Research Interests

My main research interests are the area of statistical inference for molecular evolution and for trait evolution. This work invoves using stochastic processes (discrete Markov processes or continuous diffusions), and developing tools for model selection, Bayesian inference, and new models that capture enough realism, but that remain computationally feasible in our Big Data era.

I also got interested in a number of various biological applications through statistical consulting across campus, like food science and veterinary science.

Molecular Evolution

One of my aim is to detect what groups of genes share the same genealogy, and to draw inference on the distribution of genealogies across the genome. This area involves statistical issues of model selection, hierarchical modelling of species genealogies and gene genealogies, and it also involves computational challenges. Indeed, molecular data become available faster than appropriate methods of analysis. Development of these methods is currently funded by the NSF for application to the tree of Enterobacteriaceae, and to study reticulate evolution and species delimitation in baobabs. See also these earlier awards, on discordance patterns, and monocot AToL.

Trait Evolution

More recently, I have been interested in using phylogenetic trees to analyze trait evolution, using the phylogenetic 'comparative methods'. Data collected on species (or related individuals) do not form a random sample because they lack independence: sister species are expected to have similar traits. Such samples actually show a high level of dependence, and there need to be adapted statistical methods of analysis. I am especially interested in the statistical properties of estimation methods, in adapted model selection procedure, and the effective degree of freedom for parameters in these models. See this NSF project.