Topology, Algebra, and Geometry in Data Science

Topology, Algebra, and Geometry in Data Science 2025 is a research conference at the intersection of pure mathematics and modern data science, dedicated to applying powerful abstractions from topology, algebra, and geometry to the challenges of understanding high-dimensional, complex data. The meeting reflects a growing recognition that mathematical structure provides the most principled foundation for data analysis.

Topics include topological data analysis, persistent homology, geometric deep learning, algebraic statistics, manifold learning, sheaf theory for data, graph neural networks grounded in algebraic topology, and applications to biology, materials science, and network analysis. Mathematicians, statisticians, and machine learning researchers who share a commitment to mathematical rigor will find this conference intellectually stimulating and practically relevant. Graduate students with backgrounds in pure mathematics who are drawn to data science applications will find an especially welcoming and mentorship-rich community.