LOGML Summer School 2026

London, 13-17 July 2026*

LOGML (London Geometry and Machine Learning) aims to bring together mathematicians and computer scientists to collaborate on a variety of problems at the intersection of geometry and machine learning. There will be a selection of group projects, each overseen by an experienced mentor, talks by leading figures in the field, and a variety of social events.

Several working groups that started at LOGML went on to form longer-term collaborations leading to publications in notable conferences and journals, including:

• Connecting Neural Models Latent Geometries with Relative Geodesic Representations (Workshop on Symmetry and Geometry in Neural Representations, NeurIPS 2024)
• Group-invariant machine learning on the Kreuzer-Skarke dataset (Physics Letters B, 2024)
• Implicit Convolutional Kernels for Steerable CNNs (NeurIPS, 2023 )
• Accelerating Molecular Graph Neural Networks via Knowledge Distillation (Synergy of Scientific and Machine Learning Modeling workshop, ICML, 2023)
• Surfing on the Neural Sheaf (Workshop on Symmetry and Geometry in Neural Representations, NeurIPS 2022)
• Generalized Laplacian Positional Encoding for Graph Representation Learning  (Workshop on Symmetry and Geometry in Neural Representations, NeurIPS 2022)
• Equivariant Mesh Attention Networks (TMLR, 2022)
• Unsupervised Network Embedding Beyond Homophily (TMLR, 2022)
• Towards Training GNNs Using Explanation Directed Message Passing (LOG conference, 2022)
• Eqivariant Subgraph Aggregation Networks (ICLR 2022, Spotlight)

You can find a list of previous years’ projects and speakers under Archives above.

*These are tentative dates and subject to change.