Miguel Eckstein

Biography

Miguel Eckstein earned a Bachelor Degree in Physics and Psychology at UC Berkeley and a Doctoral Degree in Cognitive Psychology at UCLA. He then worked at the Department of Medical Physics and Imaging, Cedars Sinai Medical Center and NASA Ames Research Center before moving to UC Santa Barbara. He is recipient of the Optical Society of America Young Investigator Award, the Society for Optical Engineering (SPIE) Image Perception Cum Laude Award, Cedars Sinai Young Investigator Award, the National Science Foundation CAREER Award, the National Academy of Sciences Troland Award, and a Guggenheim Fellowship. He has served as the chair of the Vision Technical Group of the Optical Society of America, chair of the Human Performance, Image Perception and Technology Assessment conference of the SPIE Medical Imaging Annual Meeting, Vision Editor of the Journal of the Optical Society of America A, the board of directors of the Vision Sciences Society, the board of editors of Journal of Vision, and as a member of National Institute of Health study section panels on Mechanisms of Sensory, Perceptual and Cognitive Processes and Biomedical Imaging Technology.

Research

Finding your toothbrush, recognizing a face, or an object all might seem effortless but behind the scenes the brain devotes over 1/4 of its neural machinery to make these complex tasks seem easy. How does the brain do it? My research uses a wide variety of tools including behavioral psychophysics, eye tracking, electro-encephalography (EEG), functional magnetic resonance imaging (fMRI) and computational modeling to understand how the brain successfully achieves these everyday perceptual tasks. The investigations involve understanding basic visual perception, eye movements, visual attention, perceptual learning and decision making. I utilize the gained knowledge about how the brain accomplishes every day vision in combination with engineering tools to advance various applied problems: 1) understanding visual, cognitive and decision processes by which doctors detect and classify abnormalities in medical images and developing computer models to improve the way in which we display medical images so that doctors can do fewer errors in clinical diagnosis; 2) develop with engineers bio-inspired computer vision systems; 3) improve the interactions between robots/computer systems and humans.