How can we detect low dimensional structure in high dimensional data? Combining ideas from spectral graph theory, convex optimization, and differential geometry, I will describe two recently developed methods for analyzing high dimensional data that has been sampled from a low dimensional submanifold. These methods can be used to estimate the dimensionality of the submanifold and to derive faithful low dimensional representations of high dimensional data.