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.

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.