This project is focused on structured high dimensional regression with the term structure meant to distinguish the methods from other methods such as l1 minimization methods. Generally speaking, this structure is assumed a priori and is chosen on the basis of finding an interpretable solution to a regression problem. In this project, structure often refers to spatial structure found in areas of application such as neuroimaging or astronomical data. The project has two principal goals. First to develop scalable, flexible algorithms and software implementations for fitting such structured models. Secondly, to understand the statistical performance of such models as well as the algorithms used to fit such models.
The results of the research proposed in this project will allow researcher in the field of neuroscience to improve neuroscientists' ability to predict behavior based on fMRI or other spatio-temporally structured data.
