Brief Bio
Long Bio
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Dr. Christopher Stauffer was born in Minneapolis, Minnesota. In 1995,
he graduated from Northwestern University with
Bachelor of Science degrees in Electrical Engineering, Computer
Science, and Biomedical Engineering with premed qualifications. In
his junior year, he participated in St. Olaf's Global
Semester Program where he studied in Greece, Switzerland, Israel,
Egypt, India, Nepal, Hong Kong, China, and Japan. A more complete
description is available on his Personal Page. In 2002, he
defended his Ph.D. dissertation at the Massacusetts Institute of Technology
in Electrical Engineering and Computer Science.
His research is focused on developing computational analogs for
basic human perception and exploiting the strengths of computers to
take full advantage of these capabilities. His research over the past
eight years has centered on the development of systems that are
capable of: automatically tracking multiple objects in real-time
across multiple overlapping and non-overlapping cameras in
unstructured indoor and outdoor environments; automatically modeling
the types of objects in a particular environment; automatically
modeling the activities that these objects perform; learning patterns
of the activities over periods of time; and detecting unusual objects
or behavior. To see the specifics of his work, please see his Publications and Research pages. More personal
information is available on his Personal page.
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Chris Stauffer was born in Minneapolis, Minnesota to Dr. William
M. Stauffer and Ruth T. Stauffer. He attended Edina High School
except for a short summer program learning French as he biked around
the Normandy Region in northwest france. He is an avid skier and
swimmer, going to the state swim meet as a sophomore, junior, and
senior primarily due to his unhealthy obsession with the butterfly
stroke.
In 1990, he entered Northwestern University. Northwestern's
swimming program was a bit to much, so he concentrated on
acedemics. He began as an electrical engineering and computer science
student, but quickly broadened his interest to include biomedical
engineering. He graduated in 5 years with BS's in electrical
engineering, computer science, and biomedical engineering with pre-med
qualifications. Part of the reason for his 5th year at Northwestern
was his participation in St. Olaf's Global
Semester Program where he studied in Greece, Switzerland, Israel,
Egypt, India, Nepal, Hong Kong, China, and Japan. A more complete
description is available on his Personal Page.
In 1995, he drove all his possessions to Boston, Massachusetts to
begin his tenure at Massachusetts
Institute of Technology in the EECS Department. As a student
in the Artificial Intelligence
Laboratory, he was initially a student of Tomas
Lozano-Perez on the Intelligent
Room Project while completing his masters on occupancy mapping.
It was with the HCI project that he developped his first tracking
system.
In 1997, he began to work with Eric Grimson. As part
of the Vision
Group, he developped an adaptive background estimation technique
that enabled active tracking of objects in unstructured outdoor
environments. This ability to actively track multiple objects
continuously over days/weeks/months/years played a central role in the
Forest of Sensors
Project, where he began investigation into automatic bootstrapping
of intelligent perceptual capabilities from basic attention.
Over the next eight years, he investigated unsupervised and
semi-supervised approaches to inferring the characteristics of active
objects in an environment under observation. In 2003, he completed
his PhD in electrical engineering and computer science. Since then,
he has been a research associate at the MIT CSAIL.
His research is focused on developing computational analogs for
basic human perception and exploiting the strengths of computers to
take full advantage of these capabilities. His research over the past
eight years has centered on the development of systems that are
capable of: automatically tracking multiple objects in real-time
across multiple overlapping and non-overlapping cameras in
unstructured indoor and outdoor environments; automatically modeling
the types of objects in a particular environment; automatically
modeling the activities that these objects perform; learning patterns
of the activities over periods of time; and detecting unusual objects
or behavior. To see the specifics of his work, please see his Publications and Research pages.
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CV and App Materials
Resume.doc
Resume.pdf
ResearchStatement.pdf
TeachingStatement.pdf
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