Research

My research interests concern Bayesian methods and their applications. Most of my work to date has been focused on Bayesian clustering models, in particular Dirichlet process clustering and conjugate agglomerative hierarchical clustering. A related area of interest is also the search and representation of uncertainty in the partition space. I am currently also conducting research on several developments for spatial-temporal models and survival response models. I have worked and continue to work in very diverse areas of application including genetics, epidemiology, road traffic accidents, football, environmental health and cardiac electrophysiology.

Clustering methods

Spatio-temporal modelling

Bayesian modelling

Computational Statistics

Data mining and machine learning

Applications

 

In the last few years I have focused on Dirichlet process Bayesian clustering, which have led to the development of the R package PReMiuM.

See also my publication list and software.