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.

The Casa Matemática Oaxaca (CMO) in Mexico, and the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, are collaborative Canada-US-Mexico ventures that provide an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station in Banff is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT). The research station in Oaxaca is funded by CONACYT.