New methods for the simultaneous inference of graphical models and covariates effects in the Bayesian framework will be discussed. I will consider regression settings where the interest is in the estimation of sparse networks among a set of primary variables, and where covariates may impact the strength of edges. The proposed models utilize spike-and-slab priors to perform edge selection, and Gaussian process priors to allow for flexibility in the covariate effects. Efficient and scalable algorithms for posterior inference will be employed for the estimation of the models. Simulation studies will demonstrate how the proposed models improve on the accuracy of existing methods, in both network recovery and covariate selection. I will show applications of the proposed models to neuroimaging and genomic datasets.

2:30-3:00pm: talk for graduate students (note the different time)