Research project in geometric deep learning
In this project, we study neural ODEs in spaces with locally varying dimension by leveraging the geometry of M-polyfolds. The result is flexible and expressive machine learning models that dynamically adapt their dimension to explore different states during training. We develop the mathematical foundations for flows and symmetries on M-polyfolds and harness these constructs to create novel models in geometric deep learning beyond the confines of traditional manifolds.
