(734)763-5238
Applications: Bioinformatics, Environmental Sciences, Political Science, Social Sciences Methodologies: High-Dimensional Data Analysis, Optimization, Statistical Inference Relevant Projects:

NSF

Yves Atchade

Associate Professor, Statistics, College of Literature, Science, and the Arts

My current research explores the possibilities and limits of Markov Chain Monte Carlo (MCMC) methods in dealing with posterior or quasi-posterior distributions that arise from high-dimensional Bayesian (or quasi-Bayesian) inference in regression and graphical models. I also have some interests in optimization, and these revolve around the use of stochastic methods: whether (and how) the use of stochastic methods can help tackle large scale optimization problems of interest in statistics. I also have interests in the use of remote sensing data to study social and environmental issues in Africa.