Todd Munson's primary research focus is algorithms and applications of optimization and complementarity. Most recently, he has been working on utilizing constrained nonlinear optimization techniques to compute mountain passes, critical points where the Hessian has exactly one negative eigenvalue. Mountain passes are of interest, for example, in computational chemistry where they correspond to transition states for chemical reactions. He has also been working with on an application of optimization to the r-refinement problem, a large nonlinear, nonconvex optimization problem that can cause grief for many general-purpose methods. Munson has also worked on several generalized Newton methods for solving complementarity problems and worked on special-purpose algorithms for solving support vector machine and mesh shape-quality optimization problems.
He has previously co-chaired the Institute for Computational Economics here at the University of Chicago, and currently serves on the editorial board for Mathematical Methods of Operations Research, as technical editor for Mathematical Programming Computation, on the advisory board for MPEC World, and as the area coordinator for Optimization Online.
Munson received his postdoctoral training between 2000 and 2002 at Argonne National Laboratory, after receiving his master's and PhD in computer science from the University of Wisconsin-Madison in 1996 and 2000 respectively. He received his bachelor's degree in computer science in 1995 from the University of Nebraska at Omaha. He's worked at Argonne ever since, but has held lecturer posts at the University of Chicago and Northwestern University.