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Past

Research Staff

Teresa Huang, Ph.D.

Flatiron Research Fellow, Center for Computational Mathematics

Bio

Teresa Huang joined the Center for Computational Mathematics as a Flatiron Research Fellow in September 2024. She received her Ph.D. in applied mathematics and statistics at Johns Hopkins University, coadvised by Soledad Villar and Carey Priebe. She is broadly interested in geometric deep learning and mechanistic understanding of machine learning models. During her Ph.D., she focused on exploring symmetries in graph learning.

Publications

Multi-View Graph Learning with Graph-Tuple

Conference on Topology Algebra and Geometry in Data Science, arxiv:2510.10341, 2025

Shiyu Chen and Teresa Huang

Diffusion for Fusion: Designing Stellarators with Generative AI

Conference on Neural Information Processing Systems, arxiv:2511.20445, 2025

Misha Padidar, Teresa Huang, Andrew Giuliani, and Marina Spivak

Functions on Symmetric Matrices and Point Clouds via Lightweight Invariant Features from Galois Theory

SIAM Journal on Applied Algebra and Geometry, arxiv:2405.08097, 2025

Ben Blum-Smith, Teresa Huang, and Marco Cuturi

CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning

Conference on Neural Information Processing Systems, arxiv:2507.03707, 2025

Teresa Huang, et al.

Featuring Teresa Huang

A portrait of Teresa Huang writing on a white board.

Teresa Huang: Bridging Science and Machine Learning at Cosmic Scales

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New Open Interval Cohort Will Explore Symmetry Through Art and Science

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