Abstract: The famous distance geometry problem (DGP) asks to reconstruct the geometry of a point set from a subset of interpoint distances. In the unlabeled DGP the goal is the same, alas without knowing which distances belong to which pairs of points. Both problems are of practical importance: the DGP models sensor network localization, clock synchronization, and molecular geometry reconstruction from NMR data, while the unlabeled DGP models room geometry reconstruction from echoes, positioning by multipath, and nanostructure determination by powder diffraction. The unlabeled DGP in 1D is known as the turnpike reconstruction problem, and it was one of the first techniques used to reconstruct genomes. The mathematics of the unlabeled DGP is nowhere near as well-understood as that of the DGP. I will introduce the unlabeled DGP, explain how it arises in various applications, discuss connections with phase retrieval and explain our approach based on empirical measure matching. Along the way I will point out numerous theoretical and algorithmic aspects of the problem that we do not understand but we wish we did.