With Darius Savory, supervised by Paul Northrop at University College London, we're looking at models for covariate non-stationarity in extreme value analysis.
With Paul Northrop and David Randell, we've contributed a chapter on non-stationary extreme value modelling to the collection "Extreme value modeling and risk analysis" which appeared in book form in 2016.
With David Randell and Michael Goldstein at the University of Durham's School of Mathematical Sciences we've developed optimal inspection methods for large industrial systems. The project applies Bayes linear methods to adjust beliefs about the integrity of large process systems. We wrote an article (here) for the Journal of Risk and Reliability illustrating the methodology in application to inspection design for offshore oil and gas facilities. A second article on variance structure learning is here .
With Matthew Jones we're currently trying to apply the methodology to optimal design in remote sensing problems also. A recent article is here. We are currently working on the optimal sequential design problem.
Pictish symbols and early medieval inscriptions
Ever wondered if ancient symbols have the characteristics of language?
Here's a recent article and a follow-up study .
It may also be possible to associate medieval inscriptions (like Pictish and Irish Ogam, Welsh Latin and Scandinavian Runes)
with modern language lexicons. Read more here.
Blowin in the wind
Whether you’re interested in monitoring greenhouse gas emissions, locating airborne viruses, or just finding a mate by detecting individual pheromone molecules
- as moths do - optical sensors, gas dispersion and statistical inversion are key fields.
Provided you can measure trace concentrations well and understand how the atmosphere mixes as it moves,
the answer to “who is emitting what, how much and where” - is literally Blowin in the wind.
Read more here and here.
Can you retrieve a picture of an object through correlated measurements of a projected light using a single pixel camera and ghost imaging?