Assistant professor in the Departments of
Statistics and of
Botany, at the
University of Wisconsin-Madison.
Member of the CALS
statistical consulting lab.
Maitre de Conférence at the
University of
Paris XI, on leave (détachement).
| Office: | 1208 Medical Science Center
(Statistics), 341 Birge Hall (Botany) |
| Phone: | Botany: (608) 262 6820, Statistics: (608) 262 3901, fax: (608) 262 0032 |
| email: | ane at stat.wisc.edu |
| Mailing address: | Department of Statistics University of Wisconsin-Madison Medical Science Center 1300 University Ave. Madison, WI 53706-1532 |
My main research interest is the area of statistical inference in molecular evolution. I am interested in model selection, either based on an information theoretic approach, or on penalized likelihood approaches. 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.
I am also interested in developing and detecting good models of molecular evolution, such as the covarion model. Covarion drift and/or covarion shift is detected in an increasing number of analyzes, and it becomes more and more accepted that various DNA sites have evolved at various speed across their history. This variation in speed of evolution is called heterotachy, when different DNA sites choose to vary in different ways. There is a growing body of literature on the consistency of likelihood-based methods that ignore heterotachy when heterotachy is actually present, and a growing number of models and ways to estimate the amount of heterotachy.
More recently, I have been interested in using phylogenetic trees to analyze trait evolution, using the so-called '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 effective degree of freedom for parameters in these models.
