Variable Selection and Prioritization in Bayesian Machine Learning Methods
Where: Beaupre 105
When: Friday, October 21st, 4PM
A consistent theme of the work done in my lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. The central aim of this talk is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions and non-additive variation are of particular interest, we introduce a novel, interpretable, and computationally efficient way to summarize the relative importance of predictor variables. Methodologically, we present flexible and scalable classes of Bayesian feedforward models which provide interpretable probabilistic summaries such as posterior inclusion probabilities and credible sets for association mapping tasks in high-dimensional studies. We illustrate the benefits of our methods over state-of-the-art linear approaches using extensive simulations. We also demonstrate the ability of these methods to recover both novel and previously discovered genomic associations using real human complex traits from the Wellcome Trust Case Control Consortium (WTCCC), the Framingham Heart Study, and the UK Biobank.
Lorin Crawford is a Principal Researcher at Microsoft Research New England, and he holds a faculty position as an Associate Professor of Biostatistics at Brown University. His lab develops machine learning algorithms and statistical tools to understand how non-additive variation plays a role in complex traits and contributes to disease in diverse human populations. Some of his most recent work has landed me a place on Forbes 30 Under 30 list and recognition as a member of The Root 100 Most Influential African Americans in 2019. He has also been awarded an Alfred P. Sloan Research Fellowship and a David & Lucile Packard Foundation Fellowship for Science and Engineering.
Prior to joining both MSR and Brown, Dr. Crawford received his PhD from the Department of Statistical Science at Duke University and a Bachelor of Science degree in Mathematics from Clark Atlanta University.