Topology studies the structure of shapes. Topological data analysis (TDA) is the
study of the shape of (large, high-dimensional, and noisy) data. Often, in
TDA, the data set is transformed into a concise descriptor, such as a
persistence diagram or a dendogram, which can then be used to (indirectly)
compare or classify the data sets. In this talk, we will define a persistence
diagram and confidence sets for persistence diagrams. Then, we will discuss how
we can use these confidence sets to perform statistical hypothesis testing, and
provide a few examples of where we've applied (or are applying) these methods.
The examples will include road network analysis, prostate cancer diagnosis, and the study of matter throughout the universe.