Daniel Spielman was appointed a Simons Investigator in Theoretical Computer Science as a part of the foundation’s inaugural class in 2012. He has been a professor of computer science, mathematics and applied mathematics at Yale University since 2006 and before that taught in the applied mathematics department at M.I.T. He earned his B.A. from Yale and his Ph.D. in mathematics from M.I.T, and he completed a National Science Foundation Mathematical Sciences Postdoctoral Fellowship at the University of California, Berkeley.
Spielman was also the 2010 recipient of the prestigious Rolf Nevanlinna Prize given every four years at the International Congress of Mathematicians for exemplary contributions in mathematical aspects of computer science. He first became interested in mathematics and computer science when he read about Burr Puzzles, a type of three-dimensional puzzle made out of blocks. Rather than attempting to assemble the puzzles, he created a computer program to help determine how the pieces could fit together. “I’ve always loved both computer science and math,” says Spielman. “I pursued these two things separately and didn’t connect them for a very long time.”
By combining mathematics with computer science, Spielman is able to make the process of solving mathematical problems faster and more efficient. He recently garnered much acclaim by using these methods to solve the Kadison-Singer problem, which originates from the mathematical foundations of quantum physics. “I know very little about quantum physics,” he says. “But I understand the problem because it popped up in many different fields, including discrepancy theory, which looks a lot like things that I work on.”
Much of Spielman’s work focuses on developing fast algorithms for solving large systems of linear equations in collaboration with Shang-Hua Teng, a 2014 Simons Investigator in Theoretical Computer Science. “Calculations for systems of linear equations are some of the main computations people do in many different fields, including computational science, physics, economics and optimizations,” says Spielman. Building on experience gained by using computer science to work with systems of linear equations, Spielman was subsequently able to apply his computer science skills to the Kadison-Singer problem, ultimately leading to success.
Now, Spielman focuses on problems that could have wide applications and that will challenge him. He seeks out problems that have existing solutions, and then works to create new solutions. “I’m interested in finding faster ways to solve computational problems. These are usually completely different from the standard approaches to solving them.”