PhD candidate, EECS Dept., MIT.
Member of the Stochastic Systems Group (SSG),
Laboratory for Information and Decision Systems (LIDS).

I attended Appalachian State University for two years before transferring to MIT, where I graduated S.B. Physics, 1995. During the next five years, I was a member of technical staff with Alphatech Inc., where I helped develop algorithms for multi-resolution signal and image processing, data fusion and multi-target tracking. In 2000, I entered the EECS graduate program at MIT under the direction of Alan Willsky, where I earned the S.M., 2003, and am currently working to complete the PhD program.

Research Summary

My research has focused on the use of information theory and convex optimization to provide principled, tractable approximation methods for solving large-scale inference and estimation problems involving graphical models, also known as Markov random fields (MRFs). In particular, Gaussian MRFs (commonly used in image processing) have played a central role in these investigations.

Here are summaries of several novel methods that I introduced:

• Recursive Cavity Modeling
• Maximum Entropy Relaxation
• Lagrangian Relaxation for Intractable Graphical Models
• Walk-Sum Analysis in Gaussian Graphical Models