Jason K. Johnson
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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:


flag Orbit-Product Representation and Correction of Gaussian Belief Propagation
as author at  Sessions,
flag Message-Passing Algorithms for GMRFs and Non-Linear Optimization
as author at  NIPS Workshop on Approximate Bayesian Inference in Continuous/Hybrid Models, Whistler 2007,