As universities try to increase their basic research portfolios and government agencies budget decrease, there is a need for academic institutions to find new opportunities for Sponsored Activities. The Department of Defense (DoD) Applied (6.2) and Advanced (6.3) programs creates a much larger pool of funds as long as universities are willing to adapt to the modus operandi and expectations of the different agencies. In this talk, we will describe a business model that has allowed the University at Buffalo and others to take advantage of the research resources in military agencies without deviating from the interests and goals by faculty in the area of Operations Research and Information Exploitation. Realizing Information Gain through Optimization of Reconnaissance and Surveillance (RIGORS) will be an example of a successful project funded by the Office of Naval Research that went from Basic Research to be considered part of a Program of Record for the Navy. Synchronization of Intelligence, Surveillance, and Reconnaissance (ISR) activities to maximize the utilization of limited resources (both in terms of quantity and capability) has become critically important to military forces. In centralized frameworks, a single node is responsible for determining and disseminating decisions (e.g., tasks assignments) to all nodes in the network. This requires a robust and reliable communication network. In decentralized frameworks, processing of information and decision making occur at different nodes in the network, reducing the communication requirements. RIGORS is an end-to-end optimization system which will consider the possibilities of centralized-control on one extreme to a completely decentralized-control set of assets/sensors on the other extreme. The solution spans from the generation of Information Requirements capturing the mission information needs over time, to the gathering and conditioning of information from multiple sources, to the optimal management of collection assets when critical information requests are not fulfilled and information deficits are identified in the system.
Dr. Moises Sudit’s primary research interests are in the theory and applications of Discrete Optimization and Information Fusion. More specifically, he has been concerned in the design and analysis of methods to solve problems in the areas of Integer Programming and Combinatorial Optimization. One primary goal of this research has been the development of efficient exact and approximate (heuristic) procedures to solve large-scale (Big Data) hard (NP-Hard) engineering and management problems. As Executive Director of the Center for Multisource Information Fusion and Chief Scientist of Information Exploitation at CUBRC, Dr. Sudit has merged the interests of Operations Research with Information Fusion.
He has an appointment as Professor in the School of Engineering and Applied Sciences at the University at Buffalo. He also serves as Associate Vice-President for Sponsored Research and Technology Transfer for the University ($170 million portfolio). Dr. Sudit is a NRC Fellow through the Information Directorate at the Air Force Research Laboratory and has received a number of scholarly and teaching awards. He also received the prestigious IBM Faculty Scholarship Award. Dr. Sudit has a number of publications in distinguished journals and has been the Principal Investigator in numerous research projects. He obtained his Bachelor of Science in Industrial and Systems Engineering from Georgia Institute of Technology, his Master of Science in Operations Research from Stanford University and his Doctorate in Operations Research from Purdue University.