Stability is a key property of topological invariants used in data analysis and motivates the fundamental role of metrics in persistence theory. This talk reviews noise systems, a framework for constructing and analysing metrics on persistence modules, and shows how a rich family of metrics enables the definition of metric-dependent stable invariants. Focusing on one-parameter persistence, we discuss algebraic Wasserstein distances and the associated Wasserstein stable ranks, invariants that can be computed and compared efficiently. These invariants depend on interpretable parameters that can be optimised within machine-learning pipelines. We illustrate the use of Wasserstein stable ranks through experiments on synthetic and real datasets, showing how different metric choices highlight specific structural features of the data.

Dimension 4 is the first dimension in which exotic smooth manifold pairs appear — manifolds which are topologically the same but for which there is no smooth deformation of one into the other. On the other hand, smooth and PL manifolds (manifolds which can be described discretely) do coincide in dimension 4. Despite this, there has been comparatively little work done towards gaining an understanding of smooth 4-manifolds from the discrete and algorithmic perspective. The aim of this talk will be to give a gentle introduction to some of the tools, techniques, and ideas, which inform a computational approach to 4-manifold topology.