Speaker: Dr. Mahlet Tadesse

Time: March 4, 2022 04:00 PM Central Time (US and Canada)

Abstract: Identifying latent classes and component-specific relevant predictors can shed important insights when analyzing high-dimensional data. In this talk, I will present methods we have proposed to address this problem in a unified manner by  combining ideas of mixture models and variable selection in different contexts. In particular, I will discuss (1) a bi-clustering approach that allows clustering on subsets of variables by introducing latent variable selection indicators in finite or infinite mixture models, (2) an integrative model to relate two high-dimensional datasets by fitting multivariate mixture of regression models using stochastic partitioning, and (3) a mixture of regression trees approach to uncover homogeneous subgroups and their associated predictors accounting for non-linear relationships and interaction effects. I will illustrate the methods with various genomic applications.

Bio: Dr. Mahlet Tadesse is Professor and Chair of the Department of Mathematics and Statistics at Georgetown University. She is an elected member of the International Statistical Institute and an elected fellow of the American Statistical Association. Her research focuses on the development of statistical and computational tools for the analysis of high-dimensional data with an emphasis on -omic applications.