Multiparameter Persistence: Theory and Applications (23w5010)


(University of Aberdeen)

(University of Southampton)

Daniela Egas Santander (Ecole Polytechnique Federale de Lausanne)

Katharine Turner (Australian National University)


Modern life is built on vast quantities of very complex data that exists in a great variety of forms, including numbers, text, images and video, graphs and networks, and many others. The data is only useful if we can access the information it contains. In recent years, topology-a branch of mathematics dedicated to the study of shape- joined statistics and machine learning as a key part of data science. A particular power of topology is its ability to visualise complex shapes and provide numerical characteristics to describe them. The topological approach has led to the discovery of a new class of breast cancers which are amenable to treatment, the discovery of new subtypes of Type II diabetes, new insights into the structure and function of the brain, the role of the shape and structure of the lungs in severe diseases and many others.

This workshop will concentrate on the development on new topological tools to capture the structure of data that depends on many parameters simultaneously. The meeting will provide an welcome opportunity to bring together many parts of mathematics to support topology in this work, and to discuss a new range of potential exciting applications.