About Me
I am currently working on a PhD in Machine Learning at the University of Cambridge. My supervisor is Carl Edward Rasmussen and my advisor is Jan Maciejowski.
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.Preprints
Talks
* 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]
Code
* Practical Bayesian Nonlinear System Identification with Autoregressive Models [code]
