John Stewart Shawe-Taylor (born 1953 in Cheltenham, England) is a researcher in computer science and statistics who serves as Director of the Centre for Computational Statistics and Machine Learning at University College London (UK). He was Head of the Computer Science Department at University College London from 2010 to 2019, where he oversaw a significant expansion and witnessed its emergence as the highest-ranked Computer Science Department in the UK in the 2014 UK Research Evaluation Framework (REF). His academic background and affiliations connect him to the University of Southampton, University College London, Imperial College London, and Royal Holloway, University of London. In 1985, he completed "Regularity and Transitivity in Graphs", working alongside Norman L. Biggs and Tomaž Pisanski.

Shawe-Taylor's main research area is statistical learning theory, with contributions extending across fields from graph theory and cryptography to statistical learning theory and its applications. His primary contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, including mapping these approaches onto novel domains such as computer vision, document classification, and brain scan analysis. More recently, he has conducted research on interactive learning and reinforcement learning.

Shawe-Taylor has also been instrumental in assembling a series of influential European Networks of Excellence, initially the NeuroCOLT projects and later the PASCAL networks. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning across science and industry. He has published over 300 papers with over 42,000 citations, and two monographs on statistical learning theory and support vector machines co-authored with Nello Cristianini have become standard textbooks that together have received 21,000 citations. His academic homepage is available online at http://www0.cs.ucl.ac.uk/staff/j.shawe-taylor/.

He has published research in neural networks, machine learning, and graph theory. Together with Nello Cristianini, he has written two books on the theory of support vector machines and kernel methods: An Introduction to Support Vector Machines (N.C. and J. S.T., Cambridge University Press, 2000) and Kernel Methods for Pattern Analysis (J. S.T and N.C., Cambridge University Press, 2004).