World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
5117
World Ranking
13062
National Ranking
5260

Philippe Burlina 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 Philippe Burlina 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: 155 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Philippe Burlina 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 Philippe Burlina 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Philippe Burlina is affiliated with Johns Hopkins University in the United States. Their research primarily spans the fields of computer science and medicine, with a significant focus on artificial intelligence and medical imaging.

Their publications cover various topics related to artificial intelligence applications in medical diagnostics, particularly in ophthalmology and retinal imaging. Notable recent papers include:

  • Addressing Artificial Intelligence Bias in Retinal Diagnostics (2021), published in Translational Vision Science & Technology
  • Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases (2020), published in JAMA Ophthalmology
  • AI-based detection of erythema migrans and disambiguation against other skin lesions (2020), published in Computers in Biology and Medicine
  • Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning (2021), published in JAMA Ophthalmology
  • Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema (2023), published in JAMA Ophthalmology

Burlina frequently collaborates with several co-authors, including William Paul, Neil Joshi, Haolin Yuan, and Yinzhi Cao. William Paul appears as a co-author in multiple publications, indicating an ongoing research partnership.

Their work has appeared in multiple publication venues, with a notable number of papers published in arXiv (Cornell University) and JAMA Ophthalmology. Other venues include the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Translational Vision Science & Technology, and Computers in Biology and Medicine.

The main fields of study for Burlina's research are:

  • Computer Science
  • Medicine

Their subfields of study reflect this interdisciplinary focus, covering:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Ophthalmology
  • Information Systems
  • Computer Vision and Pattern Recognition

The primary research topics addressed in their work include:

  • Adversarial Robustness in Machine Learning
  • Anomaly Detection Techniques and Applications
  • Retinal Imaging and Analysis
  • Privacy-Preserving Technologies in Data
  • Retinal Diseases and Treatments
  • Herpesvirus Infections and Treatments
  • Generative Adversarial Networks and Image Synthesis

Best Publications

  • Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

    Philippe M. Burlina;Neil Joshi;Michael Pekala;Katia D. Pacheco

  • A support vector method for anomaly detection in hyperspectral imagery

    A. Banerjee;P. Burlina;C. Diehl

  • Comparing humans and deep learning performance for grading AMD

    Philippe Burlina;Katia D. Pacheco;Neil Joshi;David E. Freund

  • AI for medical imaging goes deep.

    Daniel S W Ting;Daniel S W Ting;Yong Liu;Philippe Burlina;Xinxing Xu

  • Validating retinal fundus image analysis algorithms: issues and a proposal.

    Emanuele Trucco;Alfredo Ruggeri;Thomas Karnowski;Luca Giancardo

  • Deep learning based retinal OCT segmentation

    M. Pekala;N. Joshi;T.Y. Alvin Liu;N.M. Bressler

  • Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration

    Philippe M. Burlina;Philippe M. Burlina;Neil Joshi;Katia D. Pacheco;T. Y. Alvin Liu

  • Detection of age-related macular degeneration via deep learning

    P. Burlina;D. E. Freund;N. Joshi;Y. Wolfson

  • Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

    Philippe Burlina;Seth Billings;Neil Joshi;Jemima Albayda

  • Addressing Artificial Intelligence Bias in Retinal Diagnostics

    Philippe Burlina;Philippe Burlina;Neil Joshi;William Paul;Katia D. Pacheco

  • Kernel fully constrained least squares abundance estimates

    J. Broadwater;R. Chellappa;A. Banerjee;P. Burlina

  • System and method of managing web content

    Albert Brown;Philippe Burlina;Stephane Depuy;Shuang Wang

  • Adaptive target detection in foliage-penetrating SAR images using alpha-stable models

    A. Banerjee;P. Burlina;R. Chellappa

  • Higher order statistical learning for vehicle detection in images

    A.N. Rajagopalan;P. Burlina;R. Chellappa

  • A system and method for automated detection of age related macular degeneration and other retinal abnormalities

    Neil Bressler;Philippe Martin Burlina;David Eric Freund

  • Practical Blind Membership Inference Attack via Differential Comparisons

    Bo Hui;Yuchen Yang;Haolin Yuan;Philippe Burlina

  • Fast Hyperspectral Anomaly Detection via SVDD

    A. Banerjee;P. Burlina;R. Meth

  • Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

    Albert K. Feeny;Mongkol Tadarati;David E. Freund;Neil M. Bressler

  • Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

    Philippe Burlina;Philippe Burlina;William Paul;Philip Mathew;Neil Joshi

  • Automated detection of drusen in the macula

    D.E. Freund;N. Bressler;P. Burlina

  • Image segmentation and labeling using the Polya urn model

    A. Banerjee;P. Burlina;F. Alajaji

  • Automated detection of erythema migrans and other confounding skin lesions via deep learning.

    Philippe M. Burlina;Philippe M. Burlina;Neil J. Joshi;Elise Ng;Seth D. Billings

  • Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection

    Philippe Burlina;Neil Joshi;I-Jeng Wang

Frequent Co-Authors

Rama Chellappa
Rama Chellappa Johns Hopkins University
Neil M. Bressler
Neil M. Bressler Johns Hopkins University School of Medicine
Fady Alajaji
Fady Alajaji Queen's University
Daniel DeMenthon
Daniel DeMenthon Johns Hopkins University Applied Physics Laboratory
Elliot R. McVeigh
Elliot R. McVeigh University of California, San Diego
Larry S. Davis
Larry S. Davis University of Maryland, College Park
Tien Yin Wong
Tien Yin Wong Tsinghua University
Gregory D. Hager
Gregory D. Hager Johns Hopkins University
Michael D. Abràmoff
Michael D. Abràmoff University of Iowa
Robert J. Greenberg
Robert J. Greenberg Johns Hopkins University

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