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The GTML 2025 workshop brought together a wide community of researchers for a full week of exchange at the intersection of geometry, topology, and machine learning.The GTML 2025 workshop brought together a wide community of researchers for a full week of exchange at the intersection of geometry, topology, and machine learning. With overwhelming interest, the event highlighted the growing momentum of this rapidly evolving research field.
The Workshop on Geometry, Topology, and Machine Learning (GTML 2025), jointly organized by the Max Planck Institute for Mathematics in the Sciences (Leipzig) and the STRUCTURES Cluster of Excellence (Heidelberg) took place recently in Leipzig. It marked the first event of this scale to unite the research communities of geometry, topology, and machine learning. The workshop attracted 132 participants, with registration reaching full capacity within only two weeks – a clear evidence of the strong interest within the scientific community.
GTML 2025 provided a unique platform for researchers to explore the fundamental role of geometric and topological methods in understanding data structures and developing rigorous frameworks for machine learning. The workshop format fostered deep scientific exchange and created valuable opportunities to identify new connections and build bridges between traditionally separate fields.
The scientific programme featured 10 keynote lectures and 20 expert presentations from leading researchers worldwide. A number of renowned speakers contributed to the programme, including industry experts Hartmut Maennel (DeepMind), Robert Lilow (Deepshore), and Vincent Stimper (Isomorphic Labs). Short papers will be published as a special edition of the PMLR (Proceedings of Machine Learning Research) series, ensuring continued visibility of the scientific contributions beyond the event itself.
A special highlight of the workshop were the Lightning Sessions, designed specifically for early-career researchers. These rapid-format presentations created a dynamic space for young scientists to share ideas, showcase ongoing work, and expand their professional networks.
The programme covered a broad spectrum of topics, including Mathematical foundations of machine learning, geometric machine learning (geometric deep learning, graph neural networks, geometry processing), topological machine learning (topological deep learning, TDA, shape analysis), and applications in the life sciences and complex systems.
Please visit the conference website for detailed information on the scientific topics.
With its strong scientific programme, interdisciplinary focus, and outstanding level of engagement, GTML 2025 has set a promising precedent for future meetings at the intersection of geometry, topology, and machine learning.
Further information:
- Conference Website GTML 2025
- Max Planck Institute for Mathematics in the Sciences
- Organizers: Michael Bleher, Freya Jensen, Levin Maier, Diaaeldin Taha, Anna Wienhard