AMMCS-2011 Plenary Talk:

Random Matrix Theory in Applied Mathematics, Modeling, and Computational Science

by Alan Edelman

Massachusetts Institute of Technology

Random matrix theory continues to be a powerful tool for so many applications, yet the number of scientists familiar with the various aspects of the theory remains relatively small at this time.  Further the theory is developing rapidly with very many open problems.   

This talk will give a general overview of the theory and delve into a few applications and open problems.

Alan Edelman is Professor of Applied Mathematics at the Massachusetts Institute of Technology and a Principal Invistigator at the MIT Computer Science and AI Laboratory where he leads a group in Applied Computing. In 2004 Professor Edelman founded Interactive Supercomputing, recently acquired by Microsoft. He received the B.S. & M.S. degrees in mathematics from Yale in 1984, and the Ph.D. in applied mathematics from MIT in 1989 under the direction of Lloyd N. Trefethen. Following a year at Thinking Machines Corp and at CERFACS in France, Edelman went to U.C. Berkeley as a Morrey Assistant Professor and Lewy Fellow, 1990-93. He joined the MIT faculty in applied mathematics in 1993. Edelman's research interests include high performance computing, numerical computation, linear algebra and stochastic eigenanalysis (random matrix theory). He has consulted for Akamai, IBM, Pixar, and NKK Japan among other corporations. A Sloan fellow, Edelman received an NSF Faculty Career award in 1995. Dr. Edelman is a SIAM Fellow. He has received numerous awards, among them the Gordon Bell Prize and Householder Prize (1990), the Chauvenet Prize (1998), the Edgerly Science Partnership Award (1999), the SIAM Activity Group on Linear Algebra Prize (2000), and the Lester R. Ford Award, (2005).