Overview
Leila Bridgeman joined Duke as an assistant professor of mechanical engineering and materials science on January 1, 2018.
She received B.Sc. and M.Sc. degrees in Applied Mathematics in 2008 and 2010 from McGill University, Montreal. In 2016, she completed a Ph.D. in Mechanical engineering, also at McGill University.
Her doctoral research involved a return to the foundational work of George Zames, exploring how the theory of conic sectors can be used to design controllers that guarantee closed-loop input-output stability when more conventional methods fail to apply. Her graduate studies involved research semesters at University of Michigan, University of Bern, and University of Victoria, along with an internship at Mitsubishi Electric Research Laboratories (MERL) in Boston.
Through her research, Leila strives to bridge the gap between theoretical results in robust and optimal control and their use in practice. She explores how the tools of numerical analysis and input-output stability theory can be applied to the most challenging of controls problems, including the control of delayed, open-loop unstable, and nonminimum-phase systems. Her focus has been on the development of readily-applicable controller synthesis and stability analysis methods based on the evaluation of linear matrix inequalities (LMIs). Resulting publications have considered applications of this work to robotic, process control, and time-delay systems.
She is also interested in model predictive control (MPC) especially when applied to switched systems. Bridgeman continues to collaborate with colleagues at MERL, enabling the use of MPC in novel applications including networked systems, vehicle control, heating, and ventilation.
Current Duke Appointments & Affiliations
Recent Scholarly Works
Design and Evaluation of a Compliant Quasi-Direct Drive End-Effector for Safe Robotic Ultrasound Imaging
Journal article IEEE Transactions on Medical Robotics and Bionics · January 1, 2026 Robot-assisted ultrasound scanning promises to advance autonomous and accessible medical imaging. However, ensuring patient safety and compliant human-robot interaction during probe contact poses a significant challenge. Most existing systems either have h ... Full text CiteData-driven certified control barrier functions
Journal article Systems & Control Letters · January 2026 Full text CiteDissipativity-augmented multiobjective control of networks
Journal article International Journal of Control · December 2, 2025 Full text CiteRecent Grants
Title: NRT-FW-HTF: NSF Traineeship in the Advancement of Surgical Technologies
Inst. Training Prgm or CMECo-Principal Investigator · Awarded by National Science Foundation · 2021 - 2027Collaborative Research: Direct Data-Driven Positive, Control, and Contractive Invariant Sets
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2023 - 2026Robust, Distributed Control for Coordinating Aerial Vehicles
ResearchPrincipal Investigator · Awarded by Office of Naval Research · 2022 - 2025View All Grants