1. Bioengineering and Life Sciences (BELS)
The mission of the BELS program is to offer Minors and Areas of Special Interest (ASI) at the undergraduate and support areas of specialization at the graduate level, as well as to enable research opportunities for CSM students bioengineering and the life sciences. Biology is becoming increasingly significant in fulfilling the role and mission of the Colorado School of Mines. Many intellectual frontiers within the fields of environment, energy, materials, and their associated fields of science and engineering are being driven by advances in the biosciences.
This minor areas of study is an alternative available to engineering students seeking to have a direct impact on meeting the basic needs of humanity. The focus is the intersection of society, culture, and technology. Technologically oriented humanitarian projects typically address fundamental human needs such as food, water, water treatment, shelter, and power.
3. Multidisciplinary Engineering Laboratory
This is a three course sequence that replaces the traditional, subject specific lab experience. Labs are constructed so that students must approach in an inquiry mode rather than as fill in the blank exercises and each lab integrates several physical principles as well as a range of measurement methods. Computing and data acquisition is strongly embedded into this lab sequence.
4. Minority Engineering Program (MEP)
The minority engineering program provides a center for minority student atitieiss and a place for students to become a community of scholars with common goals and objectives in a comfortable learning environment.
William Hoff, Associate Professor
Professor Hoff discusses the ARGUS (Activity Recognition Understanding System) project with graduate student Bao Nguyen. The system monitors activity in a large building using a suite of inexpensive passive infrared motion detectors. The system could help make buildings safer and more secure, while preserving people’s privacy. A key challenge is automatically discovering patterns of activity from the huge amount of data that is collected. Long term patterns such as how late people work and when and where meetings occur could be used to improve energy efficiency, for example. The project is being performed in collaboration with Professor Mike Colagrosso in Math and Computer Sciences department.
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