World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
77
Citations
26881
World Ranking
1254
National Ranking
668

Daniel L. Rubin 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 Daniel L. Rubin 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: 449 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Daniel L. Rubin 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 Daniel L. Rubin 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: 77 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Overview

Daniel L. Rubin is affiliated with Stanford University in the United States. Their research spans multiple disciplines within medicine and computer science, with a significant focus on applications of artificial intelligence in medical imaging and healthcare.

The scientist's primary fields of study include:

  • Medicine
  • Computer Science

Within these broader fields, their main subfields of study comprise:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Oncology
  • Ophthalmology
  • Health Informatics

Rubin's work addresses several key topics, including:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Machine Learning in Healthcare
  • COVID-19 diagnosis using AI
  • Artificial Intelligence in Healthcare and Education
  • Retinal Imaging and Analysis
  • Retinal Diseases and Treatments

The scientist has contributed to a body of research published in notable venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Scientific Reports
  • Radiology Artificial Intelligence
  • Journal of Clinical Oncology

Recent scientific papers authored or co-authored by Daniel L. Rubin include:

  • Preparing Medical Imaging Data for Machine Learning, 2020, Radiology
  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms, 2020, JAMA Network Open
  • Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study, 2020, The Lancet Oncology
  • A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions, 2022, Scientific Data
  • Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations, 2020, Journal of the American College of Radiology

Frequent collaborators in Rubin's research include:

  • Imon Banerjee
  • Liangqiong Qu
  • Siyi Tang
  • Rikiya Yamashita
  • Christopher Lee-Messer

Best Publications

  • Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

    Kaustubh Supekar;Vinod Menon;Daniel J Rubin;Mark A. Musen

  • Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

    Zeynettin Akkus;Alfiia Galimzianova;Assaf Hoogi;Daniel L. Rubin

  • BioPortal: ontologies and integrated data resources at the click of a mouse

    Natalya Fridman Noy;Nigam H. Shah;Patricia L. Whetzel;Benjamin Dai

  • Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

    Kun-Hsing Yu;Ce Zhang;Gerald J. Berry;Russ B. Altman

  • Preparing Medical Imaging Data for Machine Learning.

    Martin J. Willemink;Wojciech A. Koszek;Cailin Hardell;Jie Wu

  • A curated mammography data set for use in computer-aided detection and diagnosis research.

    Rebecca Sawyer Lee;Francisco Gimenez;Assaf Hoogi;Kanae Kawai Miyake

  • PharmGKB: the Pharmacogenetics Knowledge Base.

    Micheal Hewett;Diane E. Oliver;Daniel L. Rubin;Katrina L. Easton

  • Content-Based Image Retrieval in Radiology: Current Status and Future Directions

    Ceyhun Burak Akgül;Daniel L. Rubin;Sandy Napel;Christopher F. Beaulieu

  • Integrating genotype and phenotype information: an overview of the PharmGKB project

    T. E. Klein;Jeffrey T Chang;M. K. Cho;K. L. Easton

  • MR imaging predictors of molecular profile and survival: Multi-institutional study of the TCGA glioblastoma data set

    David A. Gutman;Lee A.D. Cooper;Scott N. Hwang;Chad A. Holder

  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

    Thomas Schaffter;Diana S. M. Buist;Christoph I. Lee;Yaroslav Nikulin

  • Biomedical ontologies: a functional perspective

    Daniel L. Rubin;Nigam Shah;Natalya Fridman Noy

  • Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.

    Rikiya Yamashita;Jin Long;Teri Longacre;Lan Peng

  • Distributed deep learning networks among institutions for medical imaging.

    Ken Chang;Niranjan Balachandar;Carson K. Lam;Darvin Yi

  • Differential Data Augmentation Techniques for Medical Imaging Classification Tasks.

    Zeshan Hussain;Francisco Gimenez;Darvin Yi;Daniel L. Rubin

  • Deep Learning in Neuroradiology

    G. Zaharchuk;E. Gong;M. Wintermark;D. Rubin

  • Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

    Haruka Itakura;Achal S. Achrol;Lex A. Mitchell;Joshua J. Loya

  • A radiogenomic dataset of non-small cell lung cancer

    Shaimaa Bakr;Olivier Gevaert;Sebastian Echegaray;Kelsey Ayers

  • Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

    Mehmet Günhan Ertosun;Daniel L. Rubin

  • Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

    Imon Banerjee;Yuan Ling;Matthew C. Chen;Sadid A. Hasan

  • Robust noise region-based active contour model via local similarity factor for image segmentation

    Sijie Niu;Sijie Niu;Sijie Niu;Qiang Chen;Luis de Sisternes;Zexuan Ji

  • BioPortal: Ontologies and Integrated Data Resources at the Click of a Mouse

    Patricia L. Whetzel;Nigam H. Shah;Natalya F. Noy;Benjamin Dai

Frequent Co-Authors

Sandy Napel
Sandy Napel Stanford University
Mark A. Musen
Mark A. Musen Stanford University
Adrien Depeursinge
Adrien Depeursinge University of Applied Sciences and Arts Western Switzerland
Russ B. Altman
Russ B. Altman Stanford University
Christopher Ré
Christopher Ré Stanford University
Jayashree Kalpathy-Cramer
Jayashree Kalpathy-Cramer Harvard University
Nigam H. Shah
Nigam H. Shah Stanford University
Natalya F. Noy
Natalya F. Noy Google (United States)
James D. Brooks
James D. Brooks Stanford University
Allison W. Kurian
Allison W. Kurian Stanford University

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