Daniel A. Spielman is a computer scientist and mathematician specializing in the design and analysis of algorithms. His most famous works include the design of fast algorithms for the solution of systems of linear equations in the Laplacian matrices of graphs; the design of error-correcting codes that can be quickly encoded and decoded; the introduction of the smoothed analysis of algorithms, a theory that helps explain the behavior of algorithms in practice; and the solution of the Kadison-Singer problem in pure mathematics. Spielman was born and raised in Philadelphia, PA. He graduated from Yale in 1992 with B.A. degrees in Computer Science and Mathematics. He then received the Ph.D. in Applied Mathematics from MIT. He was a postdoctoral fellow in computer science at U.C. Berkeley in 1995. He joined the Applied Mathematics faculty of MIT in 1996, and then moved to Yale in 2005 where he presently holds appointments in the departments of Computer Science, Statistics and Data Science, and Mathematics.

Research Interests

Prof. Spielman is most interested in figuring out how quickly we can solve fundamental computational problems. His research group studies problems that arise in many domains, including optimization, numerical linear algebra, scientific computing, machine learning, network science, and digital communication. They also devote great effort to developing areas of mathematics that support their studies.

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Primary Section

Section 34: Computer and Information Sciences

Secondary Section

Section 32: Applied Mathematical Sciences