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

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

Exploiting geometric and topological structure in Graph Machine Learning.

Optimization and Machine Learning algorithms on geometric domains.

[06/2024] Lukas receives Kempner Fellowship
Lukas was selected for a Kempner Institute Graduate Fellowship. Congratulations!…
[05/2024] Dean’s Competitive Fund for Promising Scholarship
We received a grant from the Dean’s Competitive Fund for Promising Scholarship, which will support our research on Riemannian Optimization. Thank…
[04/2024] Andrew receives NSERC Fellowship
Andrew was selected for an NSERC Postgraduate Fellowship. Congratulations!…
[02/2024] Melanie selected as Sloan Fellow
Melanie was selected as a 2024 Alfred P. Sloan Fellow in Mathematics. See the Press Release here….
[01/2024] Two Papers accepted at ICRL
“On the Hardness of Learning under Symmetries” (selected as spotlight) and “Effective Structural Encodings via Local Curvature Profiles” were accepted at…