Abstract: Multi-parameter persistent homology is an increasingly popular tool of topological data analysis. The multi-parameter setting creates theoretical and computational challenges compared to the one-parameter case, but it also has a larger class of potential applications. We show that ideas from the theory of multi-parameter persistent homology can be used to construct a novel method of density estimation with appealing theoretical properties. We will not assume familiarity with persistent homology or density estimation. This is joint work with Luis Scoccola.