Co-Authors

Xiaoli Li
Xiaoli Li Singapore University of Technology and Design
Limsoon Wong
Limsoon Wong National University of Singapore
Wing-Kin Sung
Wing-Kin Sung Chinese University of Hong Kong
Chee Keong Kwoh
Chee Keong Kwoh Nanyang Technological University
Wynne Hsu
Wynne Hsu National University of Singapore
Mong Li Lee
Mong Li Lee National University of Singapore
Roger Zimmermann
Roger Zimmermann National University of Singapore
Bing Liu
Bing Liu University of Illinois at Chicago
Chai Quek
Chai Quek Nanyang Technological University
Jinyan Li
Jinyan Li University of Technology Sydney

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing Computer Science in the USA opens doors to a wide range of online degrees and flexible career pathways. For those interested in data analysis and artificial intelligence, the data science learning path provides a practical and affordable way to build expertise in this in-demand field.

If you prefer a more technical or engineering-focused direction, an online bachelor’s in electrical engineering can also complement a computer science background, giving you the skills to work in areas like robotics, hardware, and embedded systems.

Many students are seeking fast entry points into the tech job market. Consider exploring easy certifications that pay well in fields such as cybersecurity, networking, or cloud computing. These credentials can boost your resume and quickly expand your career options.

For those aiming to accelerate their education, there are also programs among the quickest online masters degree options, allowing you to boost qualifications and earning potential in as little as a year.

Best Scientists Citing See-Kiong Ng

Trending Scientists

Recently Published Articles