Past Event: Oden Institute Seminar

Optimal Operator and Experimental Design for Uncertain Systems

Edward R. Dougherty, Chair and Distinguished Professor, Department of Electrical and Computer Engineering, and Scientific Director of the Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University

Abstract

The most basic aspect of modern engineering is the design of an operator to act on a physical system in an optimal manner relative to a desired objective – for instance, designing a control policy to autonomously direct a system or designing a classifier to make decisions regarding the system. In the classical paradigm, knowledge regarding the system model is assumed to be certain; however, in practice, especially with complex systems, knowledge is uncertain and operators must be designed while taking this uncertainty into account. An intrinsically Bayesian robust (IBR) operator is optimal relative to both the operational objective and the partial knowledge, as quantified by a prior distribution over an uncertainty class of possible models. An objective-based experimental design procedure is naturally related to optimal operator design because, if we are to select among a collection of experiments, then we would like to perform an experiment that maximally reduces objective-related uncertainty. This uncertainty is quantified via the mean objective cost of uncertainty (MOCU), which measures the expected cost of applying an IBR operator instead of actually optimal operators across the uncertainty class. Bio Edward R. Dougherty is the Robert M. Kennedy ‘26 Chair and Distinguished Professor in the Department of Electrical and Computer Engineering at Texas A&M University, and is Scientific Director of the Center for Bioinformatics and Genomic Systems Engineering. He holds a Ph.D. in mathematics from Rutgers University and the Doctor Honoris Causa from the Tampere University of Technology.