I work at problems at the interface of statistics and genomics. For example, I have long been interested in how the genomic data of individuals can inform researchers about the underlying genealogical relationships. These relationships are a key latent variable in the study of complex disease traits. In my research, I aim to combine fundamental genomics concepts such as identity by descent and gene pathways with innovations in statistical computing such as Markov chain and sequential Monte Carlo approaches. I am also interested in exploratory visualization tools which enable investigators to picture how the health effect of an environmental factor is modified by genetic background in families. Recently, I have started to work with neuro-imaging experts on the challenging problem of integrating three-dimensional scanning with genome-wide variant data. As a statistician, my focus is on developing analytic tools to uncover patterns in data, while accounting for random variation. Much of the research involves Bayesian modelling of complex data structures and so has a strong computational component. More info can be found by following the links to the left.