Michael Woodroofe Lecture Series: Larry Wasserman, Topological Data Analysis

Topological Data Analysis (TDA) refers to a collection of techniques for extracting interesting features from point clouds and images. I will discuss two different approaches to TDA.  The first is persistent homology, which is a multiscale method for finding voids of different dimensions.  The second is ridge estimation which is aimed at finding high density filaments and walls.  This is joint work with members of TopStat (www.stat.cmu.edu/topstat).