Patrick Dumond
Full Professor
Engineering Design and Teaching Innovation,
University of Ottawa
Professor Patrick Dumond joined the Department of Mechanical Engineering as an assistant professor, where he teaches courses in engineering design in May 2016. Currently, Professor Dumond is a full professor at the School of Engineering Development and Teaching Innovation at the University of Ottawa. He strongly believes in experiential learning and is actively involved in the ongoing development of the Centre for Entrepreneurship and Engineering Design (CEED) at the University of Ottawa and advises many students involved in pre-professional engineering competitions.
PUBLICATIONS
Professor Dumond's research interests include vibration/acoustic system design, the development of inverse eigenvalue methods for vibration system design, mechanical/biomechanical design, design theory and musical acoustics. He also has a soft spot for understanding variations in the mechanical properties of wood and how these can be controlled during the design and manufacturing process.
DEGREES
2015
2010
2007
Ph.D., Mechanical Engineering,
University of Ottawa, Canada
M.A.Sc., Mechanical Engineering, University of Ottawa, Canada
B.A.Sc., Mechanical Engineering, University of Ottawa, Canada
ACADEMIC HONOURS
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Nominated for the Canadian Engineering Education Association Ron Britton Award
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Engineering Education Vanguard Award - Awarded annually to a person who, early in their career, has committed to the development of engineering education in Canada through practice and/or research (2019)
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Personal acknowledgment by the dean of engineering for teaching quality (2014-2016)
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3rd place finalist for entrepreneurship, Programme Chef de File 3.0 du RGA (2014)
TEACHING
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GNG 5300X – Personal Development and Communication
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GNG 5300J – Introduction to Engineering and Its Impact on Society
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GNG 5310B – Teamwork and Communication
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GNG 5140 - Engineering Design
DIRECTED STUDIES
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Vibration Analysis for Machine Health Monitoring
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Structural Acoustics of Deterministic and Random Phenomena
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Machine Learning Techniques for Mechanical Sensor Data
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Vibration-Based Condition Monitoring
