YouTube

Talk on estimating return values (and why you shouldn't!) here. Talk on environmental extremes (in Welsh) here.

Data science

With Jeremy Sellier and Matthew Jones, supervised by Petros Dellaportas at University College London, we're are looking at point process models and reinforcement learning.

More extremes

Kevin Ewans and I contributed a chapter on extreme ocean conditions for the collection entitled "Ocean wave dynamics" edited by Ian Young and Alex Babanin of the University of Melbourne, which appeared in 2020.

Evandro Konzen and Claudia Neves at the University of Reading and I made a comparison of applied parametric (i.e. likelihood-based) and non-parametric inference for extremes.

With Ed Mackay at the University of Exeter's Penryn Campus, we're seeking to provide straightforward engineering approaches incorporating recent developments in non-stationary and conditional extremes.

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 Ryota Wada and Takuji Waseda at the University of Tokyo, we're working on useful extreme value methods for small samples of tropical cyclones offshore Japan. Here are articles on the LWM (the likelihood weighted method) and STM-E (the spatio-temporal maximum - exposure model).

With Rob Shooter (UK MetOffice) and Emma Ross (Shell), we're examining extreme storms using satellite altimetry and scatterometry, and have provided software on GitHub for spatial conditional extremes analysis (see bottom of this page).

Statistical planning and uncertainty analysis

With David Randell and Michael Goldstein at the University of Durham's School of Mathematical Sciences we 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 apply the methodology to optimal design in remote sensing problems also. See here. We've recently written an article on optimal sequential design here, and are now working on Bayes linear analysis for ordinary differential equations, and higher-order Bayes linear.

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.

Airbourne sensing

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.

With Clay Roberts, supervised by Oli Shorttle and Kaisey Mandel at the Institute of Astronomy in Cambridge, Matthew Jones and I are looking into methods for probabilitistic inversion in environmental sensing. Read more here.

Ghost imaging

Can you retrieve a picture of an object through correlated measurements of a projected light using a single pixel camera and ghost imaging?

GitHub

You can find software for various aspects of extreme value analysis on my GitHub page.