
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 geometry and learning data representations in suitable non-Euclidean spaces.

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

Geometric Deep Learning
Designing geometric architectures. Understanding learning-theoretic trade-offs in geometric settings.

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

Geometry & AI4Science
Geometric methods for resource-efficient and interpretable scientific machine learning.

Geometry of Foundation Models
Leveraging data and model geometry for more resource-efficient and transparent foundation models.
Latest News
- [07/2025] Article on Discrete Curvature and Applications in Graph Machine Learning in SIAM News
- [07/2025] Bobak and Adit start faculty positions
- [04/2025] Workshop on Non-Euclidean Foundation Models
- [04/2025] Papers at ICLR and ICLR Re-Align Workshop
- [04/2025] Andrew passes Qualifying Exam
- [03/2025] Aramont Fellowship for Emerging Science Research