FP7   EU

University College London (UCL) has recently established a Centre for Computational Statistics and Machine Learning that spans the Departments of Computer Science, Statistics and the Gatsby Computational Neuroscience Unit. The Centre brings together expertise in theoretical and practical aspects of machine learning, statistics, engineering and neuroscience. It therefore provides a forum for dialogue across the traditional discipline divides. It has ten faculty staff with a number of research staff and postgraduate students. John Shawe-Taylor is the Director of the Centre working within the Computer Science Department, while Mark Herbster is a member of the Centre and is also in Computer Science.

Prof. John Shawe-Taylor obtained a PhD in Mathematics at Royal Holloway, University of London in 1986. He subsequently completed an MSc in the Foundations of Advanced Information Technology at Imperial College. He was promoted to Professor of Computing Science in 1996. He has published over 150 research papers. He moved to the University of Southampton in 2003 to head the ISIS research group. He will assume the Directorship of the Centre for Computational Statistics and Machine Learning at University College, London in July 2006.
He has coordinated a number of European wide projects investigating the theory and practise of Machine Learning, including the NeuroCOLT projects. He is currently the coordinator of a Framework VI Network of Excellence in Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL) involving 56 partners. He is co-author an 'Introduction to Support Vector Machines', the first comprehensive account of this new generation of machine learning algorithms and of a second book on 'Kernel Methods for Pattern Analysis' published in 2004.
Dr. Mark Herbster is a lecturer in Computer Science and Director of the MSc in Intelligent Systems at University College London. His research focus is machine learning with particular interests in Bioinformatics, kernel methods, and online learning. He has researched into the analysis and algorithmic implementation of the online use of expert advice, something that will be directly relevant to the proposed project. His recent research has focused on semi-supervised learning, in particular when the data may be naturally represented as a graph such as for example web pages and their interconnecting links. For this model of learning he has developed a number of algorithms with corresponding performance guarantees in both the convergence rate and in the quality of the predictions. He is currently a participant in an international project on multi-task learning and is an active participant of the PASCAL network.
Dr. David R. Hardoon is a research fellow at the University College, London. He is currently working on projects that are focused on learning the structure of music, medical analysis, multilingual and multi-modal integration. He has a keen interest in multi-view learning, kernel methods, regression, and sparsity. He has previously worked on various research projects in the fields of Taxonomy, Image analysis, classification and content based retrieval systems. David received his first class BSc Hons. in Computer Science with Artificial Intelligence from the Royal Holloway, University of London and his PhD in Computer Science in the field of Machine Learning from the University of Southampton. He has also received the PhD PASCAL label award from his active participation in the PASCAL network.
Dr. Zakria Hussain is a research fellow at the University College, London. After receiving his BSc in Mathematics and Computer Science and MSc in Machine Learning from Royal Holloway, University College London in 2002 and 2003 respectively, he embarked on a PhD in machine learning at the University of Southampton, and is completing it at the University College, London. His thesis is on the analysis and design of sparse machine learning algorithms such as the branch and bound set covering machine, kernel matching pursuit, sparse kernel principal components analysis and sparse kernel canonical correlation analysis. His current research interests are in kernel methods, sparsity using matching pursuit and direct application of learning theory bounds.
Dr. Cédric Archambeau is a research fellow at University College, London. His research activities focus on statistical machine learning, with particular interests in stochastic process models, pattern recognition and Bayesian inference in general. His applied research is directed towards information retrieval, data assimilation, stochastic control, computational biology and natural language processing. Cédric received the Electrical Engineering degree and the PhD in Applied Sciences from the University of Louvain, Belgium, respectively in 2001 and 2005. He was member of both the Machine Learning Group and the Crypto Group. He has previously worked in various European and UK funded research projects, including OPTIVIP (Optimisation of a Visual Implantable Prosthesis), SCARD (Side Channel Analysis Resistant Design Flow) and VISDEM (Variational Inference in Stochastic Dynamic Environmental Models).
M.Sc. Louis Dorard is a research student in Machine Learning at University College London.