Emily Jensen

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About

Emily Jensen is a leading researcher at the intersection of human-robot interaction, adaptive training systems, and formal methods for robotics. Her work addresses a critical workforce challenge: the need for scalable, personalized training for operators of robotic and autonomous systems. In her highly cited 2024 paper, "Automated Assessment and Adaptive Multimodal Formative Feedback Improves Psychomotor Skills Training Outcomes in Quadrotor Teleoperation" (4 citations), she developed a novel system that automatically assesses trainee performance and delivers adaptive, multimodal feedback—dramatically improving skill acquisition in quadrotor teleoperation. This work demonstrates her commitment to bridging the gap between current training limitations and future workforce demands. Jensen also made foundational contributions to formal specification languages with her paper "Temporal Behavior Trees: Robustness and Segmentation" (3 citations), where she introduced Temporal Behavior Trees (TBTs) and the concept of trace segmentation for optimally partitioning robot behavior traces. Her research uniquely combines rigorous theoretical formalism with practical, human-centered applications, positioning her as a rising star in robotics and human factors engineering.