Convexity verification is central to optimization in ML and data science. We introduce a framework for testing geodesic convexity in nonlinear programs on geometric domains. Julia implementation available to leverage certificates in applications. Led by Andrew Cheng, Vaibhav Dixit. bit.ly/3HIlkJu

Single-cell data reveals developmental hierarchies, but common embeddings distort them. We present Contrastive Poincaré Maps, a self-supervised hyperbolic encoder that preserves hierarchies, scales efficiently, and uncovers lineage across datasets. Lead: @nithyabhasker.bsky.social 🧬 bit.ly/4211hMY

🚀 CALL FOR SUBMISSIONS: Non-Euclidean Foundation Models & Geometric Learning Workshop @ NeurIPS 2025 🚀

⏰ DEADLINE: Sep 2, 2025

📥 SUBMIT HERE: bit.ly/3UDTvEX

Join our reviewer pool: bit.ly/3JvvI7K

🔗 Full details: bit.ly/41PDyiM

4/26 at 3pm:

'Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups'

Zakhar Shumaylov · Peter Zaika · James Rowbottom · Ferdia Sherry · @mweber.bsky.social · Carola-Bibiane Schönlieb

Submission: openreview.net/forum?id=7PL...

Community detection is a classical graph learning task. Our new JMLR paper shows how discrete Ricci curvature and geometric flows unveil (mixed) communities and studies relations between the curvature of a graph and its dual.
w\ Yu Tian, Zach Lubberts: www.jmlr.org/papers/v26/2...

A postdoc position is available in my group at @harvard.edu Applied Math to perform research in Riemannian Optimization. More details, including on how to apply, can be found here: academicpositions.harvard.edu/postings/14832

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Hypergraphs naturally parametrize higher-order relations.Yet GNNs on hypergraph expansions often outperform specialized topological models. We show that adding hypergraph-level encodings yields significant performance and expressivity gains.w/ Raphael Pellegrin, Lukas Fesser arxiv.org/pdf/2502.09570