World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
12950
World Ranking
3446
National Ranking
58

Mong Li Lee 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 Mong Li Lee 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: 297 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Mong Li Lee 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 Mong Li Lee 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: 59 D-Index — 77th percentile

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

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

Overview

Mong Li Lee is affiliated with the National University of Singapore, Singapore. Their work spans the fields of Medicine and Computer Science, with a notable focus on Artificial Intelligence and its applications in healthcare.

They have published extensively in topics including Retinal Imaging and Analysis, Machine Learning in Healthcare, Artificial Intelligence in Healthcare, Retinal Diseases and Treatments, Topic Modeling, Biomedical Text Mining and Ontologies, and Retinal and Optic Conditions.

The scientist's recent papers include the following:

  • A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre, 2020, Nature Biomedical Engineering
  • Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study, 2020, The Lancet Digital Health
  • A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations, 2020, The Lancet Digital Health
  • Personalizing Medication Recommendation with a Graph-Based Approach, 2021, ACM Transactions on Information Systems
  • Multi-Task Learning for Diabetic Retinopathy Grading and Lesion Segmentation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence

Their frequent coauthors include Wynne Hsu, Ngiap Chuan Tan, Tien Yin Wong, Hao Sen Andrew Fang, and Dejiang Xu.

Mong Li Lee publishes regularly in venues such as arXiv (Cornell University), The Lancet Digital Health, Scientific Reports, PLoS ONE, and the Journal of the American Medical Informatics Association.

Best Publications

  • Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

    Daniel Shu Wei Ting;Daniel Shu Wei Ting;Carol Yim Lui Cheung;Carol Yim Lui Cheung;Gilbert Lim;Gavin Siew Wei Tan;Gavin Siew Wei Tan

  • Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

    Valentina Bellemo;Zhan W Lim;Gilbert Lim;Quang D Nguyen

  • A prime number labeling scheme for dynamic ordered XML trees

    X. Wu;M.L. Lee;W. Hsu

  • XClust: clustering XML schemas for effective integration

    Mong Li Lee;Liang Huai Yang;Wynne Hsu;Xia Yang

  • Supporting frequent updates in R-trees: a bottom-up approach

    Mong Li Lee;Wynne Hsu;Christian S. Jensen;Bin Cui

  • An effective approach to detect lesions in color retinal images

    Huan Wang;Wynne Hsu;Kheng Guan Goh;Mong Li Lee

  • Cleansing Data for Mining and Warehousing

    Mong-Li Lee;Tok Wang Ling;Hongjun Lu;Yee Teng Ko

  • Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study

    Yuchen Xie;Quang D Nguyen;Haslina Hamzah;Gilbert Lim

  • A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

    Charumathi Sabanayagam;Dejiang Xu;Daniel Sw Ting;Simon Nusinovici

  • Image Mining: Trends and Developments

    Wynne Hsu;Mong Li Lee;Ji Zhang

  • IntelliClean: a knowledge-based intelligent data cleaner

    Mong Li Lee;Tok Wang Ling;Wai Lup Low

  • Designing Functional Dependencies for XML

    Mong-Li Lee;Tok Wang Ling;Wai Lup Low

  • Mining relationships among interval-based events for classification

    Dhaval Patel;Wynne Hsu;Mong Li Lee

  • ICICLES: Self-Tuning Samples for Approximate Query Answering

    Venkatesh Ganti;Mong-Li Lee;Raghu Ramakrishnan

  • A knowledge-based approach for duplicate elimination in data cleaning

    Wai Lup Low;Mong Li Lee;Tok Wang Ling

  • Image mining: issues, frameworks and techniques

    Ji Zhang;Wynne Hsu;Mong Li Lee

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

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

  • Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application

    Valentina Bellemo;Gilbert Lim;Tyler Hyungtaek Rim;Gavin S W Tan

  • BORDER: efficient computation of boundary points

    Chenyi Xia;W. Hsu;M.L. Lee;B.C. Ooi

  • Automatic grading of retinal vessel caliber

    Huiqi Li;W. Hsu;Mong Li Lee;Tien Yin Wong

  • Efficient mining of XML query patterns for caching

    Liang Huai Yang;Mong Li Lee;Wynne Hsu

  • The role of domain knowledge in the detection of retinal hard exudates

    W. Hsu;P.M.D.S. Pallawala;Mong Li Lee;Kah-Guan Au Eong

Frequent Co-Authors

Wynne Hsu
Wynne Hsu National University of Singapore
Tok Wang Ling
Tok Wang Ling National University of Singapore
Tien Yin Wong
Tien Yin Wong Tsinghua University
Carol Y. Cheung
Carol Y. Cheung Chinese University of Hong Kong
Jie Jin Wang
Jie Jin Wang University of Sydney
Stéphane Bressan
Stéphane Bressan National University of Singapore
Paul Mitchell
Paul Mitchell University of Sydney
Ching-Yu Cheng
Ching-Yu Cheng National University of Singapore
Beng Chin Ooi
Beng Chin Ooi National University of Singapore
Gillian Dobbie
Gillian Dobbie University of Auckland

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