Noah Amsel
Noah Amsel is a computer science PhD student at NYU's Courant Institute who brings approximation theory and numerical linear algebra to the training of neural networks. As an undergraduate at Yale he worked on neural stack networks, and he then spent time as a research engineer at Reservoir Labs before starting his PhD under Joan Bruna and Chris Musco in 2022. In 2025, he coauthored The Polar Express with David Persson, Christopher Musco and Robert Gower, a method for computing the polar decomposition inside the Muon optimizer, and ICLR gave the paper an Outstanding Paper Honorable Mention in 2026. In August 2026, he posted Dion3, a faster revision of Muon built with John Langford, Tri Dao and others, and he continues as a fifth-year PhD student at NYU.
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Timeline
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By 2019
Neural stacks at Yale
He studies as an undergraduate at Yale, where he works with Bob Frank in the CLAY lab and is advised by Dan Spielman. He is a coauthor of two BlackboxNLP workshop papers on stack-augmented neural networks: 'Context-Free Transductions with Neural Stacks' in 2018 and 'Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing' in 2019.
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By 2021
Research engineer at Reservoir Labs
He works as a research engineer at Reservoir Labs (now Qualcomm), modeling congestion control in communication networks. His papers on bottleneck structures in data networks appear at INDIS in 2020 and at ACM SIGCOMM in 2021.
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September 2022
Starting a PhD at NYU
He joins Joan Bruna's group at NYU in September 2022 as a computer science PhD student, co-advised by Chris Musco. An NSF Graduate Research Fellowship supports his degree.
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December 2024–April 2025
Matrix functions and attention theory
In December 2024, he publishes 'Nearly Optimal Approximation of Matrix Functions by the Lanczos Method' at NeurIPS with Tyler Chen, Anne Greenbaum, Cameron Musco and Christopher Musco, and the paper is a spotlight. In April 2025, he publishes 'Quality over Quantity in Attention Layers: When Adding More Heads Hurts' at ICLR with Gilad Yehudai and Joan Bruna, which proves that the rank of attention has a dramatic effect on its representational capacity.
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May 2025
The Polar Express preprint
In May 2025, he posts The Polar Express on arXiv with David Persson, Christopher Musco and Robert M. Gower. The method computes the polar decomposition used in the Muon optimizer with matrix-matrix multiplications only, solves a minimax problem to adapt the update rule at each iteration, and supplies optimal coefficients that become a core component of Muon.
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2026
An ICLR honorable mention
ICLR 2026 gives The Polar Express an Outstanding Paper Honorable Mention. Entries in the nanoGPT speedrun training contest use the method, and he says Microsoft Research and larger frontier-scale language models use it too.
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August 2026
Dion3 for Muon
In August 2026, he posts Dion3: Full-Stack Orthogonal Updates on arXiv with John Langford, Tri Dao and others. The revision of Muon combines a Gram Newton-Schulz algorithm, kernels that exploit symmetry and a row-selection update rule, and it matches or improves Muon's loss while cutting optimizer step time by up to 6x. He has also worked with John Langford at Microsoft Research on optimization algorithms for pretraining.
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As of October 2026
PhD student at NYU Courant
He is a fifth-year computer science PhD student at NYU's Courant Institute, advised by Joan Bruna and Chris Musco. His research themes are linear algebra, approximation theory and deep learning, and he helps organize the ML-NYC speaker series at the Flatiron Institute.
Career
- Until 2021Reservoir LabsResearch Engineer
Education
Sources and credits
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- Noah Amsel
- Publications | Noah Amsel
- Group :: Joan Bruna
- The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm
- Announcing the ICLR 2026 Outstanding Papers – ICLR Blog
- Noah Amsel, Professor David Persson, and Professor Christopher Musco Receive ICLR 2026 Outstanding Paper Honorable Mention
- How Flatiron Institute Mathematicians Helped Make AI More Efficient – Simons Foundation
- Dion3: Full-Stack Orthogonal Updates


