Jason K. Johnson

1192B 41st StreetLos Alamos, NM 87544 USAhome: (505) 412-4162office: (505) 665-7816email : jasonj383@gmail.comhttp://ssg.mit.edu/group/∼jasonjhttp://cnls.lanl.gov/External/people/Jason Johnson.php

Research Interestsgraphical models, network optimization, machine learning, statistical signal and image pro-cessing, statistical physics, combinatorial optimization, multiscale methods, informationtheory, convex optimization and analysis.

EducationMassachusetts Institute of Technology, Cambridge MA.Ph.D. Electrical Engineering and Computer Science, 2008.S.M. Electrical Engineering and Computer Science, 2003.S.B. Physics, 1995.Graduate Research Advisor: Prof. Alan WillskyUndergraduate Thesis Advisor: Prof. Edward Farhi

Professional ExperiencePostdoctoral Fellow/Research Associate 2008-PresentDr. Michael Chertkov Los Alamos, NMCenter for Nonlinear Studies & Theoretical Division T-4, Los Alamos National Laboratory

Director-Funded Postdoctoral Fellow (2009-2011) independently-funded, competitive 2-yearappointment made by selection committee annually. I researched combinatorial, variationaland multiscale approaches to approximate inference in graphical models, learning planarIsing models, optimization and control of power transmission networks. I was a co-organizerof the 2009 Physics of Algorithms Workshop, Santa Fe NM (http:/cnls.lanl.gov/poa),have co-advised two graduate summer interns and have contributed to several research grantproposals.

Research Assistant 2000-2008Prof. Alan Willsky Cambridge, MALaboratory for Information and Decision Systems, MIT

I researched tractable inference and learning methods for graphical models, with applica-tions to large-scale estimation problems in remote sensing. I played a central role in devel-oping walk-sum analysis of Gaussian inference algorithms (belief propagation and iterativemethods), the Lagrangian relaxation method and convergent iterative message-passing forestimation in graphical models and the maximum-entropy relaxation method and relaxediterative scaling algorithm for graphical model selection.

Summer Internship Summer 2005Dr. Evan Fortunato Burlington, MAAlphatech, Inc.

Developed Lagrangian relaxation technique for hypothesis pruning in the multiple-hypothesistesting approach to multi-target tracking.

Teaching Assistant Fall 2003Prof. Tommi Jaakkola Cambridge, MADepartment of Electrical Engineering and Computer Science, MIT

Taught recitation sections and assisted in development of problem sets for introductorymachine learning course.

Member of Technical Staff 1995-2000Dr. Robert Washburn Burlington, MAAlphatech, Inc.

Algorithm development and prototyping. C/C++ programming. Automatic target recog-nition, multi-sensor data fusion, multi-target tracking, image segmentation, recursive infer-ence for force aggregation, inference and learning for multi-scale Markov tree models.

Publications1

ThesesConvex Relaxation Methods for Graphical Models: Lagrangian and Maximum EntropyApproaches. MIT Doctoral Thesis, 257 pages, August 2008. (3 citations)http://ssg.mit.edu/group/jasonj

Estimation of GMRFs by Recursive Cavity Modeling. MIT Master’s Thesis, 205 pages,March 2003. (6 citations) http://ssg.mit.edu/group/jasonj

Journal ArticlesJKJ, A. Willsky. A recursive model-reduction method for estimation in Gaussian Markovrandom fields. IEEE Transactions on Image Processing, v.17, no.1, pp.70–83, January 2008.(10 citations) http://ieeexplore.ieee.org

D. Malioutov, JKJ, A. Willsky. Walk-sums and belief propagation in Gaussian graphicalmodels. Journal of Machine Learning Research, v.7, pp.2031–2064, October 2006. (76citations) http://jmlr.csail.mit.edu

D. Malioutov, JKJ, M. Choi, A. Willsky. Low-rank variance approximation in GMRFModels: single and multiscale approaches. IEEE Transactions on Signal Processing, v.56,no.10, pp.4621–4634, October 2008. (6 citations) http://ieeexplore.ieee.org

1Google Scholar citations ∼ 256.

V. Chandrasekaran, JKJ, A. Willsky. Estimation in Gaussian graphical models usingtractable sub-graphs: a walk-sum analysis. IEEE Transactions on Signal Processing, v.56,no.5, pp.1916-1930, May 2008. (17 citations)http://ieeexplore.ieee.org

M. Choi, V. Chandrasekaran, D. Malioutov, JKJ, A. Willsky. Multiscale stochastic model-ing for tractable inference and data assimilation. Computer Methods in Applied Mechanicsand Engineering, v.197, pp.3492–3515, August 2008. (9 citations)http://sciencedirect.com

Conference PapersS. Kudekar, JKJ, M. Chertkov. Linear programming based detectors for two-dimensionalintersymbol interference channels. Submitted to International Symposium on InformationTheory. (under review) http://arxiv.org/abs/1102.5386

JKJ, P. Netrapalli, M. Chertkov. Learning planar Ising models. (under review)http://arxiv.org/abs/1011.3494

JKJ, M. Chertkov. A Majorization-Minimization Approach to Design of Power Trans-mission Networks. 49th IEEE Conference on Decision and Control, December 2010. (2citations) http://arxiv.org/abs/1004.2285

JKJ, V. Chernyak, M. Chertkov. Orbit-Product Representation and Correction of GaussianBelief Propagation. International Conference on Machine Learning, June 2009. (4 citations)http://arxiv.org/abs/090.3769

http://videolectures.net/jason k johnson

JKJ, D. Bickson, D. Dolev. Fixing convergence of Gaussian belief propagation. Interna-tional Symposium of Information Theory, July 2009. (10 citations)http://arxiv.org/abs/0901.4192

JKJ, V. Chandrasekaran, A. Willsky. Learning Markov structure by maximum entropyrelaxation. 11th Inter. Conf. on Artificial Intelligence and Statistics (AISTATS), March2007. (15 citations) http://www.stat.umn.edu/∼aistat/proceedings/start.htm

JKJ, D. Malioutov, A. Willsky. Lagrangian relaxation for MAP estimation in graphicalmodels. 45th Allerton Conf. on Communication, Control and Computing, September 2007.(24 citations)http://www.csl.uiuc.edu/allerton/archives/allerton07/papers/0250.pdf

JKJ, D. Malioutov, A. Willsky. Walk-sum interpretation and analysis of Gaussian beliefpropagation. Advances in Neural Information Processing Systems (NIPS), v.18, pp.579–586, December 2005. (33 citations)Selected for ”spot-light” at the conference.http://books.nips.cc/nips18.html

V. Chandrasekaran, JKJ, A. Willsky, Adaptive embedded subgraph algorithms using walk-sum analysis. Advances in Neural Information Processing Systems (NIPS), v.20, December2007. (4 citations) http://books.nips.cc/nips20.html

V. Chandrasekaran, JKJ, A. Willsky. Maximum entropy relaxation for graphical modelselection given inconsistent statistics. IEEE 14th Workshop on Statistical Signal Processing(SSP), pp.625–629, August 2007. (3 citations) http://ieeexplore.ieee.org

D. Malioutov, JKJ, A. Willsky. GMRF variance approximation using spliced wavelet bases.Inter. Conf. on Acoustics, Speech and Signal Processing (ICASSP), v.3, pp.1101-1104, April2007. (8 citations) http://ieeexplore.ieee.org

D. Malioutov, JKJ, A. Willsky. Low-rank variance estimation in large-scale GMRF models.Inter. Conf. on Acoustics, Speech and Signal Processing (ICASSP), v.3, pp.676–679, May2006. (10 citations)Student Paper Award.http://ieeexplore.ieee.org

JKJ, R. Chaney. Recursive composition inference for force aggregation. Proc. of the 2ndInter. Conf. on Information Fusion, v.2, pp.1187–1195, July 1999. (15 citations)Alphatech Joseph G. Wohl Memorial Achievement Award.http://handle.dtic.mil/100.2/ADA366940

W. Irving, JKJ. SAR-FLIR sensor fusion for ATR with 3D model-based reasoning. Proc. ofthe 1998 IRIS National Symposium on Sensor and Data Fusion, March 1998. (1 citation)

Invited TalkMessage-Passing Algorithms for GMRFs and Non-Linear Optimization. Neural InformationProcessing Systems, Workshop on Approximate Inference in Continuous/Hybrid Models.Whistler B.C., Canada, December 2007.http://intranet.cs.man.ac.uk/ai/nips07

WorkshopsA Majorization-Minimization Approach to Design of Power Transmission Networks. Mini-Workshop on Optimization and Control Theory, Los Alamos, NM, August 2010.http://cnls.lanl.gov/∼chertkov/SmarterGrids/w sh 10/Talks/Johnson.pdf

Orbit-Product Analysis of (Generalized) Gaussian Belief Propagation. Physics of Algo-rithms Workshop, Santa-Fe NM, September 2009.http://cnls.lanl.gov/∼jasonj/poa/abstracts.html#johnson

ProposalM. Chertkov et al. Optimization and Control Theory for Smart Grids. This proposal wasawarded internal R&D funding for a three-year project at LANL.http://cnls.lanl.gov/∼chertkov/SmarterGrids/