David Crevillen Garcia

Research Fellow, University of Warwick

I am a Research Fellow at the University of Warwick. I joined the Warwick Centre for Predicting Modelling in 2016. I obtained my PhD in Applied Mathematics and Statistics from The University of Nottingham in 2015. During my thesis, I developed various statistical and computational techniques for performing uncertainty quantification in computationally expensive models of carbon capture and storage processes.

I have an established interest in the relationship between machine learning and mathematical models, in particular for those involving environmental fluid mechanics. My primary area of research has been the development of new techniques for uncertainty quantification in stochastic models, this includes model dimensionality reduction and surrogate modelling, with particular focus in Gaussian process emulators. I have also investigated multilevel Monte Carlo and quasi-Monte Carlo methods for uncertainty quantification in flow and transport in random porous media.

I joined the Micro & Nano Fluids Group in January 2019 and my work consists in investigating the application of machine learning to the prediction of crowd and traffic motion in modern and future urban environments. More precisely, I work on developing and applying appropriate machine learning and uncertainty quantification techniques to a) make city-scale simulation computationally realisable; b) use real-time data to improve predictive capability of crowd/traffic models and to quantify uncertainty in model outputs.

More information about my past research can be found at https://warwick.ac.uk/fac/sci/wcpm/people/dcrevillen/

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After the success of the previous International Conference on Physics, Mathematics and Statistics (I

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