Our work spans the theoretical foundations of geometric deep learning to practical applications in science and medicine.
* Note: This page is currently a work in progress and under development.
We build the mathematical foundations for symmetry-preserving neural networks. By embedding the symmetries of physics (equivariance) into the architecture, we ensure data efficiency and generalization.
Representing signals not as discrete pixels, but as continuous functions grounded in geometry. This allows for resolution-independent processing and rigorous handling of manifold data.
Applying geometric deep learning to complex medical data. From histopathology to MRI, we use equivariance to robustly analyze biological structures and improve diagnostic reliability.
Developing generative models that operate on non-Euclidean spaces (manifolds). We explore stochastic differential equations and flow matching on geometric domains.
Modeling atomic systems and molecular structures as geometric point clouds. Our methods help predict properties and simulate interactions conformant with physical laws.