Statistical inference for computer experiments

My main research interests lie in the field of Bayesian statistics and its application. In particular, I am interested in the statistical challenges posed by computer models.

David Cox said in an interview that the 'challenge [...] for an academic statistician, is to be involved in several fields of application in a non-trivial sense and combine the stimulus and the contribution you can make that way with theoretical contributions that those contacts will suggest' and this captures perfectly my research strategy: to be involved in several application areas and work on interesting methodology as it arises.

I currently have funding to work on the following research projects:

• Scaling Cardiac Biomechanics Digital Twins for Personalised Medicine. A four-year EPSRC project to develop digital twins of patients’ hearts (2023-2027).
• In-Procedure Personalized Atrial Digital Twin to Predict Outcome of Atrial Fibrillation Ablation. A four-year EPSRC project to develop predictive tools to help cardiologists treat patients suffering from atrial fibrilation (2022-2026).
• Selecting Rhythm or Rate Control in Patients with both Atrial Fibrillation and Heart Failure a five year British Heart Foundation funded project that seeks to determine the root disease in cardiac patients.
• Greenland Ice Sheet and sea-level response under climate change from AD 1600 to 2100 (2024-2026). A two-year project that aims to better understand Greenland ice sheet changes at the multi-century timescale.
• Mechanistic Model Misspecification (2018-2023). An EPSRC Industrial CASE award from Microsoft Cambridge UK funding the PhD project of Ines Krissane.

Previous funded projects include:

• Physically-informed probabilistic modelling of air pollution in Kampala using a low cost sensor network (2020-2022). A two-year EPSRC/GCRF funded project where we aim to build aspects of fluid behaviour into Gaussian processes to better make predictions about air pollution.
• Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models. A four-year EPSRC project that aims to predict, with uncertainty, which patients being treated for atrial fibrillation will go on to suffer atrial tachycardia (2017-2022)
• Enhancing Machine Learning with Physical Constraints for Predicting Microstructure Evolution. A two-year EPSRC project that aims to combine the observation-based empiricism of machine learning with the fundamental-based conceptualism of physics (2018-2020).
• Plastics: Redefining Single-Use. An EPSRC project looking at ways we can move away from the use of single-use plastics (2019-2020).
• Improving Gaussian process emulation using calibration-aware dimension reduction. Industrial funding from Total for the PhD project of Valentin Breaz (2018-2022).
• Reconstructing the past and predicting the future: what can ice-cores tell us about climate?. This is Fiona Turner’s PhD project, funded by the Grantham Centre for Sustainable Futures. and supported by a CASE award from the British Antarctic Survey in Cambridge (2016-2020).
• Tractable equations of state for carbon dioxide mixtures in carbon capture, transport and storage: algorithms for automated generation and optimisation, tailored to end-users (UKCCSRC-C1-22) (2014-2015), funded by EPSRC via the UK Carbon Capture and Storage Research Centre (UKCCSRC).

Academic service: I am currently an associate editor of the SIAM Journal of Uncertainty Quantification, have been an area chair for NeurIPS every other year since 2014, and have co-organised the Gaussian Process Summer School since 2015. I was previously the external examiner for the MSc in Statistical Science at the University of Oxford, and Chair of the Environmental Statistics section of the Royal Statistical Society.