Representing data via vectors and matrices and optimizing spectral objectives such as determinants, and traces of naturally associated matrices is a standard paradigm that is utilized in multiple areas including machine learning, statistics, convex geometry, location problems, allocation problems, and network design problems. In this talk, we will look at many of these applications with a focus on the determinant objective. We will then give algorithms for these problems that build on classical matroid intersection algorithms.