John Shawe-Taylor (Chairman) has contributed to a number of fields ranging from mathematics of graph theory through cryptography to statistical learning theory and its applications. In graph theory central contributions were to the classification of cubic distance regular graphs, while in cryptography his RSA prime generation algorithm was incorporated into an international standard. However, his main 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 to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines. His work in this area has progressed on several parallel fronts: the refinement of the fundamental statistical results that underpin the approach and can be extended to related algorithms and data analysis techniques; the mapping of these applications onto novel domains including work in computer vision, document classification and brain scan analysis; and the extension of learning to improving the representations that are created for learning on different application domains. He has also been instrumental in assembling a series of influential European Networks of Excellence (initially the NeuroCOLT projects and later the PASCAL and PASCAL2 networks). The coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing. He has also coordinated two influential European research projects, the KerMIT project and most recently the CompLACS (Composing Learning for Artificial Cognitive Systems). Reviews have frequently commented on his effective leadership in both network and other project coordination. John is also UNESCO Chair in Artificial Intelligence.