Title: Representation Theory and Topological Data Analysis
Abstract: Persistent homology, the flagship tool of topological data analysis (TDA), analyzes the shape of a data set by constructing a filtered topological space and applying homology with field coefficients. The isomorphism type of the resulting diagram of vector spaces is fully described by a simple invariant called a barcode.
Often, a single filtered space does not adequately capture the structure of interest in the data. One is then led to consider multiparameter persistence, which associates to the data a space equipped with a multiparameter filtration. In contrast to the 1-parameter case, the homology of this object has wild representation type. Hence, there is no fully satisfactory definition of a barcode in the multiparameter setting. This makes extending the 1-parameter persistence theory and methodology to multiple parameters an interesting and challenging problem.
In the last several years, multiparameter persistence has become one of the most active areas of research within TDA, with exciting progress on several fronts. These talks will introduce this subject and survey some of this progress. We will aim to understand the difficulties created by the “wildness” of multiparameter persistence, the ideas that have been developed to circumvent these difficulties, and the challenges that remain. Among other topics, we will cover stability theory, signed barcodes, and the computational and practical aspects.
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Here are the slides of each presentation in this mini-course: