Topological learning for spatial data in the life sciences

Topological data analysis (TDA) has been successfully applied to study life sciences data — often high-dimensional, noisy, and heterogeneous. In this talk, I will present recent applications of TDA in combination with machine learning to spatial data, drawing on both synthetic and real-world examples. I will introduce techniques from relational TDA that we develop to encode spatial heterogeneity of multispecies data, i.e. datasets with multiple subtypes of data points. These approaches can reveal meaningful biological patterns and integrate naturally with modern machine learning methods, such as graph neural networks (GNNs). I will discuss how combining relational TDA with GNNs can enhance performance and provide deeper insights into spatially structured data. Topological data analysis (TDA) has been successfully applied to study life sciences data — often high-dimensional, noisy, and heterogeneous. In this talk, I will present recent applications of TDA in combination with machine learning to spatial data, drawing on both synthetic and real-world examples. I will introduce techniques from relational TDA that we develop to encode spatial heterogeneity of multispecies data, i.e. datasets with multiple subtypes of data points. These approaches can reveal meaningful biological patterns and integrate naturally with modern machine learning methods, such as graph neural networks (GNNs). I will discuss how combining relational TDA with GNNs can enhance performance and provide deeper insights into spatially structured data.