Daniel Spielman

Daniel Spielman is the Sterling Professor of Computer Science, and a Professor of Statistics & Data Science and Mathematics at Yale University. He also serves as the inaugural James A. Attwood Director of the Institute for the Foundations of Data Science (FDS) and a member of the NSF Institute for Learning-Enabled Optimization at Scale (TILOS). He previously co-directed the Yale Institute for Network Science (YINS). His research focuses on algorithms, spectral graph theory, error-correcting codes, and combinatorics. Spielman holds a B.A. from Yale (1992) and a Ph.D. from MIT (1995), with postdoctoral work at UC Berkeley before joining Yale in 2005.

His awards include the Gödel Prize (2008, 2015), Nevanlinna Prize (2010), Breakthrough Prize in Mathematics (2023), and MacArthur Fellowship. He has advised numerous students, including notable alumni like Jon Kelner and Nikhil Srivastava. Spielman’s work bridges theoretical computer science, mathematics, and applied problems, with contributions to graph algorithms, linear system solvers, and randomized algorithms.

He teaches advanced courses on spectral graph theory, algorithms, and data science, and develops software tools such as Laplacians.jl. His research extends to applications in network science, machine learning, and scientific computing. Current initiatives include optimizing randomized experiments and advancing foundations of data science through interdisciplinary collaboration.