
Geometric Machine Learning
We study geometric structure in data and models and how to leverage such information
for the design of efficient machine learning algorithms with provable guarantees.
Our Research

Geometric Representation Learning
Characterizing data and geometry and learning data representations in suitable non-Euclidean spaces. Non-Euclidean geometry of foundation models.

Graph Machine Learning
Leveraging geometric structure for efficient learning on graphs. Design of expressive and stable graph machine learning architectures.

Learning Under Symmetry
Designing geometric architectures. Understanding learning-theoretic trade-offs in geometric settings.

Geometry & AI4Science
Leverage geometric machine learning for resource-efficient and interpretable scientific machine learning.

Optimization on Manifolds
Exploiting geometric structure in (non)convex (constrained) optimization.

Geometry of Foundation Models
Leveraging data and model geometry for more efficient and transparent foundation models.