Education

• Ph.D. in Mechanical and Aerospace Engineering, Princeton University, 2012
• B.S. in Mathematics, Minor in Control and Dynamical Systems, California Institute of Technology, 2006

Previous appointments

• Assistant Professor, Mechanical Engineering, University of Washington, 2014
• Acting Assistant Professor, Applied Mathematics, University of Washington, 2012–2014

Research Statement

Dr. Brunton's research focuses on combining techniques in dimensionality reduction, sparse sensing, and machine learning for the data-driven discovery and control of complex dynamical systems. He is also interested in how low-rank coherent patterns that underlie high-dimensional data facilitate sparse measurements and optimal sensor and actuator placement for control. He is developing adaptive controllers in an equation-free context using machine learning. Specific applications in fluid dynamics include closed-loop turbulence control for mixing enhancement, bio-locomotion, and renewable energy. Other applications include neuroscience, medical data analysis, networked dynamical systems, and optical systems.

Select publications

1. Brunton & Kutz. Data Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge 2019.
2. Brunton, Noack, Koumoutsakos. Machine Learning for Fluid Mechanics. Annual Review of Fluid Mechanics, 52:477–508, 2020.
3. Brunton, Proctor, Kutz. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15):3932—3937, 2016.
4. Kutz, Brunton, Brunton, Proctor. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems. Part of the Other Titles in Applied Mathematics, volume 148, Society for Industrial and Applied Mathematics, 2016.
5. Brunton & Noack. Closed-loop turbulence control: Progress and challenges. Applied Mechanics Reviews, 67(5):050801-1—050801-48, 2015.

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