CRIS

An Interdisciplinary Research Center incorporating Electrical Engineering, Computer Science, Psychology, Economics, and Statistics.

The Center for Research in Intelligent Systems (CRIS) was established at the University of California, Riverside (UCR) in 1998, to promote interdisciplinary research for developing computer systems that are flexible, adaptive and intelligent. The ultimate goal of the Center is the research and development of autonomous/semiautonomous systems with sensing capabilities that are able to communicate and interact with other intelligent (biological and artificial) systems. These intelligent systems will be able to perform tasks that require understanding of the environment through knowledge, learning, reasoning and planning. Advancements in each of the many enabling technologies required represents a major challenge and will have great impact in a wide range of applications, such as autonomous navigation, manufacturing, robotics, photointerpretation, space exploration, document understanding, remote sensing, human-computer interaction, environmental monitoring, image communication, digital libraries, data mining, management, economics and health care.

CRIS involves an interdisciplinary team of faculty members from several departments ( Electrical Engineering, Computer Science and Engineering, Bioengineering, Mechanical Engineering, Entomology, Psychology, Statistics, Botany and Plant Science, Cell Biology & NeuroScience, and Economics). This collaboration encourages greater indepth understanding and broader perspectives than is frequently possible within a single department. CRIS will advance education and research goals of the university through an interdisciplinary graduate program and collaborative research in the intelligent systems area.

Examples of research topics that CRIS pursues include robust real-world object and target recognition, distributed sensor networks, multimedia interactive distributed dynamic databases, biometrics and security, computer vision and pattern recognition, machine learning and data mining, autonomous navigation, perception-based intelligent systems and others.

Examples of collaborated research projects are bioinformatics, wireless video sensor networks, biologically motivated computational models, performance modeling and prediction, biological databases and handling uncertainty in databases.