Research in the Laboratory of Intelligent Systems and Controls aims at developing computational methods that provide intelligent systems with a higher degree of mathematical structure, to guide their application and improve reliability. Learning, adaptation, and decision-making processes are automated based on concepts drawn from control theory and the life sciences. Near-optimal control techniques for distributed systems are applied to sensor surveillance systems for target identification and tracking, and to remote medical diagnosis applications.