RuCCS participates in a graduate training program in perceptual science supported by an IGERT grant from the National Science Foundation.
Training in perception is an interdisciplinary effort designed to give students a solid background in basic perceptual phenomena and formal models of perception drawn from computer science. Training emphasizes both empirical and theoretical issues, and this is accomplished through jointly-supervised research projects and cross-disciplinary course work. The perception community at Rutgers, drawn from the Departments of Psychology, Biomedical Engineering, Computer Science, Linguistics, as well as RuCCS and the Laboratory of Vision Research, covers a wide variety of topics in both early and high-level perception. Faculty maintain active and visible research programs, with the participation of graduate students and postdoctoral researchers. Faculty in perception work at maintaining close ties across academic disciplines and individual areas of expertise, and actively collaborate in supervision of student research.
Principal topics of collaborative efforts are the perception of motion, texture, color, shape and depth, attention, eye movements, motor control, object recognition and classification, with application to both human and machine perception.
Training centers around the jointly-supervised research and also relevant cross-disciplinary courses. These courses, whose development was supported by the NSF IGERT grant, include two foundational courses in Computer Science (CS 503, Computational Thinking, and CS 504, Computational Modeling) which are structured to give all students – regardless of undergraduate experience in computer science – a thorough grounding in the development of computational and cognitive models and their application to human and computational perception. An interdisciplinary laboratory course (Perceptual Science 521, 522, Integrated Methods in Perceptual Science), led by interdisciplinary faculty, provide groups of students with the opportunity to work in teams on original projects that combine computational and experimental aspects.