Dr. John Sudano

Many times critical decisions must be made with a incomplete information set. In this presentation simple examples will be used to explore probability theory, incomplete information set, beliefs, and plausibilities, Dempster-Shafer theory, and the mapping of basic belief assignments via pignistic probability mappings.

Biography:

Dr. John Sudano is presently doing research in the automated integration of dissimilar sensor information for identification, error estimation in information-fusion processes and information discrimination in hostile environments. He developed the Generalized Belief Fusion Algorithm, a superset of the Dempster-Shafer belief fusion method.  His other research interests include tracking, the representation of true position and velocity tracking errors and precise gravitational representation for missile tracking.  He received a Ph. D. degree from New York University, an M.S. from Michigan State University, and a B.S. from Fairleigh Dickinson University, all in Physics. Most recently, he has served as the General Chairman of the 2005, 8th International Conference on Information Fusion in Philadelphia and the 2007 Chairman of the 2nd Annual Sensor Fusion Europe Conference held in Brussels.

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