Justin Bradley

Justin Bradley is an Associate Professor in the Computer Science Department at North Carolina State University. Previously, he held the position of Richard L. and Carol S. McNeel Associate Professor of Computing in the School of Computing at the University of Nebraska-Lincoln.

His educational background includes:

• Ph.D. in Aerospace Engineering from the University of Michigan (2014)
• M.S. in Aerospace Engineering from the University of Michigan (2012)
• M.S. in Electrical Engineering from Brigham Young University (2007)
• B.S. in Computer Engineering from Brigham Young University (2005)

Dr. Bradley's research focuses on cyber-physical systems with particular emphasis on control, autonomy, software, and computing for aerospace vehicles. A key theme in his work involves developing algorithms that can dynamically adjust their resource utilization in response to uncertainty while maintaining performance requirements. His research spans several critical areas including Artificial Intelligence and Intelligent Agents, Cyber-Physical Systems, and Embedded and Real-Time Systems, with applications primarily in unmanned aircraft systems (UAS) operating in challenging environments like wetlands and fire-prone areas.

His scientific achievements have been recognized with prestigious awards including:

• 2021 NSF CAREER award
• AIAA Associate Fellow designation

Dr. Bradley has been actively involved in research funding and mentoring. His NSF CAREER award (NSF-2047971) supports work on "co-regulation" algorithms for unmanned aircraft systems. He has co-directed research labs including the MAGICC lab at BYU, the A2Sys lab at the University of Michigan, and the Nebraska Intelligent MoBile Unmanned System (NIMBUS) lab since 2015. His research group focuses on developing performance-adjustable, resource-aware algorithms for autonomous systems, with applications in environmental monitoring, wetland studies, and fire management. His educational efforts target K-12 through adult engagement to expand the cyber-physical systems educational pipeline.

His laboratory work centers around the development of the Co-regulated Hybrid Systems (CHS) framework, Co-regulated Real-Time Kernel (CRTK), and co-regulated Markov Decision Process (MDP) for resource-aware autopilots. These technologies enable unmanned systems to dynamically reallocate computing resources while maintaining mission objectives, particularly valuable in complex environments like rainforests where systems must transition between surveillance, sampling, and sensor emplacement tasks.