LOGML 2024 - Projects

Calabi-Yau Metrics with U(1)-invariant Neural Networks

Exploiting Graph Neural Networks for Prescriptive maintenance of CERN’s technical infrastructure

Generating Calabi-Yau Manifolds with Machine Learning

Geometric GNNs for particle level reconstruction

Geometry for Distribution Learning

Graph Learning for Uplift Modeling

George Panagopoulos

Invariantly learning terminal singularities

Learning to predict optimal solution value for NP-Hard Combinatorial problems

Sahil Manchanda

Matching graphs with spatial constrains

Mixed Curvature Graph Neural Networks

Rishi Sonthalia

Multimodal Protein Representation Learning

Michail Chatzianastasis

On the Geometry of Relative Representations

Powerful Graph Neural Networks for Relational Databases

Joshua Robinson

Predicting the pathogenicity of a (missense) mutation

Abhishek Sharma

Self-supervised learning for Topological Neural Networks

Claudio Battiloro

Spectral Signed GNNs for fMRI Connectomes