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About

Jennifer Brown is a leading figure in bioinspired robotics and hydrodynamic sensing, whose work bridges biology and engineering to understand how aquatic animals perceive their environment. Her research centers on artificial lateral lines—sensor arrays modeled after the flow-sensing organs in fish—and their application to autonomous underwater vehicles. In her highly cited 2012 paper (119 citations), Brown provided foundational insights into how pressure sensors can detect hydrodynamic features like Kármán vortex streets and uniform flows from a fish’s perspective, using digital particle image velocimetry to map the sensing environment. This work directly informed the development of FILOSE for Svenning (2014, 74 citations), a bioinspired robotic fish that demonstrated how evolutionarily optimized flow sensing can replace traditional propeller-driven designs. Brown’s contributions have been instrumental in advancing energy-efficient, maneuverable underwater robots capable of navigating complex flows without active propulsion. Her research not only deepens our understanding of sensory biology but also paves the way for next-generation autonomous systems in ocean exploration and environmental monitoring.