World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
45682
World Ranking
7336
National Ranking
3192

John Winn 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 John Winn 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: 94 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

John Winn 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 John Winn 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

John Winn is affiliated with Microsoft in the United States. Their research spans multiple fields with notable contributions in computer science and medicine.

The main areas of study John Winn focuses on include:

  • Computer Science
  • Medicine

Their subfields consist of:

  • Computer Vision and Pattern Recognition
  • Pulmonary and Respiratory Medicine
  • Economics and Econometrics
  • Visual Arts and Performing Arts
  • Political Science and International Relations

John Winn's research topics encompass:

  • Cystic Fibrosis Research Advances
  • Inhalation and Respiratory Drug Delivery
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Advanced Vision and Imaging
  • Cinema and Media Studies
  • Art History and Market Analysis

Their recent published papers include:

  • Learning Direct Optimization for scene understanding, 2020, published in Pattern Recognition
  • Machine Learning Predicts Acute Pulmonary Exacerbations in Cystic Fibrosis, 2020, published in SSRN Electronic Journal
  • The landscapes of western movies: a history of filming on location, 1900-1970, 2023, published in New Review of Film and Television Studies

John Winn has collaborated with multiple co-authors including:

  • D. Sutcliffe
  • Emem Ukor
  • Judy Ryan
  • Janet M. Allen
  • Karen Brown

Their contributions to publication venues are distributed among:

  • SSRN Electronic Journal
  • Pattern Recognition
  • New Review of Film and Television Studies

Best Publications

  • The Pascal Visual Object Classes (VOC) Challenge

    Mark Everingham;Luc Gool;Christopher K. Williams;John Winn

  • The Pascal Visual Object Classes Challenge: A Retrospective

    Mark Everingham;S. M. Eslami;Luc Gool;Christopher K. Williams

  • TextonBoost : joint appearance, shape and context modeling for multi-class object recognition and segmentation

    Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi

  • TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

    Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi

  • Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

    Oliver Stegle;Leopold Parts;Matias Piipari;John Winn

  • Object categorization by learned universal visual dictionary

    J. Winn;A. Criminisi;T. Minka

  • Epitomic location recognition

    Kai Ni;A. Kannan;A. Criminisi;J. Winn

  • Variational Message Passing

    John Winn;Christopher M. Bishop

  • LOCUS: learning object classes with unsupervised segmentation

    J. Winn;N. Jojic

  • A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

    Oliver Stegle;Oliver Stegle;Leopold Parts;Richard Durbin;John M. Winn

  • Beyond atopy: Multiple patterns of sensitization in relation to asthma in a birth cohort study

    Angela Simpson;Vincent Y. F. Tan;John Winn;Markus Svensén

  • Non-linear Bayesian Image Modelling

    Christopher M. Bishop;John M. Winn

  • The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects

    J. Winn;J. Shotton

  • The Fourth Paradigm: Data-Intensive Scientific Discovery

    Iain Buchan;John Winn;Christopher Bishop

  • Photo clip art

    Jean-François Lalonde;Derek Hoiem;Alexei A. Efros;Carsten Rother

  • Discriminative Object Class Models of Appearance and Shape by Correlatons

    S. Savarese;J. Winn;A. Criminisi

  • The Shape Boltzmann Machine: A Strong Model of Object Shape

    S. M. Eslami;Nicolas Heess;Christopher K. Williams;John Winn

  • 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation

    D. Hoiem;C. Rother;J. Winn

  • Tree-based Classifiers for Bilayer Video Segmentation

    Pei Yin;A. Criminisi;J. Winn;M. Essa

  • Entangled decision forests and their application for semantic segmentation of CT images

    Albert Montillo;Jamie Shotton;John Winn;Juan Eugenio Iglesias

  • The Shape Boltzmann Machine: A strong model of object shape

    S. M. Ali Eslami;Nicolas Heess;John Winn

  • Gates

    Tom Minka;John Winn

Frequent Co-Authors

Antonio Criminisi
Antonio Criminisi Microsoft (United States)
Jamie Shotton
Jamie Shotton Microsoft (United States)
Christopher M. Bishop
Christopher M. Bishop Microsoft (United States)
Leopold Parts
Leopold Parts Wellcome Sanger Institute
Richard Durbin
Richard Durbin University of Cambridge
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Carsten Rother
Carsten Rother Heidelberg University
Oliver Stegle
Oliver Stegle German Cancer Research Center
Tom Minka
Tom Minka Microsoft (United States)
Tim D. Spector
Tim D. Spector King's College London

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