Bridging Topology and Machine Learning for Data Science

Topological data analysis captures connectivity, loops and multiscale organization that can be obscured by assuming data occupy ordinary Euclidean space. The workshop connects this mathematical viewpoint with the need for interpretable, robust learning systems. Mathematicians, computer scientists and researchers in chemistry, biology and materials science will examine theory, computational methods and scientific applications. Discussions link mathematical frameworks, efficient algorithms and practical datasets, with the goal of developing reliable, scalable methods and connecting early-career researchers across these communities.