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About

Thom Maughan is a leading researcher in marine robotics and autonomous ocean sampling, whose work bridges the gap between robotic control systems and oceanographic science. His primary research focuses on developing adaptive sampling strategies that enable autonomous underwater vehicles (AUVs) and Lagrangian drifters to intelligently track and characterize dynamic oceanographic features, such as fronts and eddies. Maughan’s most influential contribution is his 2012 paper on coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters, which has garnered 72 citations and established a foundational methodology for using mixed robotic platforms to tag and follow advecting water patches. He has also pioneered mixed-initiative, multi-robot field experiments, demonstrating how human operators and autonomous systems can collaborate effectively in complex marine environments. His work on momentum-based front detection methods and space-time tradeoffs in autonomous sampling has advanced the ability of robots to detect and respond to biologically active frontal zones in real time. Maughan’s compact models for adaptive sampling further enhance the efficiency of data collection in sparse, unpredictable ocean processes, making him a key figure in the evolution of intelligent, autonomous ocean observation systems.