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About

Steven L. Brunton is a prominent researcher at the intersection of data-driven methods, dynamical systems, and control engineering, whose work has fundamentally shaped how scientists and engineers approach complex system modeling in the age of machine learning. His landmark textbook, *Data-Driven Science and Engineering* (2019), has become an essential resource for the field, accumulating over 1,100 citations and elegantly unifying machine learning, mathematical physics, and control theory into a coherent framework for modern practitioners. Brunton has made significant contributions to Dynamic Mode Decomposition (DMD), a technique for extracting meaningful spatial and temporal patterns from high-dimensional time series data, with applications spanning fluid mechanics, robotics, and neuroscience. His research also extends into cutting-edge domains including soft robotics, where he has explored physics-based modeling of embodied intelligence, and safe physics-informed machine learning, addressing the critical challenge of integrating physical constraints and safety guarantees into learned models. More recently, his group has pushed boundaries in micro aerial vehicle control using deep-learned robust controllers. Brunton's work is distinguished by its breadth, pedagogical clarity, and commitment to bridging theoretical rigor with real-world engineering applications.