Timothy Fasel

Timothy Fasel attended Case Western Reserve University in Cleveland, Ohio, where he graduated summa cum laude with a B.S.E. in Aerospace Engineering in 2002. He was admitted to, and attended, the prestigious Los Alamos Dynamics Summer School at Los Alamos National Laboratory (LANL) in the summer of 2001. Through this experience he became interested in the field of Structural Health Monitoring (SHM) and met Dr. Michael Todd, who was then working at the Naval Research Laboratory in Washington, D.C. When Dr. Todd became a professor in the Structural Engineering Department at the University of California San Diego (UCSD) in 2003, Tim immediately recognized that enrolling at UCSD and working for Dr. Todd would be an excellent opportunity to continue his education in the field of SHM (as well as to enjoy the outstanding San Diego weather). Tim enrolled in the Ph.D. program at UCSD in the fall of 2003 and was awarded a National Defense Science and Engineering Graduate Fellowship from 2004-2007. He also received support from the newly formed Engineering Institute, which is a collaboration between LANL and the UCSD Jacobs School of Engineering whose mission is to develop a comprehensive approach for conducting mission-driven, multidisciplinary engineering research. Tim’s Ph.D. research focused on nonlinear time series analysis, pattern recognition and classification, and the use of novel piezoelectric transducers used in combination to detect, locate, and classify incipient damage in bolted and bonded structural connections. He completed his Ph.D. in 2009 after authoring 6 peer-reviewed journal articles and 10 conference proceedings. After graduation Tim has worked at a San Diego consulting engineering firm as an Analysis Engineer with expertise in the static, dynamic, nonlinear, and aeroelastic analysis of mechanical and aerospace systems using FEMAP and NASTRAN and specializing in the areas of signal processing, statistics, and pattern recognition.

Ph.D. Dissertation: “Chaotic Ultrasonic Excitation and Statistical Pattern Recognition for Structural Damage Classification”

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