World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
84
Citations
83414
World Ranking
817
National Ranking
40

John Shawe-Taylor 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 Shawe-Taylor 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: 543 publications — 95th percentile

95% of scientists in this discipline score the same or lower.

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

John Shawe-Taylor 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 Shawe-Taylor 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: 84 D-Index — 94th percentile

94% 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

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award

Overview

John Shawe-Taylor is affiliated with University College London in the United Kingdom. Their research spans primarily the field of computer science, with a notable focus on artificial intelligence. They have contributed extensively across multiple subfields, including artificial intelligence, computer science applications, computer vision and pattern recognition, molecular biology, and sociology and political science.

The scientist's work often addresses topics such as online learning and analytics, intelligent tutoring systems and adaptive learning, machine learning and data classification, machine learning and algorithms, adversarial robustness in machine learning, reinforcement learning in robotics, and COVID-19 epidemiological studies.

John Shawe-Taylor has published numerous papers in various scientific venues, with frequent publications in the following outlets:

  • arXiv (Cornell University) - 27 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence - 6 publications
  • bioRxiv (Cold Spring Harbor Laboratory) - 4 publications
  • Journal of artificial intelligence for sustainable development. - 4 publications
  • Scientific Reports - 3 publications

Recent papers by John Shawe-Taylor include:

  • Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools, 2024, Sustainability
  • Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study, 2022, Biological Psychiatry Cognitive Neuroscience and Neuroimaging
  • Expert-level automated malaria diagnosis on routine blood films with deep neural networks, 2020, American Journal of Hematology
  • Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture, 2021, Patterns
  • The Human Behaviour-Change Project: An artificial intelligence system to answer questions about changing behaviour, 2020, Wellcome Open Research

The scientist collaborates frequently with other researchers, including María Pérez-Ortiz, Sahan Bulathwela, Emine Yılmaz, Benjamin Guedj, and Omar Rivasplata. Their joint work indicates engagement with interdisciplinary teams and projects spanning several aspects of machine learning and artificial intelligence methodologies.

Best Publications

  • An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

    Nello Cristianini;John Shawe-Taylor

  • Kernel Methods for Pattern Analysis

    John Shawe-Taylor;Nello Cristianini

  • Estimating the Support of a High-Dimensional Distribution

    Bernhard Schölkopf;John C. Platt;John C. Shawe-Taylor;Alex J. Smola

  • An Introduction to Support Vector Machines

    Nello Cristianini;John Shawe-Taylor

  • Canonical Correlation Analysis: An Overview with Application to Learning Methods

    Unknown

  • Large Margin DAGs for Multiclass Classification

    John C. Platt;Nello Cristianini;John Shawe-Taylor

  • Support Vector Method for Novelty Detection

    Bernhard Schölkopf;Robert C Williamson;Alex J. Smola;John Shawe-Taylor

  • Text classification using string kernels

    Huma Lodhi;Craig Saunders;John Shawe-Taylor;Nello Cristianini

  • Challenges in Representation Learning: A Report on Three Machine Learning Contests

    Ian J. Goodfellow;Dumitru Erhan;Pierre Luc Carrier;Aaron Courville

  • On Kernel-Target Alignment

    Nello Cristianini;John Shawe-Taylor;André Elisseeff;Jaz S. Kandola

  • Structural risk minimization over data-dependent hierarchies

    J. Shawe-Taylor;P.L. Bartlett;R.C. Williamson;M. Anthony

  • Linear Programming Boosting via Column Generation

    Ayhan Demiriz;Kristin P. Bennett;John Shawe-Taylor

  • The 2005 PASCAL visual object classes challenge

    Mark Everingham;Andrew Zisserman;Christopher K. I. Williams;Luc Van Gool

  • Challenges in representation learning

    Ian J. Goodfellow;Dumitru Erhan;Pierre Luc Carrier;Aaron Courville

  • Generalization performance of support vector machines and other pattern classifiers

    Peter Bartlett;John Shawe-Taylor

  • Latent Semantic Kernels

    Nello Cristianini;John Shawe-Taylor;Huma Lodhi

  • Two view learning: SVM-2K, Theory and Practice

    Jason Farquhar;David Hardoon;Hongying Meng;John S. Shawe-taylor

  • Text Classification using String Kernels

    Huma Lodhi;John Shawe-Taylor;Nello Cristianini;Christopher J. C. H. Watkins

  • Kernel-Based Learning of Hierarchical Multilabel Classification Models

    Juho Rousu;Craig Saunders;Sandor Szedmak;John Shawe-Taylor

  • Large Margin DAG's for Multiclass Classification

    John Platt;Nello Cristianini;John Shawe-Taylor

  • Advances in Neural Information Processing Systems 15 (NIPS 2002)

    Christopher Williams;John S. Shawe-taylor

  • Advances in Kernel Methods - Support Vector Learning

    Nello Cristianini;J Shawe-Taylor

  • Kernel Methods for Pattern Analysis: Pattern analysis

    John Shawe-Taylor;Nello Cristianini

Frequent Co-Authors

Nello Cristianini
Nello Cristianini University of Bath
Janaina Mourao-Miranda
Janaina Mourao-Miranda University College London
Peter Auer
Peter Auer University of Leoben
François Laviolette
François Laviolette Université Laval
Robert C. Williamson
Robert C. Williamson University of Tübingen
Thore Graepel
Thore Graepel University College London
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Samuel Kaski
Samuel Kaski Aalto University
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Nicolò Cesa-Bianchi
Nicolò Cesa-Bianchi University of Milan

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