World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
13006
World Ranking
6699
National Ranking
2957

May D. Wang 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 May D. Wang 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: 299 publications — 74th percentile

74% of scientists in this discipline score the same or lower.

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

May D. Wang 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 May D. Wang 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.

Research.com Recognitions

  • 2015 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

May D. Wang is affiliated with the Georgia Institute of Technology in the United States and has contributed extensively to the field of medicine, with a primary focus on the intersection of artificial intelligence and healthcare. Their work spans multiple subfields including molecular biology, artificial intelligence, radiology, nuclear medicine and imaging, cognitive neuroscience, and public health, environmental and occupational health.

The scientist's recent papers cover a range of topics related to medical AI applications and public health challenges. These include:

  • "Multimodal deep learning models for early detection of Alzheimer's disease stage" (2021, Scientific Reports)
  • "Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis" (2020, BMC Medical Informatics and Decision Making)
  • "Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review" (2022, IEEE Reviews in Biomedical Engineering)
  • "Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?" (2020, IEEE Open Journal of Engineering in Medicine and Biology)
  • "COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks" (2021, IEEE Journal of Biomedical and Health Informatics)

Their frequent coauthors include Wenqi Shi, Yuanda Zhu, Tong Li, Felipe Giuste, and Gilbert C. Gee. This pattern of collaboration reflects a multidisciplinary approach encompassing data science, biomedical engineering, and public health.

May D. Wang has published extensively in widely recognized venues, with multiple works appearing in:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • SSRN Electronic Journal
  • Scientific Reports
  • IEEE Journal of Biomedical and Health Informatics

The main topics addressed across their body of work include machine learning applications in healthcare, artificial intelligence approaches to COVID-19 diagnosis, EEG and brain-computer interface technologies, gene expression and cancer classification, AI in healthcare and education, issues related to obesity, physical activity, and diet, as well as biomedical text mining and ontologies.

Among subfields, molecular biology, artificial intelligence, and cognitive neuroscience feature prominently, indicating an integration of computational techniques with complex biological systems.

May D. Wang was awarded the title of Fellow of the Indian National Academy of Engineering (INAE) in 2015.

Best Publications

  • GoMiner: a resource for biological interpretation of genomic and proteomic data

    Barry R Zeeberg;Weimin Feng;Geoffrey Wang;May D Wang

  • The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

    Leming Shi;Gregory Campbell;Wendell D. Jones;Fabien Campagne

  • A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

    Zhenqiang Su;Paweł P. Łabaj;Sheng Li;Jean Thierry-Mieg

  • Bioconjugated quantum dots for multiplexed and quantitative immunohistochemistry

    Yun Xing;Qaiser Chaudry;Christopher Shen;Koon Yin Kong

  • Multimodal deep learning models for early detection of Alzheimer's disease stage.

    Janani Venugopalan;Li Tong;Hamid Reza Hassanzadeh;May D. Wang

  • Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

    Wenqian Zhang;Ying Yu;Falk Hertwig;Falk Hertwig;Jean Thierry-Mieg

  • Detecting and correcting systematic variation in large-scale RNA sequencing data

    Sheng Li;Paweł P Łabaj;Paul Zumbo;Peter Sykacek

  • Omic and Electronic Health Record Big Data Analytics for Precision Medicine

    Po-Yen Wu;Chih-Wen Cheng;Chanchala D. Kaddi;Janani Venugopalan

  • Pathology imaging informatics for quantitative analysis of whole-slide images.

    Sonal Kothari;John H Phan;Todd H Stokes;May D Wang

  • Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study

    Sheng Li;Scott W Tighe;Charles M Nicolet;Deborah Grove

  • Hand-held Spectroscopic Device for In Vivo and Intraoperative Tumor Detection: Contrast Enhancement, Detection Sensitivity, and Tissue Penetration

    Aaron M. Mohs;Michael C. Mancini;Sunil Singhal;James M. Provenzale;James M. Provenzale

  • Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques

    Sonal Kothari;Qaiser Chaudry;May D. Wang

  • LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning

    Cheng Yang;Cheng Yang;Longshu Yang;Man Zhou;Haoling Xie

  • (Glyco)sphingolipidology: an amazing challenge and opportunity for systems biology

    Alfred H. Merrill;May Dongmei Wang;Meeyoung Park;M. Cameron Sullards

  • DeeperBind: Enhancing prediction of sequence specificities of DNA binding proteins

    Hamid Reza Hassanzadeh;May D. Wang

  • Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

    Li Tong;Jonathan Mitchel;Kevin Chatlin;May D. Wang

  • Sphingolipidomics: a valuable tool for understanding the roles of sphingolipids in biology and disease.

    Alfred H. Merrill;Todd H. Stokes;Amin Momin;Hyejung Park

  • k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction

    R. M. Parry;W. Jones;T. H. Stokes;J. H. Phan

  • Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data

    Ying Sha;John H. Phan;May D. Wang

  • Combining Two-Dimensional Diffusion-Ordered Nuclear Magnetic Resonance Spectroscopy, Imaging Desorption Electrospray Ionization Mass Spectrometry, and Direct Analysis in Real-Time Mass Spectrometry for the Integral Investigation of Counterfeit Pharmaceuticals

    Leonard Nyadong;Glenn A. Harris;Stéphane Balayssac;Asiri S. Galhena

  • A Review of Emerging Technologies for the Management of Diabetes Mellitus

    Konstantia Zarkogianni;Eleni Litsa;Konstantinos Mitsis;Po-Yen Wu

  • DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins

    May D Wang;Hamid Reza Hassanzadeh

Frequent Co-Authors

Shuming Nie
Shuming Nie University of Illinois at Urbana-Champaign
Facundo M. Fernández
Facundo M. Fernández Georgia Institute of Technology
Weida Tong
Weida Tong National Center for Toxicological Research
Leming Shi
Leming Shi Fudan University
Alfred H. Merrill
Alfred H. Merrill Georgia Institute of Technology
Matthias Fischer
Matthias Fischer University of Cologne
Christopher E. Mason
Christopher E. Mason Cornell University
Hong Fang
Hong Fang National Center for Toxicological Research
Huixiao Hong
Huixiao Hong United States Food and Drug Administration
Cesare Furlanello
Cesare Furlanello Fondazione Bruno Kessler

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