Bundle-theoretic methods in data science

Many tasks in data science, including distributed modeling,
dimensionality reduction, data alignment and coordinatization, can be
phrased as local-to-global inference problems with well-defined
topological obstructions. The goal of this talk is to describe several
of these constructions, their applications to specific machine learning
problems, and the emergent theoretical/algorithmic challenges in
geometric and topological data analysis.