Analysis Seminar

Alex Chirvasitu, University of Buffalo

"Loosely embeddable metric spaces"

Embedding finite metric spaces isometrically into Hilbert spaces has elicited some interest outside of pure mathematics due to applications to fields like computer vision, machine learning, the structure of networks and other such areas.

In the talk I will introduce a weaker notion of embeddability motivated by the study of "quantum symmetries" for metric spaces and Riemannian manifolds. I will mention some results on the generic behavior of "most" compact metric spaces and list a number of open questions.

MC 5417