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About

Stephanie Quinn is a human factors and human-robot interaction researcher whose work centers on the critical intersection of autonomous systems, intelligent agents, and human performance in complex, multitasking environments. Her research has made notable contributions to the field of human-agent teaming, particularly through her sustained investigation of RoboLeader, an intelligent route-planning agent designed to coordinate teams of ground robots in dynamic battlefield scenarios. Quinn's most influential work examines how varying levels of autonomy (LOA) affect operator performance across demanding concurrent tasks — including convoy navigation, target detection, and situation awareness maintenance — providing empirically grounded insights for the design of military robotics systems. Her studies, which employ methodological tools such as eye tracking and simulated environments, have accumulated over 48 citations, reflecting their value to both researchers and defense applications. Notably, Quinn also explored how individual differences among operators shape human-agent teaming effectiveness, and how display dimensions and viewpoint synchronization influence teleoperation performance — broadening the practical scope of her findings. Collectively, her research offers essential guidance for designing autonomous systems that meaningfully support, rather than overwhelm, human operators managing multiple robotic assets in high-stakes settings.