Christopher Musco
Christopher Musco is a computer scientist whose research develops efficient algorithms for large-scale data analysis and machine learning. He studied applied mathematics and computer science at Yale, completed a computer science Ph.D. at MIT in 2018, worked as a Research Instructor at Princeton, and joined NYU Tandon as an assistant professor in 2019. His research on big-data processing earned an NSF CAREER Award in 2021, and his paper on Shapley-value estimation was selected as an ICLR spotlight in 2025. The Polar Express earned an ICLR 2026 Outstanding Paper Honorable Mention; Musco is an Institute Associate Professor at NYU, where he studies efficient algorithms and modern machine learning.
- Now at
- New York University
Timeline
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2018
Yale and MIT degrees
He earns a Ph.D. in computer science at MIT in 2018 after studying applied mathematics and computer science at Yale; before MIT, he works as an engineer at Redfin.
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2018–2019
Research instructor at Princeton
Before joining NYU, he works as a Research Instructor at Princeton and teaches Advanced Algorithm Design (COS 521) in Fall 2018. By 2019, Princeton's TCS group includes him among its instructors and lists New York University as his affiliation.
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2019
Assistant professor at NYU Tandon
NYU Tandon names him a new assistant professor in its Department of Computer Science and Engineering in 2019. His research tackles how to scale statistical methods to large datasets for machine learning and data-driven discovery.
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2021
An NSF CAREER Award
He receives the 2021 NSF Faculty Early Career Development Award for work on processing and analyzing massive datasets. His project applies matrix sketching to compress large datasets and aims to make big-data processing more accessible.
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2025
A Shapley estimation spotlight
In 2025, his paper with R. Teal Witter, Provably Accurate Shapley Value Estimation via Leverage Score Sampling, is selected as an ICLR spotlight.
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2026
An ICLR Outstanding Paper Honorable Mention
With Noah Amsel, David Persson, and Robert Gower, he coauthors The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm, which receives an ICLR 2026 Outstanding Paper Honorable Mention. The work uses approximation theory to design polynomial approximations for the polar decomposition used in the Muon optimizer, with GPU computation and low-precision arithmetic in view.
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As of October 2026
Institute associate professor at NYU
He is an Institute Associate Professor of Computer Science and Engineering at NYU and belongs to its Theoretical Computer Science Group and VIDA Center. His research develops efficient algorithms for science and engineering and studies algorithmic aspects of modern machine learning, including fast inference and retrieval.
Career
- Since 2019New York UniversityAssistant Professor
- 2018–2019Princeton UniversityResearch Instructor
- RedfinEngineer
Education
Sources and credits
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