DiMattina, Chris (PhD)
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EducationPh.D. Neuroscience (Systems/Computational), Johns Hopkins University
B.A. Mathematics and Psychology, Cornell University
SpecialtiesVision science, computational neuroscience, cognitive neuroscience
Research and Teaching InterestsI am an experimental vision scientist and computational neuroscientist who is interested in understanding information processing in the early visual system. In collaboration with colleagues at McGill University (Montreal, Canada), I am currently working on understanding how luminance and texture cues are combined to detect image region boundaries. Our ultimate goal is to understand the computations which enable the visual system to segment natural images.
I also have a longstanding interest in applying various machine learning methods (neural networks, adaptive stimulus optimization) to problems in neuroscience and cognitive science, and supervise student research projects in the broad areas of cognition + perception.
Courses OfferedPSB-4002: Physiological Psychology
EXP-3202: Sensation and Perception
PSY-3205: Survey of Analytical Techniques
- DiMattina, C. & Baker, C.L. (2019). Modeling second-order boundary perception: A machine learning approach. PLoS Computational Biology 15(3): e1006829.)
- DiMattina, C. & Zhang, K. (2017). Adaptive Stimulus Optimization. In: Encyclopedia of Computational Neuroscience (2nd Ed.) Springer Science.
- DiMattina, C. (2016). Comparing models of contrast gain using psychophysical experiments. Journal of Vision 16(9): 1-18.
- DiMattina, C., Fox, S.A. & Lewicki, M.S. (2012). "Detecting natural occlusions using local cues." Journal of Vision 12(13):1-21.
- DiMattina, C. & Zhang, K. (2011). "Active data collection for efficient estimation and comparison of nonlinear neural models." Neural Computation 23(9):2242–2288.
- Associate Editor: Frontiers in Psychology: Perception Science
- Associate Editor: Frontiers in Neuroscience: Perception Science