Daniel A. Spielman

Daniel Spielman is the Sterling Professor of Computer Science and a Professor of Statistics and Data Science and of Mathematics at Yale University. Spielman's contributions to computer science include developing nearly linear time algorithms for solving systems of equations in Laplacian matrices; also algorithms for graph sparsification and local graph clustering. Early work on asymptotically good error-correcting codes with linear time encoding and decoding, making them asymptotically optimal by using irregular graphs, already brought him worldwide attention. He developed smoothed analysis of algorithms to explain the success of the Simplex Method for Linear Programming, and gave the first rigorous analysis of an efficient algorithm for Dictionary Learning. In mathematics, he solved the Kadison-Singer problem, and the related construction of Ramanujan graphs.