My main research area is the application of modern statistical machine learning methods to create high-quality models of nonlinear dynamical systems (e.g. aircraft, yachts, cars) and quantify the uncertainty present in those models.
Prior to starting my PhD, I worked as an engineer at the McLaren-Mercedes Formula 1 Team (in Woking, UK) and at Airbus (in Toulouse, France). During these years I have tackled problems in areas such as control theory, design of experiments, optimization and scientific computing. I have relied heavily on mathematical models and I have grown an appreciation for the key importance that uncertainty has when dealing with real world systems. How accurate is a model? How informative is the data available? How much uncertainty do we have in our predictions? I believe that the field of statistical Machine Learning provides an excellent practical framework to answer these questions and I am very interested in its introduction into engineering branches traditionally linked with deterministic approaches to modelling.Here is a version of my CV.
* Bayesian Nonparametric Nonlinear System Identification, Reglerteknik Monday Meeting, Linköping University, 10 June 2013. [pdf]
* Learning to Control: State Estimation, Research Talk, Cambridge, 30 April 2012. [pdf]
* Statistical Inference for Engineers, Seminar, Maranello, 19 March 2012. [pdf]
* An Overview of Control Theory, Tutorial, Cambridge, 12 January 2012. [pdf]
* Practical Bayesian Nonlinear System Identification with Autoregressive Models [code]
| rf342 -at- cam.ac.uk
Cambridge University Engineering Department
Cambridge, CB2 1PZ