People | Steve Brunton's Lab (2024)

Selected publications:

• RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology (Gong, Erichson, Kelly, Tutoiu, Schowengerdt, Brunton, Seibel)

Selected publications:

Robust Principal Component Analysis for Particle Image Velocimetry (Scherl, Strom, Shang, Williams, Polagye, Brunton)

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Website: https://www.isabelscherl.com/

Selected publications:

• Robust flow reconstruction from limited measurements via sparse representation (Callaham, Maeda, Brunton)

Selected publications:

Multiresolution Convolutional Autoencoders (Liu, Ponce, Brunton, Kutz)

Selected publications:

Characterizing Magnetized Plasmas with Dynamic Mode Decomposition (Kaptanoglu, Morgan, Hansen, Brunton)

2020-present

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2020-present​

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2020-present

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Postdoctoral Fellow 2019-2020, Mentors: Steven L. Brunton and J. Nathan Kutz

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PhD 2019, Applied Mathematics, University of Washington (Advisors: Steven Brunton and Nathan Kutz)

Thesis: From data to dynamics: discovering governing equations from data

Selected publications:

Data-driven discovery of coordinates and governing equations (Champion, Lusch, Kutz, Brunton)

Postdoctoral Fellow 2020, Mentors: Steven L. Brunton and J. Nathan Kutz

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PhD 2020, Applied Mathematics, University of Washington (Advisors: Steven Brunton and Nathan Kutz)

Thesis: Data-driven discovery and model reduction of complex systems

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Selected publications:

PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data (de Silva, Champion, Quade, Loiseau, Kutz, Brunton)

Postdoctoral Fellow 2016-2018,

Mentors: Steven L. Brunton, J. Nathan Kutz, and Bing W. Brunton

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PhD 2016, Applied Mathematics, University of Washington (Advisor: Nathan Kutz)

Thesis: Machine learning and data decompositions for complex networked dynamical systems

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Selected publications:

• Deep learning for universal linear embeddings of nonlinear dynamics (Lusch, Kutz, Brunton)

Postdoctoral fellow 2017-2018

Mentors: J. Nathan Kutz and Steven L. Brunton

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PhD 2017, Statistics, St. Andrews (Advisor: Carl Donovan)

Thesis: Randomness as a computational strategy: On matrix and tensor decompositions

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Selected publications:

• Randomized matrix decompositions in R (Erichson, Voronin, Brunton, Kutz)

• Compressed dynamic mode decomposition for real-time object detection (Erichson, Brunton, Kutz)

PhD 2020, Physics, University of Washington

Advisors: Steven L. Brunton and Nathan Kutz

Selected publications:

Greedy Sensor Placement with Cost Constraints (Clark, Askham, Brunton, Kutz)

PhD 2020, Applied Mathematics, University of Washington

Advisors: Steven L. Brunton and Nathan Kutz

Selected publications:

PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data (de Silva, Champion, Quade, Loiseau, Kutz, Brunton)

Discovery of Physics from Data: Universal Laws and Discrepancy Models (de Silva, Higdon, Brunton, Kutz)

PhD 2020, Mechanical Engineering, University of Washington

Advisors: Steven Brunton and Tom Daniel

Selected publications:

Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data (Mohren, Daniel, Brunton, Brunton)

PhD 2019, Applied Mathematics, University of Washington

Advisors: Steven L. Brunton and Nathan Kutz

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Selected publications:
Data-driven discovery of coordinates and governing equations (Champion, Lusch, Kutz, Brunton)

Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings (Champion, Brunton, Kutz)

PhD 2019, Applied Mathematics, University of Washington

Advisors: Steven L. Brunton and Nathan Kutz

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Selected publications:

• Data-driven discovery of partial differential equations (Rudy, Proctor, Brunton, Kutz)

• Deep learning of dynamics and signal-noise decomposition with time-stepping constraints (Rudy, Kutz, Brunton)

PhD 2019, Mechanical Engineering, University of Washington

Advisors: Steven L. Brunton and Brian Polagye

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Selected publications:

• Intracycle angular velocity control of cross-flow turbines (Strom, Brunton, Polagye)

PhD 2018, Applied Mathematics, University of Washington

Advisors: Steven L. Brunton and J. Nathan Kutz

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Selected publications:

• Data-driven sparse sensor placement (Manohar, Brunton, Kutz, Brunton)

• Predicting shim gaps in aircraft assembly with machine learning and sparse sensing (Manohar, Hogan, Buttrick, Banerjee, Kutz, Brunton)

PhD 2018, Mechanical Engineering, University of Washington

Advisor: Steven L. Brunton

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Selected publications:

• Data-driven methods in fluid dynamics: Sparse classification from experimental data (Bai, Brunton, Brunton, Kutz, Kaiser, Spohn, Noack)

• Dynamic mode decomposition for compressive system identification (Bai, Kaiser, Proctor, Kutz, Brunton)

PhD 2016, Mechanical Engineering, University of Washington
Advisors: Per Reinhall and Steven L. Brunton


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Selected publications:

• Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data (Au-Yeung, Reinhall, Bardy, Brunton)

Masters Degree 2015, Mechanical Engineering, University of Washington 

Advisors: Steve Brunton and Jim Riley

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Selected publications:

• Lagrangian coherent structures and inertial particle dynamics (Madhavan, Brunton, Riley)

Bachelor's Degree 2017, Computer Engineering, University of Washington

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High school intern, (2017-2019; Interlake High School, Bellevue, WA)

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​High school intern, (2019-2020; Interlake High School, Bellevue, WA)

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