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Steve Brunton

Faculty Photo

Data Science Fellow, eScience Institute

James B. Morrison Endowed Career Development Professor in Mechanical Engineering
Mechanical Engineering

Adjunct Professor
Applied Mathematics

James B. Morrison Career Development Professor


  • 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.