This talk addresses the following three topics: positive semidefinite (psd) matrix completions, universal rigidity of frameworks, and the Strong Arnold Property (SAP). We show some strong connections among these topics, using semidefinite programming as unifying theme. Our main contribution is a sufficient condition for constructing partial psd matrices which admit a unique completion to a full psd matrix. Such partial matrices are an essential tool in the study of the Gram dimension $gd(G)$ of a graph $G$, a recently studied graph parameter related to the low rank psd matrix completion problem. We show some connections with Connelly's sufficient condition for the universal rigidity of Euclidean frameworks. We also give a geometric characterization of psd matrices satisfying the Strong Arnold Property in terms of nondegeneracy of an associated semidefinite program and as the extreme points of a certain spectrahedron. These characterizations are used to establish links between the Gram dimension $gd(G)$ and the Colin de Verdiere type graph parameter $\nu^=(G)$.