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Computational intelligence encompasses a collection of fundamental research areas dealing with the creation of knowledge from data, the development of algorithms for controlling computing decisions, and the effective approaches for interfacing computers and humans. The area focuses on enhancing human decision making and learning and the automation of computing processes.
CIDSE researchers are addressing problems in automated planning and scheduling, constraint satisfaction, knowledge representation and reasoning, natural language processing, multi-agent systems, and the semantic web.
Understanding complexity and the theory of computation are critical for developing efficient algorithms. Research in this group focuses on both fundamental theory for analyzing algorithms and on developing specific deterministic and randomized algorithms for solving classic problem formulations relevant to the emerging problems in society and technology.
As scientific and enterprise data sets grow with respect to data characteristics (scale, accuracy, timeliness, media, dimensions and instances) it becomes imperative to develop new approaches to extract spatial and temporal relationships, correlation patterns and knowledge. The faculty are actively engaged in developing new methods for identifying patterns and extracting information.
CIDSE researchers work on advancing the quality of visual presentations, be it through the recovery and digitization of information used in physical media and dynamic movements or through the geometric modeling of images for new approaches to detect biosignature disease indicators. They also work to create higher-quality models and images of urban scenes for games and homeland security – impacting the lives of virtually everyone.
From universe to earth to nano scale, random phenomena influence behavior. Models and methods are being developed to better understand and predict random behavior to allow for more efficient acquisition of knowledge (design of experiments), improved estimation of system reliability, better characterization of system capability and making more accurate and meaningful inferences from data.