Topology as an Organizing Principle for Biomedical Data

In recent years, adaptations of topological constructions and methods to the analysis of high dimensional and large data sets have been shown to be of a great deal of value.  They are particularly appropriate for data in the biomedical domain, where the presence of large amounts of noise, diverse data types, and a lack of underlying theory call for methods which have a great deal of robustness to changes in underlying metrics and data models.  In this talk we will discuss some of these methods, and present some interesting examples.