The diffusion map is a popular new technique for data parametrization and dimensionality reduction that has been developed by Coifman & Lafon (2006) and Lafon & Lee (2006). The diffusion map has the powerful ability to uncover simple structure in complicated, high-dimensional data. I briefly introduce the diffusion map process and then discuss in detail how and why we applied it to astronomical data. Specifically, our goal was to model the different populations of stars which comprise galaxies in the local Universe. While I will focus on the scientific application of diffusion mapping in this talk, I will also address real-world challenges we faced in this cross-discipline project. Examples include deficiencies in the underlying scientific models, algorithmic constraints on the statistical analysis, and computational constraints due to size of the dataset.