Professor Chris Oates

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chris oates

Position

Data-Centric Engineering Group Leader, and Turing Fellow

Partner Institution

Newcastle University

Links

http://oates.work/
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Research areas

Non-parametric & semi-parametric methods Monte Carlo methods Causality Numerical analysis

Bio

Chris is a Professor of Statistics at Newcastle University and Group Leader for the data-centric engineering programme at the Turing.

Chris’ research aims to develop probabilistic numerical methods for simulation-based risk management. The use of computers to simulate physical governing equations introduces numerical and discretisation errors; his research aims to account for these with statistical methods, to provide a proper quantification of risk for decisions that are based on simulation output.

Event speaker

Turing Research

Organiser

Turing Publications

research_publications

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems

This paper develops meshless methods for probabilistically describing discretisation error in the numerical solution...

Cockayne J, Oates CJ, Sullivan T, Girolami M (2017) Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Ed. Geert Verdoolaege, AIP Conference Proceedings, 1853:060001.