Multiparameter Persistent Homology (18w5140)


(University of Victoria)

Peter Bubenik (University of Florida, Mathematics)

(Princeton University)


Topological data analysis is a young field where we apply the tools of topology to see the coarse `shapes' of potentially large or high-dimensional data sets. Multi-parameter persistent homology is a relatively new thread of ideas, that allows for topological data analysis to be forgiving of `noise' in data sets. Being a young field, there are connections to an array of disciplines in mathematics as well as the sciences.

We are bringing together experts from within the field of topological data analysis, neighboring areas of mathematics and statistics, as well as users of topological data analysis for the purpose of data exploration and visualization. The aim is to further extend the machinery of multi-parameter persistent homology for the purpose of having an effective and pleasant apparatus for studying large data sets.
