Here is a quick graphic view of my areas of research

Some key current areas of focus for statistical theory, methods and computational research include:

• Bayesian modelling of sparse multivariate structures in complex statistical models - sparse latent factor models and graphical modelling;
• Problems with large data sets and high-dimensional covariate spaces in regression and classification models for prediction, including highly structured mixture modelling for large data sets;
• Latent factor models, including applications in high-dimensional statistical models, multi-scale time series, and others;
• Graphical models, and related statistical and graph theory for graph structuring;
• Multivariate time series and state-space modelling with factor, graphical and non-parametric/non-linear structure;
• MCMC and sequential Monte Carlo (particle filtering) simulation methods for Bayesian computation;
• Large-scale model search and stochastic methods for evolutionary exploration of complex model spaces;
• Image modelling and tracking problems in dynamic imaging;
• GPU (graphics processing unit) computation for statistics.

• Large-scale portfolio studies in global investment management, and small-scale portfolios in mutual fund studies at the level of individual investors;
• Molecular profiling in cancer (and other areas) using genomic data, and statistical analysis for translation of laboratory-to-clinical studies;
• Gene expression analysis in biological pathway studies, - deregulation of oncogenic pathways, pathways related to environmental influences in cancer, and others;
• Parameter estimation and model evaluation using discrete-time dynamic stochastic models of dynamic cellular networks;
• Imaging methodology for single-cell fluorescent studies in systems biology;
• Statistical mixture modelling of large-scale spatio-temporal organisation of multiple cell types in immunology.