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Daniel Doyle

Research Assistant Professor, Aerospace and Ocean Systems Lab

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danieldoyle@vt.edu  •  540-231-6140

1311 Research Center Drive
Blacksburg, VA 24060
United States

Daniel Doyle

Research Assistant Professor, Aerospace and Ocean Systems Lab

Daniel Doyle currently serves as a Research Assistant Professor at Virginia Tech in the Aerospace and Ocean Systems Lab, Hume Center for National Security and Technology.  His research focus areas are in computer vision, object tracking, sense-making, autonomous drones, and spaceflight programs.

Prior to coming to Virginia Tech, Dr. Doyle served 21 years in the United States Air Force and retired as a Lieutenant Colonel.  He last served as the Special Access Programs Branch Chief in the Plans and Programs Directorate, Headquarters Air Force, Pentagon.  Dr. Doyle helped to develop and defend the Special Access Programs portfolio and managed the corporate process, integrating all Air Force program data and information.  He directed and coordinated the integration of Major Command inputs and evaluated program data to support resourcing decisions through Programmed Objective Memorandum and Program Budget Review.

Doyle entered active duty in 1997 after graduating from Western Michigan University with his Bachelor of Science in Aeronautical Engineering.  During his Air Force service, he gained a variety of acquisition and leadership experience in program management, flight test, career field management, MH-53/UH-1N sustainment, standardization and evaluation, officer training, B-2 engine field support, and power systems research. 

Doyle earned his Master of Science degree in Mechanical Engineering from North Carolina State University, Raleigh, North Carolina, and his Doctor of Philosophy in Mechanical Engineering from the Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio. His doctoral work focused on optical flow background estimation using multiple cameras and culminated in a patent in 2017 towards a 'Real-Time camera tracking system using optical flow feature points'.