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.

Learning Under Symmetry

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

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