Daniel Spielman
Also known as Dan Spielman, Daniel A. Spielman
Daniel Spielman is a Yale computer scientist and one of the organizers of First Proof, a project that poses research-level math questions to test AI systems. Together with Shang-Hua Teng, he won the Gödel Prize in 2008 for smoothed analysis of algorithms, and the Rolf Nevanlinna Prize followed in 2010. In 2012 he received a MacArthur Fellowship, and in 2014 the Pólya Prize honored the Kadison–Singer result. In 2023 he won the Breakthrough Prize in Mathematics, and in 2026 the AMS elected him to its class of Fellows. As of October 2026 he is Sterling Professor of Computer Science at Yale and directs the Institute for Foundations of Data Science.
Timeline
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1992–2006
An MIT doctorate, then Yale
He receives a Yale bachelor's degree in 1992 and a PhD in applied mathematics from MIT in 1995. He joins Yale's faculty in 2006.
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2008–2015
Two Gödel Prizes
Together with Shang-Hua Teng, he wins the Gödel Prize in 2008 for smoothed analysis of algorithms, and they win it again in 2015.
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2010
The Rolf Nevanlinna Prize
The 2010 Rolf Nevanlinna Prize recognizes him for smoothed analysis of linear programming and algorithms for graph-based codes.
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2012
A MacArthur Fellowship
In October 2012 he is named a MacArthur Fellow, an award that provides $500,000 over five years.
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2014–2015
Interlacing Families II
The 2014 Pólya Prize honors the Kadison–Singer result. In 2015 he, Adam Marcus, and Nikhil Srivastava publish Interlacing Families II in the Annals of Mathematics.
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2023–2026
Breakthrough Prize and AMS Fellow
He wins the 2023 Breakthrough Prize in Mathematics. The AMS elects him to its 2026 class of Fellows.
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2026
The First Proof benchmark
He co-authors the First Proof paper, which poses ten research-level math questions to test AI systems, and he is one of the project's organizers. The project's second batch comes with a formal benchmark planned for March–June 2026.
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As of October 2026
Sterling professor at Yale
He is Sterling Professor of Computer Science at Yale and directs the Institute for Foundations of Data Science.
Sources and credits
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