Projects

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.

Publications

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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

Postdocs

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

Teaching

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)