Science draws on mathematics and provides a playground for mathematicians. We develop mathematics for living systems, combining theory, computation, models, and data. From molecules to tissues, living systems are rich in structure. We use the mathematics of shape, dynamics, and high-dimensional data to find it.

• Chalc — Persistent homology of chromatic alpha complexes
• desr — A Python library for nondimensionalizing differential equations and dynamical systems.
• muphasa — Software to compute presentations of multi-parameter persistent homology, developed by Matías R. Bender, Oliver Gäfvert, and Michael Lesnick. This fork of Muphasa aims to modernise the codebase and build system, and implement fast computation of persistence landscapes, in particular for spatiotemporal Rips trifiltrations. This fork was developed by Oliver Gäfvert, Katherine Benjamin, and Silviana Amethyst.
• PersForest — Python tools for studying how codimension 1 topological cycles evolve in point-cloud data. Compute codimension 1 cycle representatives at fixed scales or across scales, create quantitative summaries for comparison, and visualize or animate cycle representatives and barcodes.
• ph-knotted-proteins — Code and data for the paper "Homology of Homologous Knotted Proteins"
• PHyperRicci — Mathematical pipeline leveraging Persistent Homology and Unweighted Forman-Ricci Curvature to retrieve Knotting signature
• SampEuler — A Python package for computing Euler Characteristic Transforms (ECT), Smooth Euler Characteristic Transforms (SECT), SampEuler and related topological data analysis tools for geometric simplicial complexes.
• scdiv — Tissue heterogeneity scores for transcriptomics, without cell types.
• scdiv-paper — Code and data for the paper "Diversity in transcriptomics without cell types"
• topact-paper — Code for the paper "Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics"