Geometry for AI
Organizers
Speaker
Daniel Platt
Time
Thursday, November 27, 2025 5:30 PM - 6:30 PM
Venue
A3-4-301
Online
Zoom 293 812 9202
(BIMSA)
Abstract
In this talk I will give an overview over applications of geometry to AI. I plan to mention the following topics:
- Invariant/equivariant machine learning,
- Convolutional Neural Networks (CNNs) on manifolds,
- Graph Neural Networks (GNNs),
- Graph curvature and graph rewiring,
- Geometry of embeddings,
- Optimal transport and Wasserstein distance,
- Riemannian optimisation,
- Learning of symmetries,
- Persistent homology,
- Kernels on manifolds.
Each of these are big research areas, so I will say quite little about each of them. I plan to give one definition and then introduce the main paper(s) in this area as well as current research directions, to empower interested listeners to learn more about the topic and maybe start a project in this area themselves.
- Invariant/equivariant machine learning,
- Convolutional Neural Networks (CNNs) on manifolds,
- Graph Neural Networks (GNNs),
- Graph curvature and graph rewiring,
- Geometry of embeddings,
- Optimal transport and Wasserstein distance,
- Riemannian optimisation,
- Learning of symmetries,
- Persistent homology,
- Kernels on manifolds.
Each of these are big research areas, so I will say quite little about each of them. I plan to give one definition and then introduce the main paper(s) in this area as well as current research directions, to empower interested listeners to learn more about the topic and maybe start a project in this area themselves.