At TDA-Brain, we develop topological learning methods: principled statistical and machine learning frameworks that incorporate topological representations directly into modeling pipelines. Our work integrates persistent homology, graph topology, and geometric representations with modern machine learning to analyze complex, high-dimensional neuroimaging data. We focus particularly on multimodal data—including structural MRI, functional MRI, diffusion imaging, and brain connectivity networks—where topology provides a natural language for describing brain organization across spatial and temporal scales.
Website updated on 3/10/2026