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About

Andrew Lamperski is a researcher whose work bridges the frontiers of robotics, hybrid systems, and neuroscience. His key research areas include bio-inspired robotics, the mathematical analysis of hybrid dynamical systems, and the optimization of clinical treatments for neurological disorders. Lamperski’s major contributions are twofold: he has pioneered the use of neuromechanical principles—such as antenna-based tactile sensing in cockroaches—to design robust wall-following controllers for wheeled robots (garnering over 80 citations), and he has made foundational theoretical advances in hybrid systems theory, notably developing Lyapunov methods to characterize Zeno stability—a phenomenon where infinite discrete transitions occur in finite time (77 citations). His work has had significant impact, with his most-cited papers accumulating hundreds of citations. Notably, Lamperski has also applied control theory to medicine, co-authoring a highly cited study on semi-automated optimization of deep brain stimulation parameters for Parkinson’s disease (38 citations). More recently, he has explored the explore-versus-exploit trade-off in biological organisms, demonstrating how mode-switching strategies can optimize decision-making. Lamperski’s research exemplifies how insights from biology can inspire robotic design, while rigorous mathematics enables breakthroughs in both engineering and clinical practice.