Predoctoral fellowships available at UCSD

[Gary Cottrell]

UCSD has obtained a $3.4M grant NSF IGERT grant for
interdisciplinary predoctoral training in "Vision and
Learning in Humans and Machines". Current Ph.D. applicants
to UCSD should apply also to this program if they are
interested in these topics.

Please see my home page for details. 

The text below is extracted from the grant application:

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; c) 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 time
is ripe to apply our knowledge of human vision to the
application of computer vision algorithms.  Simultaneously
we believe that the consideration of why vision is difficult
for computers can give great insight to experimentalists
examining the human and animal visual systems. Similarly,
new techniques from computational learning will advance
computer vision, while the high dimensional nature of video
data will challenge current learning algorithms.  {\em 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 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.  Example research studies might
include, but are not limited to, machine learning applied to
machine vision; extensions of machine learning to well-known
human learning methods, such as imitation; a study of how
humans solve some of the hardest problems in machine vision
-- e.g., viewpoint variation, lighting variation,
deformation of non-rigid objects, etc.; or the study of how
children learn to see. {\em 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.}

Our plan is to use the very successful dual mentor approach
that has been employed here by the La Jolla Interfaces in
Science Program to encourage students to use the techniques
from at least two areas in their research. 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. Monthly
faculty/fellow dinner meetings with students presenting
their individual projects will keep everyone informed of
progress.  We will balance on-campus training with summer
internships in industry.

Gary Cottrell 858-534-6640 FAX: 858-534-7029 
Faculty Assistant: none assigned, but try:
Jennifer Dickson at (858) 534-5948 jdickson at cs.ucsd.edu
Computer Science and Engineering 0114
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3101 Applied Physics and Math Building
University of California San Diego			-E.M. Forster
La Jolla, Ca. 92093-0114

Email: gary at ucsd.edu
Home page: http://www-cse.ucsd.edu/~gary/
Lab Phone: 858-822-3521