Project Profile
Vision and Learning in Humans and Machines
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University of California at San Diego
Abstract
Consider creating (a) a computer system to help physicians make a diagnosis using all of a patient’s medical data and images along with millions of case histories; (b) intelligent buildings and cars that are aware of their occupants activities; © personal digital assistants that watch and learn your habits —… more »
Consider creating (a) a computer system to help physicians make a diagnosis using all of a patient’s medical data and images along with millions of case histories; (b) intelligent buildings and cars that are aware of their occupants activities; © personal digital assistants that watch and learn your habits — not only gathering information from the web but recalling where you had left your keys; or (d) a computer tutor that watches a child as she performs a science experiment. Each of these scenarios requires machines that can see and learn, and while there have been tremendous advances in computer vision and computational learning, current computer vision and learning systems for many applications (such as face recognition) are still inferior to the visual and learning capabilities of a toddler. Meanwhile, great strides in understanding visual recognition and learning in humans have been made with psychophysical and neurophysiological experiments. The intellectual merit of this proposal is its focus on creating novel interactions between the four areas of: computer and human vision, and human and machine learning. We believe these areas are intimately intertwined, and that the synergy of their simultaneous study will lead to breakthroughs in all four domains.
Our goal in this IGERT is therefore to train a new generation of scientists and engineers who are as versed in the mathematical and physical foundations of computer vision and computational learning as they are in the biological and psychological basis of natural vision and learning. On the one hand, students will be trained to propose a computational model for some aspect of biological vision and then design experiments (fMRI, single cell recordings, psychophysics) to validate this model. On the other hand, they will be ready to expand the frontiers of learning theory and embed the resulting techniques in real-world machine vision applications. The broader impact of this program will be the development of a generation of scholars who will bring new tools to bear upon fundamental problems in human and computer vision, and human and machine learning.
We will develop a new curriculum that introduces new cross-disciplinary courses to complement the current offerings. In addition, students accepted to the program will go through a two-week boot camp, before classes start, where they will receive intensive training in machine learning and vision using MatLab, perceptual psychophysics, and brain imaging. We will balance on-campus training with summer internships in industry.
IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In this sixth year of the program, awards are being made to institutions for programs that collectively span the areas of science and engineering supported by NSF
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