Nathan Srebro
Also known as Nati Srebro
Nathan Srebro is a professor at the Toyota Technological Institute at Chicago (TTIC), and his research covers statistical and computational aspects of machine learning. He earned his PhD at MIT in 2004 and held earlier posts at Toronto, IBM and the Technion. He won an ICML Test of Time honorable mention in 2017 for Pegasos, an SVM solver. In 2016 he co-authored a paper that proposes a criterion for discrimination in supervised learning. TTIC named him the new Lenore Blum Professor of Computer Science in November 2025.
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Publication numbers from OpenAlex, as of 15 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.
Timeline
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2004
An MIT doctorate and earlier posts
He completes his PhD at MIT in 2004. Earlier posts include a postdoctoral fellowship at the University of Toronto, a visiting scientist role at IBM and an associate professorship at the Technion.
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2016
A criterion against discrimination
In 2016 he co-authors Equality of Opportunity in Supervised Learning with Moritz Hardt and Eric Price. The paper proposes a criterion for discrimination against a specified sensitive attribute in supervised learning.
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2017
A Test of Time honorable mention
He wins an ICML Test of Time honorable mention in 2017 for his paper on Pegasos, an SVM solver.
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2020
Organizing a deep learning theory quarter
In 2020 he organizes the autumn Special Quarter on Theory of Deep Learning with Zhaoran Wang and Dongning Guo of Northwestern University.
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2023
A federated learning workshop award
In 2023 he shares a Best Student Paper Honorable Mention at the ICML Workshop on Federated Learning and Analytics in Practice, for a paper on federated averaging.
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November 2025
Named Lenore Blum Professor
TTIC names him the new Lenore Blum Professor of Computer Science in November 2025.
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As of October 2026
Professor at TTIC
He is a professor at the Toyota Technological Institute at Chicago, with cross-appointments in the University of Chicago's Department of Computer Science and its Committee on Computational and Applied Mathematics. His research covers statistical and computational aspects of machine learning.
Sources and credits
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