Developing the theoretical foundations underpinning the development of digital twins for complex engineering systems.

Developing a statistical model of how environmental factors influence long-term aero-engine performance.

Developing efficient Stein-based discrepancies for inference and assessment.

Spatiotemporal modelling of propagation of pressure and temperatures throughout an aero-engine

Developing data-driven decision support systems to enable faster, more effective decision making for nuclear engineering operations

Speeding up MCMC for scalable Bayesian Inference.

Principled approaches to coarse graining for stochastic systems.


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Bayesian inference problems require sampling or approximating high-dimensional probability distributions. The focus of this paper is on …

Our investigation raises an important question that is of relevance to the wider turbomachinery community: how do we estimate the …

In this second part of our two-part paper, we provide a detailed, frequentist framework for propagating uncertainties within our …

While likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric …

When maximum likelihood estimation is infeasible, one often turns to score matching, contrastive divergence, or minimum probability …

Research Group

Postdocs, PhDs and MScs


2019- : Pranay Seshadri (Research Fellow at Imperial)

2019- : Jonathan Cockayne (PDRA at ATI)

2019- : Henrique Hoeltgebaum (PDRA at ATI)

2018-2019 : Nikolas Nusken (PDRA at ATI)

PhD Students

2019- : George Wynne (cosupervised with Mark Girolami and F.X. Briol)

2019- : Enrico Crovini

2019- : Yanni Papandreou

MSc Students

2019: Enrico Crovini

2019: Yanni Papandreou


Imperial College London

Statistics Section

  • M5MS01 Probability for Statistics (Autumn’18)
  • M1R Project Coordinator (Summer ‘19)

University of Sussex

Department of Mathematics

  • 865G1, Monte Carlo Simulations (Summer ‘18)
  • G5096, Algebra (Autumn ‘16)

Imperial College London

Applied Mathematics Section

  • M4A44 Computational Stochastic Processes (Winter ‘16)
  • M4A42 Applied Stochastic Processes (Autumn ‘14)

University of Oxford

Department of Mathematics

  • C6.4b Class Tutor for Stochastic Modelling of Biological Processes (Hilary Term, ‘14)