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Department of Computer and Information Sciences
332 John Mulcahy Hall
441 East Fordham Road, Bronx NY 10458
My research studies the computational principles underlying visual perception. I pursue connections between the statistical patterns of the natural world of sights and the resulting representations in the minds of human and animal observers. My work draws on theories in computer vision, machine learning, psychology, biology, and statistics, among other areas. I also dedicate significant attention to the development and application of data analysis techniques to gain better understandings of neural (and other biological) data.
My current projects include:
Computer vision models of object representations in the brain
Realtime fMRI search for visual properties encoded in the brain
Analysis of semantic representations of objects in the brain
Daniel D. Leeds, Darren A. Seibert, John A. Pyles, and Michael J. Tarr (2013). "Comparing visual representations across human fMRI and computational vision", Journal of Vision, 13(13)
Daniel D. Leeds, Darren A. Seibert, John A. Pyles, and Michael J. Tarr (2011). "Unraveling the visual and semantic components of object representation", Vision Sciences Society, Naples, Florida, USA, May 11 - 16.