Projects

Beyond Text: Exploring Adaptations of LLMs for Graph-Based Tasks

Fabrizio Frasca

Beyond VC Dimension - Rademacher Complexity for GNN Generalization

Caterina Graziani

Cycle Matching for High-Dimensional Neural Activation Patterns

Anthea Monod, Omer Bobrowski

Fairness-Aware GraphRAG for Trustworthy and Equitable Document Retrieval

Guadalupe Gonzalez, Chirag Agarwal, Kyle Higgins

Geometric deep learning for cortical surface analysis

Graph Transformers for Relational Deep Learning

Vijay Prakash Dwivedi

Investigating Emergent Invariance and Sampling Thresholds in Hopfield Networks on Graph Orbit Datasets

Iterative Reasoning in Graph Neural Networks for Drug Repurposing

Learning the Graphical Nature of Symmetries

Looking for Einstein Metrics with Machine Learning

Tancredi Schettini Gherardini

On Depth in Geometric Deep Learning: Scaling Up Biomolecular Analysis Using Deep Neural k-Forms

Polyhedral Complex Extraction from ReLU Networks

Representation learning and knowledge encoding with biomedical knowledge graphs

Representational Alignment for Universal Spaces

Donato Crisostomi

Riemannian deep reinforcement learning for PDE-constrained shape optimisation

Estefania Loayza Romero

Symmetry, degeneracy and effective dimensions of neural networks

Topological Machine Learning for Brain Dynamics

Dhananjay Bhaskar, Inés García-Redondo

Topological data analysis (TDA) to elucidate protein functions via variant landscapes

Towards a More Rigorous Evaluation of Hyperbolic Graph Representation Learning