Julian Center, Ph.D., CEO
Dr. Center has extensive experience in Bayesian methods, navigation and guidance systems, trusted software development, image processing, and pattern recognition. He has successfully led several research and development projects.
He received the BSEE from Oklahoma University and the M. S. and D.Sc. in Control System Science and Engineering from the Sever Institute at Washington University in St. Louis. His dissertation applied Bayesian methods to nonlinear estimation problems.
At the McDonnell Douglas Astronautics Company, he developed the attitude reference and self-leveling software for the strapdown inertial guidance system of the Harpoon missile. At The Analytic Sciences Corporation (TASC), now part of Northrop-Grumman, he led a twenty person project to develop a comprehensive accuracy model for the Trident submarine navigation system and missile guidance system. As Technical Director of the Systems Engineering Division of Jaycor, Inc., he supervised a team performing a variety of navigation and control systems projects.
As Vice President of Geospace Systems, he led a project to develop software to combine satellite orbit tracking and terrain elevation data to build a detailed model of the earth’s gravity for highly accurate, unaided inertial navigation. The Institute of Navigation presented him the Samuel M. Burka award for a paper describing this work.
As Director of Technology Development at Dynamic Research Corporation, he formulated methods for trusted software development. These methods relied heavily on object-oriented programming methods. During this period he taught an internal course on object-oriented software development with C++.
As President of Creative Research Corporation, he concentrated on image processing and pattern recognition systems. Under contract to Lau Technologies Inc., he led the development and installation of a facial recognition access control system for the Office of the Secretary of Defense, Defense Manpower Data Center.
As Vice President and Technical Director of Perceptive Network Technologies Inc., he led the development of a system that utilized facial recognition to facilitate personal communication across a computer network. This system used Bayesian methods to determine when the appropriate user was present at a computer node and notified others of that user’s presence.
Recently, he has developed new pattern recognition and data analysis methods based on Bayesian methods. These methods take advantage of current computer technology to implement fully Bayesian methods without requiring simplifying assumptions for practical implementation. The following papers are representative of this research.
1. Julian L. Center, Jr., “Annealed Adaptive Importance Sampling,” Bayesian Inference and Maximum Entropy Methods in Science and Engineering, American Institute of Physics, 2008.
2. Julian L. Center, Jr., “Regression for Proportion Data,” Bayesian Inference and Maximum Entropy Methods in Science and Engineering, American Institute of Physics, 2008.
3. Julian L. Center, Jr., “Distributed Processing to Learn Complex Classification Models from Large Data Sets,” High Performance Computing for Statistical Inference, Trinity College, Dublin, Aug. 2006.
4. Julian L. Center, Jr., “Learning Complex Classification Models from Large Data Sets,” Bayesian Inference and Maximum Entropy Methods in Science and Engineering, American Institute of Physics, 2006.
5. Julian L. Center, Jr., “Semi-Supervised Learning for Bayesian Pattern Classification,” Bayesian Inference and Maximum Entropy Methods in Science and Engineering, American Institute of Physics, 2005.
6. Julian L. Center, Jr., “Approximating Posterior Distributions for Mixture-Model Parameters,” Bayesian Inference and Maximum Entropy Methods in Science and Engineering, American Institute of Physics, 2004.
7. Julian L. Center, Jr., “Correcting Amplitude and Offset Variations for Pattern Classification,” Bayesian Inference and Maximum Entropy Methods in Science and Engineering, American Institute of Physics, 2003.
8. Julian L. Center, Jr., “Blind Source Separation, Independent Component Analysis, and Pattern Classification -- Connections and Synergies,” Bayesian Inference and Maximum Entropy Methods in Science and Engineering, American Institute of Physics, 2003.