- 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
- Assistant Professor, Mechanical Engineering, University of Washington, 2014
- Acting Assistant Professor, Applied Mathematics, University of Washington, 2012–2014
Dr. Brunton has been an assistant professor in the Department of Mechanical Engineering at UW since 2014. He was previously an acting assistant professor in the Department of Applied Mathematics at UW. His 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.
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems.
Brunton, Proctor, Kutz, Proceedings of the National Academy of Sciences, 113(15):3932—3937, 2016.
- Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems.
Kutz, Brunton, Brunton, Proctor, SIAM Text 149 in Other Topics Series, 2016.
- Machine Learning Control – Taming Nonlinear Dynamics and Turbulence.
Duriez, Brunton, Noack, Springer Text 116 in Fluid Mechanics and Its Applications Series, 2016.
- Network structure of two-dimensional isotropic turbulence.
Taira, Nair, Brunton, Journal of Fluid Mechanics, 795(R2):1—11, 2016.
- Closed-loop turbulence control: Progress and challenges.
Brunton & Noack, Applied Mechanics Reviews, 67(5):050801-1—050801-48, 2015.