Kathryn Hess - Mathematical Foundations of AI Conference 2026

Kathryn Hess | Ring Scores: Quantifying Circularity in Data How can mathematics identify circular patterns hidden within complex datasets, and what might these patterns reveal about biological processes? Kathryn Hess, Professor of Mathematics and Life Sciences at EPFL in Switzerland, explores how algebraic topology can be used to detect and measure circular structures in data. Her talk introduces "ring scores", mathematical measures that quantify how closely a dataset resembles a circle. These provide a new approach to analysing data, complementing conventional methods that identify clusters or groups of similar observations. Hess explains the mathematical principles behind ring scores, including their relationship to persistent homology, and considers their potential application to single-cell biological data, where circular patterns may reveal important processes such as cell cycles. This talk was recorded at the Mathematical Foundations of AI Conference 2026, organised by the Erlangen AI Hub at the University of Oxford, 1–3 September 2026. Find out more: https://erlangenhub.ox.ac.uk/ #ArtificialIntelligence #Mathematics #Topology #ErlangenAIHub