Papillon, Mathilde https://orcid.org/0000-0003-1674-4218
Sanborn, Sophia https://orcid.org/0000-0002-1957-7067
Mathe, Johan https://orcid.org/0009-0000-8096-574X
Cornelis, Louisa https://orcid.org/0009-0000-7156-9884
Bertics, Abby https://orcid.org/0009-0000-5081-4983
Buracas, Domas
J Lillemark, Hansen
Shewmake, Christian https://orcid.org/0000-0003-4363-5615
Dinc, Fatih https://orcid.org/0000-0003-0921-0162
Pennec, Xavier https://orcid.org/0000-0002-6617-7664
Miolane, Nina https://orcid.org/0000-0002-1200-9024
Funding for this research was provided by:
National Science Foundation (2134241)
National Institutes of Health (R01NS119468)
Gordon and Betty Moore Foundation (2919.02)
University of California Santa Barbara Chancellor’s Fellowship
Natural Sciences and Engineering Research Council of Canada (587432)
European Research Council (786854)
Chan Zuckerberg Initiative
Agence Nationale de la Recherche (ANR-23-IACL-0001)
Article Title: Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures
Journal Title: Machine Learning: Science and Technology
Article Type: paper
Copyright Information: © 2025 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2024-09-26
Date Accepted: 2025-07-23
Online publication date: 2025-08-01