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.
Data mining and machine learning 
Clustering methods 
Applications 
Spatial and spatio-temporal modelling 
Bayesian modelling 
Computational Statistics
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.
Current PhD students
Marco Gramatica (2018- ; co-supervised with Prof Peter Congdon)
Polychronis Kemos (2018- ; co-supervised with Prof Nick Croft)
Postdoctoral fellows
Susana Conde (2018-2019, currently at the Alan Turing Institute)
PhD Graduates
Xi Liu (2014-2018; co-supervised with Prof Keming Yu; currently a Data Scientist at Kennedys)