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

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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