Research in the Laboratory of Intelligent Systems and Controls (LISC) aims at design and analysis of methods and algorithms for learning and computational intelligence. LISC has also done research on investigation of theory and properties of network models, such as neural and probabilistic networks, for the purpose of enhancing their learning abilities and improving reliability. Learning, adaptation, and decision-making processes are automated based on concepts drawn from control theory and the life sciences.  Near-optimal control and emerging probabilistic techniques are applied to aircraft control, distributed sensor surveillance systems, and interdisciplinary problems, such as, criminal profiling. LISC is also interested in the application of expert systems and nonlinear dynamics techniques to psychological and cognitive modeling from data.