Stephen Roberts is Professor of Machine Learning in the Department of Engineering Science.
Stephen’s interests lie in methods for intelligent data analysis in complex problems, especially those in which noise and uncertainty abound. He has successfully applied these approaches to a wide range of problem domains including astronomy, biology, finance, sensor networks, control and system monitoring.
Stephen's main area of research lies in machine learning approaches to data analysis. He has particular interests in the development of machine learning theory for problems in time series analysis and decision theory. His recent research has focused on non-parametric Bayesian models for multi-sensor data fusion, system optimisation and network analysis. Particular emphasis is placed on the real-world applications of advanced theory and over many years he has applied these statistical methods to diverse problems in astrophysics, biology, finance and engineering as well embedding them in a variety of commercial and industrial settings.
His current major interests include the application of intelligent data analysis to huge astrophysical data sets (for discovering exo-planets, pulsars and cosmological models), biodiversity monitoring (for detecting changes in ecology and spread of disease), smart networks (for reducing energy consumption and impact), sensor networks (to better acquire and model complex events) and finance (to provide timeseries and point process models and aggregate large numbers of information streams).
He leads the Machine Learning Research Group. He is the Director of the Oxford-Man Institute and previously served as Director of the EPSRC Centre for Doctoral Training in Autonomous, Intelligent Machines and Systems (AIMS).
In addition to his tutorials, Stephen currently lectures on Vector Algebra and Calculus, Signal Processing and Advanced Probability Theory