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An introduction to Persistent Homology

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If you have a question about this talk, please contact Alexis Marchand.

Topological Data Analysis (TDA) aims at extracting and inferring information about the shape of datasets. One of its most successful tools is Persistent Homology which uses algebraic topology to uncover topological features of point clouds at various scales. It has been applied in a wide range of domains from biology to sensor networks and material science. While the theory of single-parameter Persistent Homology is well established, its multiparameter version is an active area of research.

After introducing Persistent Homology, we will see some examples of applications and we will discuss the challenges arising in the multiparameter setting.

This talk is part of the Junior Geometry Seminar series.

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