steven l. brunton - university of washingtonfaculty.washington.edu/sbrunton/cv.pdf · steven l....

Steven L. BruntonJames A. Morrison Associate Professor

Mechanical Engineering, University of Washington

MEB 305 eigensteve.comUniversity of Washington [email protected], WA 98195 cell: +1-(609)-921-6415

Research Interests

• Flow control, fluid transport phenomena, and turbulent mixing enhancement

• Data science and machine learning for modeling and control

• Model reduction, sparse sensing, and feedback control of high-dimensional dynamical systems

• Adaptive and robust control techniques for energy optimization and conversion

Affiliations

University of Washington Seattle, WA 98195

(Sept. 2018 – present) Associate Professor of Mechanical Engineering,(Sept. 2018 – present) Adjunct Associate Professor of Applied Mathematics,(Sept. 2014 – present) Data Science Fellow, eScience Institute,(Sept. 2014 – 2018) Assistant Professor of Mechanical Engineering,(Sept. 2014 – 2018) Adjunct Assistant Professor of Applied Mathematics,(Sept. 2012 – Sept. 2014) Acting Assistant Professor of Applied Mathematics.

Education

Princeton University Princeton, NJ 08544

Ph.D. in Mechanical and Aerospace Engineering, 2012Advisor: Clarence W. RowleyThesis: Unsteady aerodynamic models for agile flight at low Reynolds numbers.

California Institute of Technology Pasadena, CA 91125

B.S. in Mathematics, Minor in Control and Dynamical Systems, 2006Advisor: Jerrold E. MarsdenThesis: Rank-1 saddle transport in three or more degrees of freedom scattering reactions.

Awards & Honors

• Presidential Early Career Award for Scientists and Engineers (PECASE) [2019]• SIAM CSE Early Career Prize [2019]• UW College of Engineering Junior Faculty Award [2018]• AFOSR Young Investigator Award [2017]• ARO Young Investigator Award [2017]• UW College of Engineering Faculty Teaching Award [2017]• Data Science Fellow, eScience Institute [2014]• Athena-Feron Award for Mathematical Excellence at Princeton [2007]• Princeton MAE Second Year Graduate Fellowship [2007]• Gordon Wu Graduate Fellowship to attend Princeton [2006-2010]

CV-1

Books

3. S. L. Brunton, and J. N. KutzData Driven Science and Engineering:Machine Learning, Dynamical Systems, and ControlCambridge 2019. databookuw.com

2. T. Duriez, S. L. Brunton, and B. R. NoackMachine Learning Control –Taming Nonlinear Dynamics and TurbulenceSpringer 2016.

1. J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. ProctorDynamic Mode Decomposition: Data-Driven Modeling of ComplexSystemsSIAM 2016.

Book Chapters

6. J.-Ch. Loiseau, B. R. Noack, and S. L. BruntonFrom the POD-Galerkin method to sparse manifold modelsHandbook on Model Reduction, 2019.

5. S. L. Brunton and J. N. KutzData-driven methods for reduced order modelingHandbook on Model Reduction, 2019.

4. J. N. Kutz, S. Sargsyan, and S. L. BruntonLeveraging sparsity and compressive sensing for reduced order modelingMoRePaS, 2016.

3. Z. Bai, S. L. Brunton, B. W. Brunton, J. N. Kutz, E. Kaiser, A. Spohn, and B. R. NoackData-driven methods in fluid dynamics: Sparse classification from experimental dataWhither Turbulence and Big Data in the 21st Century (Springer, 2016).

2. J. N. Kutz, S. L. Brunton, and X. FuData methods and computational tools for characterizing complex cavity dynamicsNonlinear Optical Cavity Dynamics: From Microresonators to Fiber Lasers, P. Grelu Ed. (Wiley-VCH Verlag GmbH & Co. KGaA, 2016).

1. J. N. Kutz, X. Fu, S. L. Brunton, and J. GrosekDynamic mode decomposition for robust PCA with applications to foreground/background sub-traction in video streams and multi-resolution analysisCRC Handbook on Robust Low-Rank and Sparse Matrix Decomposition: Applications in Imageand Video Processing, T. Bouwmans Ed. (CRC Press, 2015).

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Journal Publications

75. S. L. Brunton, B. R. Noack, and P. Koumoutsakos.Machine Learning for Fluid Mechanics.To appear in Annual Review of Fluid Mechanics, 2020.

74. K. Taira, M. S. Hemati, S. L. Brunton, Y. Sun, K. Duraisamy, S. Bagheri, S. T. M. Dawson,and C.-A. Yeh.Modal Analysis of Fluid Flows: Applications and Outlook.To appear in AIAA Journal, 2019

73. S. Li, E. Kaiser, S. Laima, H. Li, S. L. Brunton, and J. N. Kutz.Discovering time-varying aeroelastic models of a long-span suspension bridge from field measure-ments by sparse identification of nonlinear dynamical systems.To appear in Physical Review E, 2019.

72. S. H. Rudy, S. L. Brunton, and J. N. Kutz.Smoothing and parameter estimation by soft-adherence to governing equations.To appear in Journal of Computational Physics, 2019.

71. N. B. Erichson, L. Mathelin, J. N. Kutz, and S. L. Brunton.Randomized dynamic mode decomposition.To appear in SIAM Journal on Applied Dynamical Systems, 2019.

70. N. B. Erichson, P. Zeng, K. Manohar, S. L. Brunton, J. N. Kutz, and A. Y. Aravkin.Sparse principal component analysis via variable projection.To appear in SIAM Journal of Applied Mathematics, 2019.

69. S. H. Rudy, J. N. Kutz, and S. L. Brunton.Deep learning of dynamics and signal–noise decomposition with time-stepping constraints.To appear in Journal of Computational Physics, 2019.

68. Z. Bai, E. Kaiser, J. L. Proctor, B. W. Brunton, J. N. Kutz, and S. L. BruntonDynamic mode decomposition for compressive system identificationTo appear in AIAA Journal, 2019.

67. C. Gong, N. B. Erichson, J. P. Kelly, L. Trutoiu, B. T. Schowengerdt, S. L. Brunton, and E. J.Seibel.RetinaMatch: Efficient Template Matching of Retina Images for Teleopthamology.IEEE Transactions on Medical Imaging, 38(8):1993–2004, 2019.

66. A. Nair, C.-A. Yeh, E. Kaiser, B. Noack, S. L. Brunton, and K. Taira.Cluster-based feedback control of turbulent post-stall separated flows.Journal of Fluid Mechanics, 875:345–375, 2019.

65. S. L. Brunton and J. N. Kutz.Data-driven model discovery for materials.Journal of Physics: Materials, 2:044002, 2019.

64. N. B. Erichson, S. Voronin, S. L. Brunton, and J. N. KutzRandomized Matrix Decompositions using R.Journal of Statistical Software, 89(11):1–48, 2019.

63. S. Rudy, A. Alla, S. L. Brunton, and J. N. Kutz.

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Data-driven identification of parametric partial differential equations.SIAM Journal on Applied Dynamical Systems, 18(2):643–660, 2019.

62. E. Clark, T. Askham, S. L. Brunton, and J. N. Kutz.Greedy sensor placement with cost constraints.IEEE Sensors Journal, 19(7):2642–2656, 2019.

61. N. M. Mangan, T. Askham, S. L. Brunton, J. N. Kutz, and J. L. Proctor.Model selection for hybrid dynamical systems via sparse regression.Proceedings of the Royal Society A, 475(20180534), 2019.

60. K. P. Champion, S. L. Brunton, and J. N. Kutz.Discovery of nonlinear multiscale systems: Sampling strategies and embeddings.SIAM Journal on Applied Dynamical Systems, 18(1):312–333, 2019.

59. K. Manohar, E. Kaiser, S. L. Brunton, and J. N. KutzOptimized sampling for multiscale dynamics.SIAM Multiscale Modeling and Simulation, 17(1):117–136, 2019.

58. P. Zheng, T. Askham, S. L. Brunton, J. N. Kutz, and A. Y. Aravkin.A Unified Framework for Sparse Relaxed Regularized Regression: SR3.IEEE Access, 7(1):1404–1423, 2019.

57. S. Gupta, P. Malte, S. L. Brunton, and I. Novosselov.Prevention of Lean Flame Blowout Using a Predictive Chemical Reactor Network Control.Fuel, 236:583–588, 2019.

56. Y. Hu, S. L. Brunton, N. Cain, S. Mihalas, J. N. Kutz, and E. Shea-BrownFeedback through graph motifs relates structure and function in complex networks.Physical Review E, 98:062312, 2018.

55. B. Lusch, J. N. Kutz, and S. L. Brunton.Deep learning for universal linear embeddings of nonlinear dynamics.Nature Communications, 9(1):4950, 2018.

54. E. Kaiser, J. N. Kutz, and S. L. BruntonSparse identification of nonlinear dynamics for model predictive control in the low-data limit.Proceedings of the Royal Society A, 474(2219), 2018.

53. J. N. Kutz, S. L. Brunton, and J. L. ProctorKoopman theory for partial differential equations.Complexity, 2018, 6010634, 2018.

52. M. Au-Yeung, P.G. Reinhall, G. Bardy, and S. L. BruntonDevelopment and validation of warning system of ventricular tachyarrhythmia in patients withheart failure with heart rate variability data.PLoS ONE, 13(11):e0207215, 2018.

51. T. Mohren, T. L. Daniel, S. L. Brunton, and B. W. Brunton.Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data.Proceedings of the National Academy of Sciences, 115(42):10564–10569, 2018.

50. K. Manohar, T. Hogan, J. Buttrick, A. G. Banerjee, J. N. Kutz, and S. L. BruntonPredicting shim gaps in aircraft assembly with machine learning and sparse sensing.Journal of Manufacturing Systems, 48(Part C):87–95, 2018.

49. B. Strom, S. L. Brunton, and B. Polagye

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Advanced control methods for cross-flow turbines.International Marine Energy Journal, 1(2):129–138, 2018.

48. M. Quade, M. Abel, J. N. Kutz, and S. L. Brunton.Sparse identification of nonlinear dynamics for rapid model recovery.Chaos, 28(6):063116-1–063116-10, 2018.

47. A. G. Nair, S. L. Brunton, and K. TairaNetworked oscillator based modeling and control of unsteady fluid flows.Physical Review E., 97(6):063107-1–063107-14, 2018.

46. W. Guo, K. Manohar, S. L. Brunton, and A. G. BanerjeeSparse-TDA: Sparse realization of topological data analysis for multi-way classification.IEEE Transactions on Knowledge and Data Engineering, 30(7):1403–1408, 2018.

45. K Manohar, B. W. Brunton, J. N. Kutz, and S. L. BruntonData-Driven Sparse Sensor Placement.IEEE Control Systems Magazine, 38(3):63–86, 2018 (invited).

44. J. C. Loiseau, B. R. Noack, and S. L. BruntonSparse reduced-order modeling: Sensor-based dynamics to full-state estimation.Journal of Fluid Mechanics, 844:459–490, 2018.

43. J. L. Proctor, S. L. Brunton, and J. N. KutzGeneralizing Koopman theory to allow for inputs and control.SIAM Journal of Dynamical Systems, 17(1):909–930, 2018.

42. S. Sargsyan, S. L. Brunton, and J. N. KutzOnline interpolation point refinement for reduced order models using a genetic algorithm.SIAM Journal on Scientific Computing, 40(1):B283–B304, 2018.

41. T. Baumeister, S. L. Brunton, and J. N. KutzDeep learning and model predictive control for self-tuning mode-locked lasers.J. Optical Society of America B, 35(3): 617–626, 2018.

40. J. C. Loiseau and S. L. BruntonConstrained sparse Galerkin regression.Journal of Fluid Mechanics, 838:42–67, 2018.

39. E. Kaiser, M. Morzynski, G. Daviller, J. N. Kutz, B. Brunton, and S. L. BruntonSparsity enabled cluster reduced-order modeling for control.Journal of Computational Physics, 352:388–409, 2018.

38. K. Taira, S. L. Brunton, S. T. M. Dawson, C. W. Rowley, T. Colonius, B. J. McKeon, O.Schmidt, S. Gordeyev, V. Theofilis, and L. S. UkeileyModal Analysis of Fluid Flows: An Overview.AIAA Journal, 55(12):4013–4041, 2017.

37. N. M. Mangan, J. N. Kutz, S. L. Brunton, and J. L. ProctorModel selection for dynamical systems via sparse regression and information criteria.Proceedings of the Royal Society A, 473: 1–16, 2017

36. B. Strom, S. L. Brunton, and B. PolagyeIntracycle angular velocity control of cross-flow turbines.Nature Energy, 2(17103):1–9, 2017.

35. S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, and J. N. Kutz

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Chaos as an intermittently forced linear system.Nature Communications, 8(19):1–9, 2017.

34. S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. KutzData-driven discovery of partial differential equations.Science Advances, 3:e1602614, 2017.

33. S. L. Brunton, J. N. Kutz, and J. L. ProctorData-driven discovery of governing physical laws.SIAM News, 50(1), 2017.

32. K. Manohar, S. L. Brunton, and J. N. KutzEnvironment identification in flight using sparse approximation of wing strain.Journal of Fluids and Structures, 70:162–180, 2017.

31. J. M. Kunert, J. L. Proctor, S. L. Brunton, and J. N. KutzSpatiotemporal feedback and network structure drive and encode Caenorhabditis elegans loco-motion.PLoS Computational Biology, 13(1):e1005303, 2017.

30. N. M. Mangan, S. L. Brunton, J. L. Proctor, and J. N. KutzInferring biological networks by sparse identification of nonlinear dynamics.IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, Special Issue onBiological Applications of Information Theory in Honor of Claude Shannon’s Centennial – Part1, 2(1):52–63, 2016.

29. N. B. Erichson, S. L. Brunton, and J. N. KutzCompressed Dynamic Mode Decomposition for Real-Time Object Detection.Journal of Real-Time Image Processing, 1–14, 2016.

28. B. W. Brunton, S. L. Brunton, J. L. Proctor, and J. N. Kutz.Sparse sensor placement optimization for classification.SIAM Journal on Applied Mathematics, 76(5):2099–2122, 2016.

27. J. L. Proctor, S. L. Brunton, and J. N. KutzIncluding inputs and control within equation-free architectures for complex systems. (invitedreview)European Physical Journal Special Topics, 225:2413–2434, 2016.

26. V. Parezanovic, L. Cordier, T. Duriez, A. Spohn, B. R. Noack, J.-P. Bonnet, M. Segond, M. Abel,and S. L. BruntonFrequency selection by feedback control in a turbulent shear-flow.Journal of Fluid Mechanics, 797:247–283, 2016.

25. S. L. Brunton, J. L. Proctor, and J. N. Kutz.Discovering governing equations from data: Sparse identification of nonlinear dynamical systems.Proceedings of the National Academy of Sciences, 113(15):3932-3937, 2016.

24. K. Taira, A. G. Nair, and S. L. BruntonNetwork Structure of Two-Dimensional Isotropic Turbulence.Journal of Fluid Mechanics, 795(R2):1–11, 2016.

23. S. Madhavan, S. L. Brunton, and J. J. RileyFinite-time Lyapunov exponents for inertial particles in an unsteady fluid.Physical Review E, 93:033108, 2016.

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22. S. L. Brunton, B. W. Brunton, J. L. Proctor, and J. N. Kutz.Koopman invariant subspaces and finite linear representations of nonlinear dynamical systemsfor control.PLoS ONE, 11(2):e0150171, 2016.

21. J. N. Kutz, X. Fu, and S. L. Brunton.Multi-resolution dynamic mode decomposition.SIAM Journal of Applied Dynamical Systems, 15(2):713–735, 2016.

20. J. L. Proctor, S. L. Brunton, and J. N. Kutz.Dynamic mode decomposition with control.SIAM Journal of Applied Dynamical Systems, 15(1):142–161, 2016.

19. M. C. Johnson, S. L. Brunton, N. B. Kundtz, and J. N. Kutz.Extremum-seeking control of the beam pattern of a reconfigurable holographic metamaterialantenna.Journal of the Optical Society of America, A, 33(1):59–68, 2016.

18. S. L. Brunton, J. L. Proctor, and J. N. Kutz.Compressive sampling and dynamic mode decomposition.Journal of Computational Dynamics, 2(2):165–191, 2015.

17. J. N. Kutz and S. L. Brunton.Intelligent systems for stabilizing mode-locked lasers and frequency combs: Machine learning andequation-free control paradigms for self-tuning optics.Nanophotonics, 4:459–471, 2015.

16. S. Sargsyan, S. L. Brunton, and J. N. KutzNonlinear model reduction for complex systems using sparse optimal sensor locations from learnednonlinear libraries.Physical Review E, 92(3):033304-1–033304-13, 2015.

15. S. L. Brunton, and B. R. NoackClosed-loop turbulence control: Progress and challenges.Applied Mechanics Reviews, 67(5):050801-1–050801-48, 2015.

14. M. C. Johnson, S. L. Brunton, J. N. Kutz, and N. B. Kundtz.Sidelobe canceling for optimization of reconfigurable holographic metamaterial antenna.IEEE Transactions on Antennas and Propagation, 63(4):1881–1886, 2015.

13. V. Parezanovic, J. C. Laurentie, J. Delville, L. Cordier, C. Fourment, A. Spohn, B. R. Noack,J.-P. Bonnet, T. Shaqarin, M. Segond, M. Abel, and S. L. Brunton.Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learningturbulence control.The Journal of Flow, Turbulence and Combustion, 94(1):155–173, 2015.

12. S. L. Brunton, J. H . Tu, I. Bright, and J. N. Kutz.Compressive sensing and low-rank libraries for classification of bifurcation regimes in nonlineardynamical systems. arXiv:1312.4221 [math.DS]SIAM Journal of Applied Dynamical Systems, 13(4):1716–1732, 2014

11. J. L. Proctor, S. L. Brunton, B. W. Brunton, and J. N. Kutz.Exploiting sparsity and equation-free architectures in complex systems.The European Physical Journal Special Topics (EPJ-ST), 223: 2665–2684, 2014. (invited review)

10. J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz.

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Generalizing dynamic mode decomposition to a larger class of datasets. arXiv:1312.0041 [math.NA]Journal of Computational Dynamics, 1(2):391–421, 2014.

9. D. M. Luchtenburg, S. L. Brunton, and C. W. Rowley.Long-time uncertainty propagation using generalized polynomial chaos and flow map composition.Journal of Computational Physics, 274: 783–802, 2014.

8. S. L. Brunton, S. T. M. Dawson, and C. W. Rowley.State-space identification of reduced-order unsteady aerodynamic models for feedback control.Journal of Fluids and Structures, 50:253–270, 2014.

7. S. L. Brunton, X. Fu, and J. N. Kutz.Self-tuning fiber lasers.IEEE Journal of Special Topics in Quantum Electronics, 20(5), 2014.

6. X. Fu, S. L. Brunton, and J. N. Kutz.Classification of birefringence in mode-locked fiber lasers using machine learning and sparse rep-resentation.Optics Express, 22(7):8585–8597, 2014.

5. S. L. Brunton, X. Fu, and J. N. Kutz.Extremum-seeking control of a mode-locked laser.IEEE Journal of Quantum Electronics, 49(10):852–861, 2013.

4. S. L. Brunton, C. W. Rowley, and D. R. Williams.Reduced-order unsteady aerodynamic models at low Reynolds numbers.Journal of Fluid Mechanics, 724:203–233, 2013.

3. S. L. Brunton and C. W. Rowley.Empirical state-space representations for Theodorsen’s lift model.Journal of Fluids and Structures, 38:174–186, 2013.

2. S. L. Brunton, C. W. Rowley, S. R. Kulkarni, and C. Clarkson.Maximum power point tracking for photovoltaic optimization using ripple-based extremum seek-ing control.IEEE Transactions on Power Electronics, 25(10):2531–2540, 2010.

1. S. L. Brunton and C. W. Rowley.Fast computation of finite-time Lyapunov exponent fields for unsteady flows.Chaos 20(1), 2010.

Submitted for Publication

15. C.-H. Walter, K. S. Lerch, S. L. Brunton, and G. BrennerAnalysis of Detached Flows in Turbomachines by Dynamic Mode Decomposition, 2019.

14. S. Ouala, S. L. Brunton, D. Nguyen, L. Drumetz and R. FabletLearning Constrained Dynamical Embeddings for Geophysical Dynamics, 2019.

13. B. de Silva, D. M. Higdon, S. L. Brunton, and J. N. Kutz.Discovery of physics from data: Universal laws and discrepancy models, 2019.

12. K. Bieker, S. Peitz, S. L. Brunton, J. N. Kutz, and M. Dellnitz.Deep Model Predictive Control with Online Learning for Complex Physical Systems, 2019.

11. K. Champion, P. Zheng, A. Y. Aravkin, S. L. Brunton, and J. N. Kutz.

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A unified sparse optimization framework to learn parsimonious physics-informed models fromdata, 2019.

10. I. Scherl, B. Strom, J. K. Shang, O. Williams, B. L. Polagye, and S. L. Brunton.Robust Principal Component Analysis for Particle Image Velocimetry, 2019.

9. K. Champion, B. Lusch, J. N. Kutz, and S. L. Brunton.Data-driven discovery of coordinates and governing equations, 2019.

8. N. B. Erichson, L. Mathelin, Y. Zhewei, S. L. Brunton, M. Mahoney, and J. N. Kutz.Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data.

7. K. Manohar, J. N. Kutz, and S. L. Brunton.Optimal sensor and actuator placement using balanced model reduction.

6. J. L. Callaham, K. Maeda, and S. L. Brunton.Robust flow reconstruction from limited measurements via sparse representation.

5. M. Kamb, E. Kaiser, S. L. Brunton, and J. N. Kutz.Time-Delay Observables for Koopman: Theory and Applications.

4. N. B. Erichson, L. Mathelin, S. L. Brunton, and J. N. Kutz.Diffusion maps meet Nystrom.

3. E. Kaiser, J. N. Kutz, and S. L. BruntonData-driven discovery of Koopman eigenfunctions for control.Submitted to Automatica.

2. S. D. Pendergrass, J. N. Kutz, and S. L. BruntonStreaming GPU Singular Value and Dynamic Mode Decompositions.

1. N. B. Erichson, K. Manohar, S. L. Brunton, and J. N. KutzRandomized CP Tensor Decomposition.

Conference Papers

28. K. Kaheman, E. Kaiser, B. Strom, J. N. Kutz, and S. L. Brunton.Learning Discrepancy Models from Experimental Data, 2019.To appear in the Conference on Decision and Control, December 2019.

27. J. N. Kutz, S. Rudy, A. Alla and S. L. Brunton.Data-driven discovery of governing physical laws and their parametric dependencies in engineer-ing, physics and biology.IEEE ICASSP, Curacao, 2018.

26. E. Kaiser, J. N. Kutz, and S. L. Brunton.Discovering conservation laws from data for control.Conference on Decision and Control, December 2018.

25. Mathieu Le Provost, David R. Williams, and S. L. Brunton.SINDy analysis of disturbance and plant model superposition on a rolling delta wing.AIAA Aviation, Atlanta, GA, June 2018.

24. S. L. Brunton.Flow map composition to identify coherent structures.ISFV, Zurich, Switzerland, June 2018.

23. S. L. Brunton.Machine learning of dynamics with applications to flow control and aerodynamic optimization.

CV-9

IUTAM, Santorini, Greece, June 2018.

22. J.-Ch. Loiseau, N. Deng, L. Pastur, M. Morzynski, B. R. Noack, and S. L. Brunton.Sparse reduced-order modeling of the fluidic pinball.GDR Contrle des dcollements, 2017.

21. J. N. Kutz, N. B. Erichson, T. Askham, S. Pendergrass, and S. L. Brunton.Dynamic Mode Decomposition for Background Modeling.ICCVW, 2017.

20. N. B. Erichson, S. L. Brunton, and J. N. Kutz.Compressed Singular Value Decomposition for Image and Video Processing.ICCVW, 2017.

19. K. Taira, A. G. Nair, and S. L. Brunton.Vortex interaction analysis using complex network framework.Annual Meeting of the Japan Society of Fluid Mechanics, Nagoya, Japan, September, 2016.

18. K. Taira, A. G. Nair, and S. L. Brunton.Complex network analysis of unsteady fluid flows.ICTAM, Montreal, Canada, August, 2016.

17. S. L. Brunton, J. L. Proctor, and J. N. Kutz.Sparse Identification of Nonlinear Dynamics with Control (SINDYc).NOLCOS, Monterey CA, August, 2016.

16. B. Strom, S. L. Brunton, A. Aliseda, and B. Polagye.Comparison of acoustic Doppler and particle image velocimetry characterization of a cross-flowturbine wake.Proceedings of the 4th Marine Energy Technology Symposium, Washington D.C., April, 2016.

15. S. L. Brunton, J. N. Kutz, and X. Fu.Self-tuning fiber lasers.SPIE Photonics West, paper 9728-61, 2016.

14. B. Strom, S. L. Brunton, and B. Polagye.Consequences of preset pitch angle for cross flow turbines.11th European Wave and Tidal Energy Conference, Nantes, France, September 5-11, 2015.

13. B. Strom, S. L. Brunton, and B. Polagye.Hydrodynamic optimization of cross-flow turbines with large chord to radius ratios.Proceedings of the 3th Marine Energy Technology Symposium, Washington D.C., April, 2015.

12. J. N. Kutz, X. Fu, and S. L. Brunton.Machine learning for self-tuning optical systems.Proceedings of the World Congress on Engineering, 1:70–73, 2015.

11. J. N. Kutz, X. Fu, and S. L. Brunton.Multi-resolution analysis of dynamical systems using dynamic mode decomposition.Proceedings of the World Congress on Engineering, 1:90–93, 2015.

10. M. C. Johnson, S. L. Brunton, N. B. Kundtz, and J. N. Kutz.An Extremum-Seeking Controller for Dynamic Metamaterial Antenna Operation.IEEE APWC, 2015.

9. J. N. Kutz, X. Fu, and S. L. Brunton.Self-tuning fiber lasers: machine learning applied to optical systems.Nonlinear Photonics, July 2014.

8. M. C. Johnson, S. L. Brunton, J. N. Kutz, and N. B. Kundtz.

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Sidelobe canceling on a reconfigurable holographic metamaterial antenna.IEEE APWC, 2014.

7. T. Duriez, V. Parezanovic, J.-C. Laurentie, C. Fourment, J. Delville, J.-P. Bonnet, L. Cordier, B.R. Noack, M. Segond, M. W. Abel, N. Gautier, J.-L. Aider, C. Raibaudo, C. Cuvier, M. Stanislas,and S. L. Brunton.Closed-loop control of experimental shear flows using machine learning (Invited).AIAA Paper 2014-XXXX, 7th Flow Control Conference, June 2014.

6. S. L. Brunton, C. W. Rowley, and D. R. Williams.Linear unsteady aerodynamic models from wind tunnel measurements.AIAA Paper 2011-3581, 41st Fluid Dynamics Conference and Exhibit, June 2011.

5. S. L. Brunton, and C. W. Rowley.Low-dimensional state-space representations for classical unsteady aerodynamic models.AIAA Paper 2011-476, 49th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2011.

4. S. L. Brunton, and C. W. Rowley.Unsteady aerodynamic models for agile flight at low Reynolds numbers.AIAA Paper 2010-552, 48th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2010.

3. S. L. Brunton, C. W. Rowley, S. R. Kulkarni, and C. Clarkson.Maximum power point tracking for photovoltaic optimization using extremum seeking.34th IEEE Photovoltaic Specialist Conference, June 2009.

2. S. L. Brunton, and C. W. Rowley.Modeling the unsteady aerodynamic forces on small-scale wings.AIAA Paper 2009-1127, 47th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2009.

1. S. L. Brunton, C. W. Rowley, K. Taira, T. Colonius, J. Collins, and D. R. Williams.Unsteady aerodynamic forces on small-scale wings: Experiments, simulations & models.AIAA Paper 2008-520, 46th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2008.

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Funding – Active ($4,000k as PI, $3,250k out of $19,500k as Co-PI)

15. $7,500,000 AFOSR MURI. “Neural-inspired sparse sensing and control for agile flight.” (Co-PI,Bingni Brunton PI)

14. $2,000,000 from The Boeing Company. “Boeing Data Science Research.” (PI)

13. $1,000,000 ARO PECASE. “PECASE: Uncovering Nonlinear Flow Physics with Machine Learn-ing Control and Sparse Modeling.” (PI)

12. $1,000,000 from DARPA. “Physics Inspired Learning and Learning the Order and Structure ofPhysics” (Co-PI, Nathan Kutz PI)

11. $210,000 from The Boeing Company. “Bracket Standardization.” (PI)

10. $450,000 AFOSR YIP. “YIP: Interpretable Nonlinear Models of Unsteady Flow Physics.” (PI)

9. $360,000 ARO YIP. “YIP: Uncovering Nonlinear Flow Physics with Machine Learning Controland Sparse Modeling.” (PI)

8. $6,000,000 ARO MURI. “From Data-Driven Operator Theoretic Schemes to Prediction, Inferenceand Control of Systems.” (Co-PI, Igor Mezic PI)

7. $150,000 from The Boeing Company. “Automated Fiber Placement.” (Co-PI, Ashis Banerjee PI)

6. $333,333 from The Boeing Company. “Multi-Robot Control.” (Co-PI, Santosh Devasia PI)

5. $750,00 from AFOSR. “Network-based feedback control of fluid flows.” (Co-PI, Sam Taira PI)

4. $450,000 from ARO. “Active turbulence control from a network-theoretic perspective.” (Co-PI,Sam Taira PI)

3. $1,100,000 from DOE. “3rd Generation integrated instrumentation: Enhancements to the adapt-able monitoring package.” (Co-PI, Brian Polagye PI)

2. $835,000 from DOE. “SWIFT : A rapid approach to evaluating marine energy converter sound.”(Co-PI, Brian Polagye PI)

1. $445,000 from AFRL. “Integrating compressive sensing and machine learning for outer-loop targettracking control on an autonomous quadrotor aircraft.” (Co-PI, B. W. Brunton PI)

Funding – Past ($1,500k as PI, $3,500k as Co-PI)

10. $215,000 from The Boeing Company. “Executive Data Science Workshops.” (PI)

9. $650,000 from the NSF. “MRI: Development of a hyper-sensed environmentally controlled windtunnel” (PI w/ Jeff Riffell, Co-PIs Aliseda, Morgansen, Thornton)

8. $1,000,000 from DARPA. “Koopman operator theory and applications” (Co-PI, Igor Mezic PI)

7. $250,000 AFOSR SBIR. “Scalable Real-Time Background/Foreground Separation using DynamicMode Decomposition.” (Co-PI, Nathan Kutz PI)

6. $1,201,787 from DOE. “Advanced Laboratories and Field Arrays.” (Co-PI, DOE Consortium)

5. $999,190 from DOE. “An intelligent Adaptable Monitoring Package for Marine Renewable EnergyProjects.” (Co-PI, DOE FOA-0000971 Topic 2, Brian Polagye PI)

4. $642,597 from The Boeing Company. “Predictive Shimming.” (PI, Boeing A96600)

3. $277,777 from DOE. “Automatic optical detection and classification of marine animals aroundMHK converters using machine vision.” (PI, DOE EE-0006785)

2. $40,000 from MathWorks. “Closing the gap in MOOCs: Scientific Computing and Data Analyt-ics.” (Co-PI, Nathan Kutz PI)

1. $10,000 Google.“Data-driven characterization and control of complex nonlinear systems.” (PI)

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Teaching

Instructor, University of Washington

•ME565 - Mechanical Engineering Analysis, Winter 2019, 120 students

Course Evaluations: Median 4.6/5.0, Adj. Median 4.7/5.0

•ME564 - Mechanical Engineering Analysis, Fall 2018, 132 students






•ME520 - Control Theory Bootcamp, Winter 2017, 8 students














•AMATH301 - Beginning Scientific Computing, Spring 2014, 309 students

•AMATH301 - Beginning Scientific Computing, Winter 2014, 309 students


•AMATH301 - Beginning Scientific Computing, Fall 2013, 368 students

•AMATH301 - Beginning Scientific Computing, Winter 2013, 300 students


•AMATH500A - Boeing Distinguished Seminar, Fall 2012 - Fall 2013

Teaching Assistant, Princeton University

•MAE434 - Modern Control, Fall 2009

•MAE433 - Automatic Control Systems, Spring 2009, 2010

•MAE331 - Aircraft Flight Dynamics, Fall 2008

Teaching Assistant, California Institute of Technology

•CDS140ab - Introduction to Dynamics, Fall 2005, Spring 2006

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Mentoring/Advising

Current (11 PhDs, 4 Postdocs, 1 Masters, 1 Highschool)

Eurika Kaiser [2016-present]. Moore Sloan Data Science Postdoctoral Fellow, co-advised w/ KutzAditya Nair [2018–present]. Postdoctoral Fellow, co-advised w/ Bing BruntonBenjamin Herrmann [2019-present]. DAAD Fellow, co-advised with Richard SemaanKathleen Champion [2019-present]. Postdoctoral Fellow, co-advised w/ Nathan Kutz

Michelle Hickner [2019-present]. PhD UW ME, co-advised w/ Bing BruntonDan Shea [2019-present]. PhD UW Materials, co-advised w/ Nathan KutzJared Callaham [2018-present]. PhD UW MEKahdirdan Kahirman [2018-present]. PhD UW MEAbhay Gupta [2018-present]. PhD UW ME, co-advised w/ Ashis BanerjeeAriana Mendible [2017-present]. PhD UW ME, co-advised w/ Nathan KutzIsabel Scherl [2017-present]. PhD UW ME, co-advised w/ Brian PolagyeBrian DeSilva [2017-present]. PhD UW AMATH, co-advised w/ Nathan KutzEmily Clark [2016-present]. PhD UW Physics, co-advised w/ Nathan KutzThomas Mohren [2016-present]. PhD UW ME, co-advised w/ Tom DanielChen Gong [2016-present]. PhD UW ME, co-advised w/ Eric Seibel

Kartik Krishna [2018-present]. Masters student at UW

Surtaz Khan [2017-present]. High school student at Lakeside

Lab Alumni (6 PhDs graduated, 3 Postdocs, 3 Masters, 3 Undergraduates)

Kazuki Maeda [2018-2019]. Acting Assistant ProfessorBethany Lusch [2016-2018]. Postdoctoral Fellow, co-advised w/ Bing Brunton and Nathan KutzBen Erichson [2016-2018]. Postdoctoral Fellow, co-advised w/ Nathan Kutz

Kathleen Champion [2017-2019]. PhD UW AMATH, co-advised w/ Nathan KutzSam Rudy [2016-2019]. PhD UW AMATH, co-advised w/ Nathan KutzBen Strom [2014-2019]. PhD UW ME, co-advised w/ Brian PolagyeKrithika Manohar [2013-2018]. PhD UW AMATH, co-advised w/ Nathan KutzZhe Bai [2014-2018]. PhD UW MEMichael Au-Yeung [2014-2016]. PhD UW ME, co-advised w/ Per Reinhall

Ben Erichson [2016]. Visiting PhD student from St. Andrews, co-advised w/ Nathan Kutz

David Law [2015-2016], Masters student at UWTadbhagya Kumar [2015-2016]. Masters student at UW, co-advised w/ Jim RileySudharsan Madhavan [2012-2014]. Masters student at UW, co-advised w/ Jim Riley

Seth Pendergrass [2014-present]. Undergraduate at UWJoseph Sullivan [2013]. Undergraduate at UWAllen Maudlin [2013]. Undergraduate at UW

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Invited Talks (SLB invited)

83. S. L. Brunton, “Discovering Interpretable and Generalizable Dynamical Systems from Data”,MolKyn19, Berlin, Germany, June 2019.

82. S. L. Brunton, “Physics Aware Sparse Sensing and Control”, AFOSR Workshop on Multisen-sory Integration in Insect Flight Dynamics, NCBS Bangalore, India, June 2019.

81. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering Inter-pretable and Generalizable Nonlinear Models”, DTU Applied Mathematics and Computer ScienceDepartment, Copenhagen, Denmark, May 2019.

80. S. L. Brunton, J. N. Kutz, J.-Ch. Loiseau, and B. R. Noack, “Interpretable Nonlinear Modelsof Unsteady Flow Physics”, SIAM DS, Snowbird, UT, May 2019.

79. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering Inter-pretable and Generalizable Nonlinear Models”, OSU Seminar, Ohio State University, Columbus,OH, April, 2019.

78. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering Inter-pretable and Generalizable Nonlinear Models”, Aerospace Engineering Seminar, Georgia Tech,Atlanta, GA, April, 2019.

77. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering In-terpretable and Generalizable Nonlinear Models”, Electrical and Systems Engineering Seminar,Washington University, St. Louis, MO, April, 2019.

76. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering Inter-pretable and Generalizable Nonlinear Models”, SILO Seminar, UW, Madison, WI, April, 2019.

75. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering In-terpretable and Generalizable Nonlinear Models”, Biophysics and Soft Matter Seminar, SimonFraser, Vancouver, BC, Canada, April, 2019.

74. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering Inter-pretable and Generalizable Nonlinear Models”, SIAM CSE, Spokane, WA, February 2019. (EarlyCareer Prize Plenary)

73. S. L. Brunton, J. N. Kutz, J.-Ch. Loiseau, and B. R. Noack, “Discovering Unknown Physics andEnforcing Known Symmetries and Constraints with Machine Learning”, SIAM CSE, Spokane,WA, February 2019.

72. S. L. Brunton, “Machine learning of interpretable nonlinear models for unsteady flow physics”,GAMM, Vienna, Austria, February 2019. (Topical Keynote).

71. S. L. Brunton, “Machine learning of interpretable nonlinear models for unsteady flow physics”,Geophysical flows workshop, Rennes, France, February 2019. (Keynote).

70. S. L. Brunton, J. N. Kutz, J.-Ch. Loiseau, and B. R. Noack, “Interpretable Models of NonlinearFlow Physics,” APS DFD, Atlanta, GA, November 2018.

69. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems: Uncovering Inter-pretable Models of Nonlinear Flow Physics”, Georgia Tech Aero Seminar, Atlanta, GA, November2018.

68. S. L. Brunton, “Machine learning of interpretable nonlinear models for unsteady flow physics,”AI & Geophysical Dynamics, Brest, France, November 2018 (Keynote).

67. S. L. Brunton, “Machine Learning and Sparse Optimization for Aerospace Manufacturing,”

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Boeing Distinguished Research and Scholar Seminar (B-DRASS), Everettt, WA, November 2018.

66. S. L. Brunton, “Physics-aware sparse sensing technology,” Carderock Site visit at UW AppliedPhysics Lab, Seattle, WA, September 2018.

65. S. L. Brunton, “Uncovering nonlinear flow physics with sparse modeling and machine learningcontrol,” ITRI Taiwanese Delegation, Seattle, WA, September 2018.

64. S. L. Brunton, “Data-driven discovery and control of nonlinear systems,” WCCM mini-symposium,New York, NY, July 2018. (Keynote)

63. S. L. Brunton, “Data-driven discovery and control of nonlinear systems,” University of Wash-ington Industrial and Systems Engineering Seminar, Seattle, WA, May 2018.

62. S. L. Brunton, “Data-driven discovery and control of nonlinear systems,” Johns Hopkins Centerfor Environmental and Applied Fluid Mechanics Seminar, Baltimore, MD, April 2018.

61. S. L. Brunton, “Data-driven discovery and control of nonlinear systems,” USC Center forSystems and Control Seminar, Los Angeles, CA, April 2018.

60. S. L. Brunton, “Identifying nonlinear dynamics and intrinsic coordinates under uncertainty,”SIAM UQ, Los Angeles, CA, April 2018.

59. S. L. Brunton, “Data-driven modeling and control of complex systems,” RIKEN Institute,Tokyo, Japan, March 2018.

58. S. L. Brunton, “Data-driven modeling and control of complex systems,” US-Japan Workshopon Bridging Fluid Mechanics and Data Science, Tokyo, Japan, March 2018.

57. S. L. Brunton, “Learning physics and the physics of learning,” TU Munich, Munich, Germany,March 2018.

56. S. L. Brunton, “Sparse sensor placement in dynamical systems,” GAMM Conference, Munich,Germany, March 2018.

55. S. L. Brunton, “Learning physics and the physics of learning,” UWIN Seminar, Seattle, March2018.

54. S. L. Brunton, “Data-driven discovery and control of complex systems,” Seminar at Paderborn,Germany, December 2017.

53. S. L. Brunton, “Machine Learning Control in Turbulence,” DFG Workshop, Goetingham, Ger-many, December 2017. (Keynote)

52. S. L. Brunton, “Koopman operator theory: Past, present, and future,” APS DFD, Denver, CO,November 2017.

51. S. L. Brunton, “Machine Learning to Discover and Control Nonlinear Systems,” West CoastROM Workshop, Berkeley, CA, November 2017. (Keynote)

50. S. L. Brunton, “Predictive shimming: Advanced automated gap-filling with data science,” AOSWorkshop, Seattle, WA, November 2017.

49. S. L. Brunton, “Data-driven characterization and control of complex systems,” IPAM Work-shop, UCLA, CA, November 2017.

48. S. L. Brunton, “Data-Driven Models for Nonlinear Systems,” Set Oriented Numerics Workshop,Santa Barbara, CA, September 2017. (Keynote)

47. S. L. Brunton, “Machine Learning Control,” State of the Art Review (SOAR8), Oxford, UK,July 2017.

46. S. L. Brunton, “Machine Learning to Discover and Control Nonlinear Systems,” wMLC-2 Work-shop, Valenciennes, France, July 2017. (Plenary)

CV-16

45. S. L. Brunton, “Predictive Shimming,” Boeing BARC seminar, Harbor Point Technical Center,Harbor Point, WA, June 2017.

44. S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, and J. N. Kutz. “Hankel alternativeview of Koopman (HAVOK) analysis of chaotic systems,” SIAM DS, Snowbird, UT, May 2017.

43. S. L. Brunton, “Discovering Governing Equations from Data by Sparse Identification of Non-linear Dynamics,” MIT Applied Mathematics Seminar, Cambridge MA, May 2017.

42. S. L. Brunton, “Discovering Governing Equations from Data by Sparse Identification of Non-linear Dynamics,” Virginia Tech Applied Mathematics Seminar, Blacksburg VA, April 2017.

41. S. L. Brunton, “Discovering Governing Equations from Data by Sparse Identification of Non-linear Dynamics,” Harvard Applied Mathematics Seminar, Cambridge MA, April 2017.

40. S. L. Brunton, “Discovering Governing Equations from Data by Sparse Identification of Non-linear Dynamics,” APS March Meeting, New Orleans, LA, March 2017.

39. S. L. Brunton, “Discovering Governing Equations by Sparse Identification of Nonlinear Dynam-ics,” SIAM Conference on Computational Science and Engineering, Atlanta, GA, March 2017.

38. S. L. Brunton, “Data-Driven Discovery and Control of Nonlinear Dynamical Systems,” BanffBIRS Workshop, Banff, Canada, January 2017. (Plenary)

37. S. L. Brunton, “Observing and controlling the nonlinear world in a linear framework,” NeuralComputation and Engineering Connection, Seattle, WA, January 2017.

36. S. L. Brunton, “Discovering and Controlling Nonlinear Dynamical Systems from Data,” CaltechMCE Seminar, Pasadena, CA, January 2017.

35. S. L. Brunton, “Data-Driven Discovery and Control of Complex Systems,” DARPA Workshop,Santa Barbara, CA, November 2016.

34. S. L. Brunton, “Data-Driven Modeling and Control of Nonlinear Systems,” The Future ofVibration Energy Transfer in Solids and Structures Workshop, Seattle, WA, October 2016.

33. S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Sparse Identification of Nonlinear Dynamicswith Control (SINDYc),” NOLCOS, Monterey, CA, August 2016.

32. S. L. Brunton, “Discovering Nonlinear Dynamical Systems from Data,” UTRC, Hartford, CT,July 2016.

31. S. L. Brunton, “Data-Driven Identification of Nonlinear Dynamical Systems with Control usingSparse Regression,” SIAM Annual Meeting, Boston, MA, July 2016.

30. S. L. Brunton, “Predictive Shimming: Advanced Automated Gap-Filling with Data Science,”Boeing Workshop, Seattle WA, June 2016.

29. S. L. Brunton, “A Compressed Overview of Sparsity,” AIAA Aviation Meeting, DC, June 2016.

28. S. L. Brunton, “Data-Driven Modeling and Control via Sparse Sensing and Machine Learning,”Laboratoire d’Hydrodynamique de l’Ecole polytechnique, Paris, France, April 2016.

27. S. L. Brunton, “Data-Driven Modeling and Control via Sparse Sensing and Machine Learning,”Laboratoire d’Informatique pour la Mecanique et les Sciences de l’Ingenieur, France, April 2016.

26. S. L. Brunton, “Discovering Nonlinear Dynamical Systems from Data,” SIAM Conference onUncertainty Quantification, Lausanne, Switzerland, April 2016.

25. S. L. Brunton, “Discovering Nonlinear Dynamical Systems from Data,” Courant Institute ofMathematical Sciences, New York, NY, USA, March, 2016.

24. S. L. Brunton, “Extensions to DMD and Koopman Analysis: Compressed Sensing, Multi-Resolution, and Control,” United Technologies Research Corporation, Hartford, CT, USA, Novem-

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ber, 2015.

23. S. L. Brunton, “Uncertainty quantification (and sensitivity!) in fluid dynamics and control,”International Congress on Industrial and Applied Mathematics, Beijing, China, August, 2015.

22. S. L. Brunton, B. W. Brunton, J. L. Proctor, J. N. Kutz, and B. R. Noack, “Big data solutionsfor active flow control,” Bifurcations in Fluid Dynamics, Paris, France, July, 2015.

21. S. L. Brunton, “Data-driven modeling and control of complex systems: sparse sensing andmachine learning,” SIAM Conference on Control and its Applications, Paris, France, July, 2015.

20. S. L. Brunton, “Compressive sensing and dynamic mode decomposition,” SIAM Conference onApplications of Dynamical Systems, Snowbird, UT, May, 2015.

19. S. L. Brunton, J. N. Kutz, and B. R. Noack “Self-tuning complex systems and data-drivencontrol,” Whither Turbulence and Big Data in the 21st Century, Corsica, France, April, 2015.

18. S. L. Brunton, “Discovering underlying nonlinear dynamics of complex systems from data,”SIAM Conference on Computational Science and Engineering, Salt Lake City, March, 2015.

17. S. L. Brunton, “Data-driven modeling and control towards self-tuning complex systems,” SFB880 Flow Control Workshop, TU-Braunschweig, Germany, February, 2015.

16. S. L. Brunton, “Data-driven modeling and control of complex systems,” TU-Berlin, Germany,Feb., 2015.

15. S. L. Brunton, “Data methods for the control of complex systems: Dimensionality reduction,sparsity, and machine learning,” Berkeley Electrical Engineering Semi-Autonomous Group, Au-gust, 2014.

14. S. L. Brunton, D. M. Luchtenburg, C. W. Rowley, and J. N. Kutz, “Investigating long-timeuncertainty in dynamical systems using flow map composition,” SIAM Conference on UncertaintyQuantification, Savannah Georgia, April 2014.

13. S. L. Brunton, “Data-driven control of complex systems: Dimensionality reduction, sparsity,and uncertainty quantification,” UW Mechanical Engineering Seminar, January 2014.

12. S. L. Brunton, “Flow map composition for non-autonomous dynamical systems,” SIAM Con-ference on Applications of Dynamical Systems, May 2013.

11. S. L. Brunton, “Reduced-order unsteady aerodynamic models for closed-loop feedback control,”PPRIME, Poitiers France, April 2013.

10. S. L. Brunton, “Low-dimensional coherent structures in unsteady fluids,” MIT, Physical Math-ematics Seminar, April 2013.

9. S. L. Brunton, “Reduced order models of unsteady aerodynamic flow for control,” SIAM Con-ference on Computational Science and Engineering, February 2013.

8. S. L. Brunton, “Feedback control of a pitching airfoil based on unsteady lift models,” Universityof Washington, Applied Physics Laboratory, October 2012.

7. S. L. Brunton, “Feedback control of a pitching airfoil based on unsteady lift models,” UnitedTechnologies Research Center, September 2012.

6. S. L. Brunton, S. T. M. Dawson, and C. W. Rowley, “Feedback control of a pitching airfoilbased on unsteady lift models,” 42nd AIAA Fluid Dynamics Conference, June 2012.

5. S. L. Brunton, “Unsteady aerodynamic models for agile flight at low Reynolds number,” Uni-versity of Washington, Applied Math Department, October 2011.

4. S. L. Brunton, C. W. Rowley and D. R. Williams, “Linear unsteady aerodynamic models fromwind tunnel measurements,” 41st AIAA Fluid Dynamics Conference, June 2011.

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3. S. L. Brunton and C. W. Rowley, “Fast computation of time-varying finite time Lyapunovexponents,” SIAM Conference on Applications of Dynamical Systems, May 2011.

2. S. L. Brunton and C. W. Rowley, “Reduced order models for unsteady aerodynamic forces atlow Reynolds numbers,” Illinois Institute of Technology, Mechanical, Materials and AerospaceEngineering, March 2011.

1. S. L. Brunton, “Invariant manifold transport tubes in space mission design & chemical reactiondynamics,” Princeton Program in Applied and Computational Mathematics, Dynamical Systems& Nonlinear Science Seminar, December 2006.

Other Talks

97. J. N. Kutz, B. Lusch, and S. L. Brunton, “Data-driven discovery of Koopman embeddings forspatio-temporal systems,” SIAM DS, Snowbird, UT, May 2019. (invited)

96. K. Manohar, J. N. Kutz, and S. L. Brunton, “Optimal sensor and actuator placement usingbalanced model reduction,” SIAM DS, Snowbird, UT, May 2019. (invited)

95. E. Kaiser, S. L. Brunton, and J. N. Kutz, “Discovering conservation laws from data for control,”SIAM DS, Snowbird, UT, May 2019. (invited)

94. S. T. M. Dawson and S. L. Brunton, “Data-driven modeling of unsteady aerodynamic systems,”SIAM DS, Snowbird, UT, May 2019. (invited)

93. A. G. Nair, C.-A. Yeh, E. Kaiser, B. R. Noack, S. L. Brunton, and K. Taira, “Design of FeedbackControl Laws for Turbulent Post-Stall Separated Flows,” SIAM DS, Snowbird, UT, May 2019.(invited)

92. N. M. Mangan, T. Askham, S. L. Brunton, J. N. Kutz, and J. L. Proctor, “Sparse modelselection for structurally challenging, dynamic, biological systems,” SIAM DS, Snowbird, UT,May 2019. (invited)

91. K. Bieker, S. Peitz, S. L. Brunton, J. N. Kutz, and M. Dellnitz, “Deep Learning and Model Pre-dictive Control for a Flow Control Problem,” Seventeenth International Conference on NumericalCombustion, Aachen, Germany, May 2019.

90. B. R. Noack, D. Fan, Y. Zhou, R. Li, E. Kaiser, and S. L. Brunton, “Turbulence control –Better, faster and easier with machine learning,” PCM-CMM, Krakow, Poland, 2019. (plenary)

89. J. L. Callaham, K. Maeda, and S. L. Brunton, “Robust reconstruction of flow fields from limitedmeasurements,” SIAM CSE, Spokane, WA, February 2019. (invited)

88. Z. Bai, K. T. Carlberg, L. Peng, and S. L. Brunton, “Nonintrusive nonlinear model reductionvia machine-learning approximations to low-dimensional operators,” SIAM CSE, Spokane, WA,February 2019. (invited)

87. E. Kaiser, S. L. Brunton, and J. N. Kutz, “Exploiting sparsity for modeling and control ofdynamical systems,” SIAM CSE, Spokane, WA, February 2019. (invited)

86. J. N. Kutz, S. Rudy, and S. L. Brunton, “Data-driven discovery of governing physical laws andtheir parametric dependencies,” SIAM CSE, Spokane, WA, February 2019. (invited)

CV-19

85. A. Mendible, S. L. Brunton, and J. N. Kutz, “The space-time problem with model reductionfor traveling waves,” SIAM CSE, Spokane, WA, February 2019. (invited)

84. A. G. Nair, C.-A. Yeh, E. Kaiser, B. R. Noack, S. L. Brunton, and K. Taira, “Data-driven feed-back control strategies for unsteady flows,” SIAM CSE, Spokane, WA, February 2019. (invited)

83. K. Champion, S. L. Brunton, and J. N. Kutz, “Data-driven discovery of nonlinear dynamics,”SIAM CSE, Spokane, WA, February 2019.

82. S. Rudy, J. N. Kutz, and S. L. Brunton, “Deep learning of signal-noise decomposition,” SIAMCSE, Spokane, WA, February 2019. (invited)

81. K. Manohar, J. N. Kutz, and S. L. Brunton, “Greedy sensor placement for controlling high-dimensional dynamics,” SIAM CSE, Spokane, WA, February 2019. (invited)

80. K. Maeda, and S. L. Brunton, “Data-driven modeling and analysis of non-stationary fluidflows,” SIAM CSE, Spokane, WA, February 2019. (invited)

79. E. Kaiser, J. N. Kutz, and S. L. Brunton, “Sparse identification of nonlinear dynamics formodel predictive control in the low-data limit,” APS DFD, Atlanta, GA, November 2018.

78. S. Rudy, J. N. Kutz, and S. L. Brunton, “Deep learning of dynamics and signal-noise decom-position with time-stepping constraints,” APS DFD, Atlanta, GA, November 2018.

77. I. Scherl, K. Maeda, B. Polagye, and S. L. Brunton, “Robust Principal Component Analysis ofCorrupted Flow Fields,” APS DFD, Atlanta, GA, November 2018.

76. A. G. Nair, C.-An. Yeh, E. Kaiser, B. R. Noack, S. L. Brunton, and K. Taira, “Optimalcluster-based feedback control for separated flows,” APS DFD, Atlanta, GA, November 2018.

75. Z. Bai, N. B. Erichson, M. G. Meena, K. Taira, and S. L. Brunton, “Sparse and randomizedsampling methods for scalable turbulent flow networks,” APS DFD, Atlanta, GA, November2018.

74. J. Callaham, K. Maeda and S. L. Brunton, “Robust reconstruction of flow fields from limitedmeasurements,” APS DFD, Atlanta, GA, November 2018.

73. T. L. Mohren, S. L. Brunton, B. W. Brunton, and T. L. Daniel, “Neural-Inspired sparse wingsensors for insect flight”, APS DFD, Atlanta, GA, November 2018.

72. B. Strom, I. Scherl, S. L. Brunton, and B. Polagye, “Geometric and Control Optimization ofan Array of Two Cross-Flow Turbines,” APS DFD, Atlanta, GA, November 2018.

71. K. Manohar, J. N. Kutz, and S. L. Brunton, “Greedy Sensor and Actuator Placement UsingBalanced Model Reduction,” APS DFD, Atlanta, GA, November 2018.

70. S. L. Brunton.Flow map composition to identify coherent structures.ISFV, Zurich, Switzerland, June 2018.

69. S. L. Brunton.Machine learning of dynamics with applications to flow control and aerodynamic optimization.IUTAM, Santorini, Greece, June 2018.

CV-20

68. M. Meena, Z. Bai, C.-A. Yeh, S. L. Brunton, and K. Taira.High-dimensional turbulent network characterization & modeling.NetSci, Paris, France, June 2018.

67. E. Kaiser, J. N. Kutz, and S. L. Brunton. “Data-driven discovery of Koopman eigenfunctionsfor control,” APS DFD, Denver, CO, November 2017.

66. N. B. Erichson, S. L. Brunton, and J. N. Kutz. “Randomized Dynamic Mode Decomposition,”APS DFD, Denver, CO, November 2017.

65. B. Lusch, S. L. Brunton, and J. N. Kutz. “Data-driven discovery of Koopman eigenfunctionsusing deep learning,” APS DFD, Denver, CO, November 2017.

64. Z. Bai, E. Kaiser, J. L. Proctor, J. N. Kutz, and S. L. Brunton. “Dynamic mode decompositionfor compressive system identification,” APS DFD, Denver, CO, November 2017.

63. K. Manohar, E. Kaiser, B. W. Brunton, J. N. Kutz, and S. L. Brunton. “Data-driven sensorplacement from coherent fluid structures,” APS DFD, Denver, CO, November 2017.

62. B. Strom, S. L. Brunton, and Brian Polagye. “Particle image and acoustic Doppler velocimetryanalysis of a cross-flow turbine wake,” APS DFD, Denver, CO, November 2017.

61. J. M. Kunert-Graf, J. L. Proctor, S. L. Brunton, and J. N. Kutz. “Spatiotemporal Feedbackand Network Structure Drive and Encode C. Elegans Locomotion,” SIAM DS, Snowbird, UT,May 2017. (invited)

60. N. M. Mangan, J. N. Kutz, S. L. Brunton, and J. L. Proctor. “Data-Driven Discovery of Dy-namical System Models for Biological Networks Using Sparse Selection and Information Criteria,”SIAM DS, Snowbird, UT, May 2017. (invited)

59. M. Quade, M. W. Abel, K. Shafi, R. K. Niven, B. R. Noack, and S. L. Brunton. “Prediction ofDynamical Systems by Symbolic Regression,” SIAM DS, Snowbird, UT, May 2017. (invited)

58. K. Manohar, E. Kaiser, S. L. Brunton, and J. N. Kutz. “Sparse Sensor Placement for MultiscalePhenomena,” SIAM DS, Snowbird, UT, May 2017. (invited)

57. E. Kaiser, B. R. Noack, A. Spohn, R. K. Niven, L. N. Cattafesta, M. Morzynski, S. L. Brunton,B. W. Brunton, and J. N. Kutz. “Data-Driven Techniques for Modeling, Control and SensorPlacement,” SIAM DS, Snowbird, UT, May 2017. (invited)

56. K. Manohar, E. Kaiser, S. L. Brunton, and J. N. Kutz. “Sensor placement for multiscalephenomena,” SIAM CSE, Atlanta, GA, March 2017. (invited)

55. J. N. Kutz, S. Sargsyan, K. Manohar, and S. L. Brunton. “Online interpolation point refinementfor reduced order models,” SIAM CSE, Atlanta, GA, March 2017. (invited)

54. A. G. Nair, K. Taira, and S. L. Brunton. “Data-based extraction of modal interaction net-works,” SIAM CSE, Atlanta, GA, March 2017. (invited)

53. S. L. Brunton, J. L. Proctor, and J. N. Kutz. “Sparse identification of nonlinear dynamics(SINDy),” APS DFD, Portland, OR, November 2016.

52. Z. Bai, E. Kaiser, J. L. Proctor, J. N. Kutz, and S. L. Brunton. “Compressed sensing DMD

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with control,” APS DFD, Portland, OR, November 2016.

51. E. Kaiser, B. R. Noack, A. Spohn, L. N. Cattafesta, M. Morzynski, G. Caviller, B. W. Brunton,and S. L. Brunton. “A probabilistic approach to modeling and controlling fluid flows,” APSDFD, Portland, OR, November 2016.

50. A. G. Nair, M. G. Men, K. Taira, and S. L. Brunton. “Vortical and modal network analysis ofunsteady cylinder wake,” APS DFD, Portland, OR, November 2016.

49. K. Manohar, E. Kaiser, S. L. Brunton, and J. N. Kutz. “Sensor placement in multi scalephenomena using multi-resolution dynamic mode decomposition,” APS DFD, Portland, OR,November 2016.

48. B. Strom, S. L. Brunton, and B. Polagye. “Coordinated control of cross-flow turbines,” APSDFD, Portland, OR, November 2016.

47. S. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz. “Data-driven discovery of partialdifferential equations,” APS DFD, Portland, OR, November 2016.

46. K. Taira, A. G. Nair, and S. L. Brunton. “Vortex interaction analysis using complex networkframework,” Annual Meeting of the Japan Society of Fluid Mechanics, Nagoya, Japan, September,2016.

45. K. Taira, A. G. Nair, and S. L. Brunton. “Complex network analysis of unsteady fluid flows,”ICTAM, Montreal, Canada, August, 2016.

44. A. G. Nair, K. Taira, and S. L. Brunton, “Network-Theoretic Analyses of Vortex Dynamics,”SIAM Annual Meeting, Boston, MA, July 2016. (invited)

43. B. Strom, S. L. Brunton, A. Aliseda, and B. Polagye. “Comparison of acoustic Doppler andparticle image velocimetry characterization of a cross-flow turbine wake,” Proceedings of the 4thMarine Energy Technology Symposium, Washington D.C., April, 2016.

42. S. L. Brunton, M. Johnson, X. Fu, and J. N. Kutz. “Self-tuning fiber lasers and other opticalsystems,” SPIE Photonics West, San Francisco, CA, February 2016.

41. B. Strom, A. Aliseda, B. Polagye, and S. L. Brunton, “Unsteady Separated Flow Associatedwith Cross-Flow Turbines,” AIAA Sci-Tech, San Diego, CA, January 2016.

40. K. Taira, A. Nair, and S. L. Brunton. “Network structure of two-dimensional homogeneousturbulence,” APS DFD, Boston, November 2015.

39. B. Strom, S. L. Brunton, and B. Polagye. “Phase resolved angular velocity control of cross flowturbines,” APS DFD, Boston, November 2015.

38. K. Manohar, S. L. Brunton, and J. N. Kutz. “Sparse sensing of aerodynamic loads on insectwings,” APS DFD, Boston, November 2015.

37. Z. Bai, S. L. Brunton, B. W. Brunton, J. N. Kutz, E. Kaiser, A. Spohn, and B. R. Noack. “Flow classification using machine learning on sparsely sampled experimental flow visualizationdata,” APS DFD, Boston, November 2015.

36. B. R. Noack, T. Duriez, V. Parezanovic, V. K. von Krbek, E. Kaiser, L. Cordier, J.-P. Bonnet, M.

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Segond, M. W. Abel, N. Gautier, J.-L. Aider, C. Raibaudo, C. Cuvier, M. Stanislas, A. Debien,N. Mazellier, A. Kourta, S. L. Brunton, and R. K. Niven, “Closed-loop turbulence controlusing machine learning,” Meeting of the GDR 2502 Flow Separation Control and GDR MOSAR,LIMSI, Orsay, France. (Plenary)

35. M. C. Johnson, S. L. Brunton, N. B. Kundtz, and J. N. Kutz. “ An Extremum-Seeking Con-troller for Dynamic Metamaterial Antenna Operation,” IEEE APWC, Torino Italy, September2015.

34. B. Strom, S. L. Brunton, and B. Polagye. “Consequences of preset pitch angle for cross flowturbines,” 11th European Wave and Tidal Energy Conference, Nantes, France, September 5-11,2015.

33. B. Strom, S. L. Brunton, and B. Polagye. “Hydrodynamic optimization of cross-flow turbineswith large chord to radius ratios,” Proceedings of the 3th Marine Energy Technology Symposium,Washington D.C., April, 2015.

32. A. L. Eberle, S. L. Brunton, F. E. Fish, and T. L. Daniel, “Unsteady forces form in flappingfoils and depend on fluid-solid coupling in water but not in air,” SICB, West Palm Beach, FL,January 2015.

31. B. Polagye, B. Strom, C. Haegele, S. Mehta, C. Bowman, and S. L. Brunton. “Parametricexperimentation with cross-flow turbines,” AGU Fall Meeting, San Francisco, December 2014.

30. S. Madhavan, S. L. Brunton, and J. J. Riley. “Lagrangian coherent structures and the dynamicsof inertial particles,” APS DFD, San Francisco, November 2014.

29. V. Parezanovic, L. Cordier, B. R. Noack, A. Spohn, J.-P. Bonnet, T. Duriez, M. Segond, M. W.Abel, and S. L. Brunton. “Closed-loop control of an experimental mixing layer using MLC,”APS DFD, San Francisco, November 2014.

28. T. Duriez, V. Parezanovic, K. von Krbek, L. Cordier, B. R. Noack, J.-P. Bonnet, M. Segond, M.W. Abel, N. Gautier, J.-L. Aider, C. Raibaudo, C. Cuvier, M. Stanislas, A. Debien, N. Mazellier,A. Kourta, and S. L. Brunton. “Closed-loop control of experimental shear flows using MLC,”APS DFD, San Francisco, Nov. 2014.

27. B. R. Noack, L. Cordier, V. Parezanovic, K. von Krbek, M. Segond, M. W. Abel, S. L. Brunton,and T. Duriez. “Machine learning control (MLC) – a novel method for optimal control of complexnonlinear systems,” APS DFD, San Francisco, November 2014.

26. M. C. Johnson, S. L. Brunton, J. N. Kutz, and N. B. Kundtz. “Sidelobe canceling on areconfigurable holographic metamaterial antenna,” IEEE APWC, Aruba, August 2014.

25. J. N. Kutz, X. Fu, and S. L. Brunton. “Self-tuning fiber lasers: machine learning applied tooptical systems,” Nonlinear Photonics, Barcelona, Spain, July 2014.

24. B. R. Noack, L. Cordier, T. Duriez, V. Parezanovic, J. Delville, J.-P. Bonnet, M. Segond, M.Abel, M. Morzynski, and S. L. Brunton, “Closed-loop turbulence control using reduced-ordermodeling and machine learning,” Computational Science & Engineering (CompSE) Workshop,Aachen, Germany, 2014. (Keynote)

23. M. Segond, M. Abel, V. Parezanovic, T. Duriez, B. R. Noack, L. Cordier, J.-P. Bonnet, M.

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Morzynski, and S. L. Brunton, “Genetic programming for control of dynamical systems –a new generic framework,” 85th Annual Meeting of the International Association of AppliedMathematics and Mechanics, Erlangen Nurnberg, Germany, 2014.

22. M. Abel, M. Segond, T. Duriez, L. Cordier, V. Parezanovic, B. R. Noack, J.-P. Bonnet, M.Morzynski, and S. L. Brunton, “Turbulence control by machine learning,” 85th Annual Meetingof the International Association of Applied Mathematics and Mechanics, Erlangen Nurnberg,Germany, 2014.

21. B. R. Noack, T. Duriez, V. Parezanovic, J.-C. Laurentie, M. Schlegel, E. Kaiser, L. Cordier, A.Spohn, J.-P. Bonnet, M. Morzynski, M. Segond, M. W. Abel, and S. L. Brunton,, “Closed-Loop Turbulence Control - A Systematic Strategy for the Nonlinearities,” SIAM Conference onUncertainty Quantification, April 2014. (invited)

20. J. N. Kutz and S. L. Brunton, “Sparsity, Sensitivity and Encoding/decoding of NonlinearDynamics using Machine Learning Methods,” SIAM Conference on Uncertainty Quantification,April 2014. (invited)

19. S. L. Brunton, J. H. Tu, and J. N. Kutz, “Nonlinear dynamic estimation with sparse sensors,”APS Division of Fluid Dynamics, November 2013.

18. J. H. Tu, D. M. Luchtenburg, C. W. Rowley, S. L. Brunton, and J. N. Kutz, “Novel samplingstrategies for dynamic mode decomposition,” APS Division of Fluid Dynamics, November 2013.

17. S. T. M. Dawson, S. L. Brunton, and C. W. Rowley, “Nonlinear switched models for control ofunsteady forces on a rapidly pitching airfoil,” APS Division of Fluid Dynamics, November 2013.

16. B. R. Noack, T. Duriez, L. Cordier, M. Segond, M. Abel, S. L. Brunton, M. Morzynsky, J.-C. Laurentie, V. Parezanovic, and J.-P. Bonnet, “Closed-loop turbulence control with machinelearning methods” APS Division of Fluid Dynamics, November 2013.

15. C. W. Rowley, S. L. Brunton, D. M. Luchtenburg, and M. O. Williams, “Coherent structureidentification using flow map composition and spectral interpolation,” BIRS: Uncovering Trans-port Barriers in Geophysical Flows, September 2013.

14. D. M. Luchtenburg, S. L. Brunton, and C. W. Rowley, “Uncertainty propagation using spectralmethods and flow map composition,” APS Division of Fluid Dynamics, November 2012.

13. S. T. M. Dawson, S. L. Brunton, and C. W. Rowley, “Feedback control of a pitching andplunging airfoil via direct numerical simulation,” APS Division of Fluid Dynamics, November2012.

12. S. L. Brunton and C. W. Rowley, “Unsteady aerodynamic models for separated flows past aflat plate at Re=100,” APS Division of Fluid Dynamics, November 2011.

11. S. L. Brunton, C. W. Rowley, and D. R. Williams, “Linear unsteady aerodynamic models fromwind tunnel measurements,” 41st AIAA Fluid Dynamics Conference and Exhibit, June 2011.

10. S. L. Brunton, and C. W. Rowley. “Low-dimensional state-space representations for classicalunsteady aerodynamic models,” 49th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2011.

9. S. L. Brunton and C. W. Rowley, “State-space representation of unsteady aerodynamic models,”APS Division of Fluid Dynamics, November 2010.

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8. S. L. Brunton, and C. W. Rowley, “Unsteady aerodynamic models for agile flight at lowReynolds numbers,” 48th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2010.

7. S. L. Brunton and C. W. Rowley, “Fast computation of Lagrangian coherent structures: algo-rithms and error analysis,” APS Division of Fluid Dynamics, November 2009.

6. S. L. Brunton, C. W. Rowley, S. R. Kulkarni, and C. Clarkson, “Maximum power point track-ing for photovoltaic optimization using extremum seeking,” 34th IEEE Photovoltaic SpecialistConference, June 2009.

5. S. L. Brunton and C. W. Rowley, “Understanding bio-flight,” Thousand Islands Meeting, April2009.

4. S. L. Brunton, and C. W. Rowley, “Modeling the unsteady aerodynamic forces on small-scalewings,” 47th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2009.

3. S. L. Brunton and C. W. Rowley, “A model for fast computation of FTLE fields,” APS Divisionof Fluid Dynamics, November 2008.

2. S. L. Brunton and C. W. Rowley, “Modeling unsteady aerodynamic forces on small-scale wings,”Thousand Islands Meeting, April 2008.

1. S. L. Brunton, C. W. Rowley, K. Taira, T. Colonius, J. Collins, and D. R. Williams, “Unsteadyaerodynamic forces on small-scale wings: Experiments, simulations & models,” 46th AIAA ASM,Jan. 2008.

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Contributed Posters and Movies

8. S. L. Brunton, J. N. Kutz, X. Fu, and M. C. Johnson, “Data-driven control of complex optical systems,”Nonlinear Optics, Hawaii, August, 2015.

7. T. Duriez, K. von Krbek, L. Cordier, B. R. Noack, J.-P. Bonnet, M. Segond, M. W. Abel, N. Gautier, J.-L.Aider, C. Raibaudo, C. Cuvier, M. Stanislas, and S. L. Brunton, “Controlling turbulent flows in closed-loopusing Machine Learning Control,” XIII Reunin sobre Recientes Avances en Fsica de Fluidos y sus Aplicaciones,Tandl, Argentina, 2014.

6. B. W. Brunton, A. L. Eberle, B. H. Dickerson, S. L. Brunton, J. N. Kutz, and T. L. Daniel, “Sensorplacement for sparse sensory decision making,” COSYNE, Salt Lake City, February 2014.

5. S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Compressive sensing and dynamic mode decomposition(DMD),” Moore-Sloan Data Science Poster and Networking Session, UW, February 2014.

4. S. L. Brunton, B. W. Brunton, A. Eberle, and J. N. Kutz, “Sparse sensing in mechanosensory systems,”BMES Annual Meeting, September 2013.

3. B. W. Brunton, S. L. Brunton, J. L. Proctor, and J. N. Kutz, “An adaptive sparse sampling approach tosensory decision making,” BMES Annual Meeting, September 2013.

2. S. L. Brunton, and C. W. Rowley, “Stirring faces: mixing in a quiescent fluid,” Gallery of Fluid Motion,APS Division of Fluid Dynamics, November 2012. arXiv:1210.3747 [physics.flu-dyn]

1. S. L. Brunton, and C. W. Rowley, “A Comparison of Methods for Fast Computation of FTLE Fields,”SIAM Conference on Applications of Dynamical Systems, May 2009.

Patents Granted

2. J. N. Kutz, J. Grosek, S. L. Brunton, X. Fu, and S. Pendergrass “Using dynamic mode decomposition forreal-time background/foreground separation in video,”US Patent Number 9674406, June 6, 2017.

1. J. N. Kutz, S. L. Brunton, X. Fu, “Tuning multi-input complex dynamic systems using sparse representationsof performance and extremum-seeking control,”US Patent Number 9972962, May 15, 2018.

Software

9. sindy-mpc, Developed by Eurika Kaiser. [https://github.com/eurika-kaiser/SINDY-MPC/]

8. kronic, Developed by Eurika Kaiser. [https://github.com/eurika-kaiser/KRONIC/]

7. deepkoopman, Developed by Bethany Lusch. [https://github.com/BethanyL/DeepKoopman/]

6. pde-find, Developed by Sam Rudy. [https://github.com/snagcliffs/PDE-FIND/]

5. sspor, Developed by Krithika Manohar. [https://github.com/kmanohar/SSPOR pub/]

4. rsvd, Developed by Ben Erichson. [github.com/erichson/rSVD/]

3. eigenfish, Developed by Seth Pendergrass, funded by DOE. [github.com/sethdp/eigenfish/]

2. libssvd, Developed by Seth Pendergrass. [github.com/sethdp/libssvd/]

1. python-control, led by Richard Murray. [sourceforge.net/projects/python-control/]

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Service

Professional societies: SIAM, APS, AIAA (lifetime member), IEEE

Associate Editor for ACC SIAM Section. [2015-2016]

Special Issue Editor for TCFD. [2018–present]

Review papers (>100): Proc. Nat. Acad. Sci.; Proc. Roy. Soc. Lon. A; J. Fluid. Mech.; J.Comp. Phys.; AIAA J.; J. Aircraft ; IEEE TPEL; IEEE TVCF ; IEEE TIE ; Chaos; Energies; AJSE ;J. Nonlin. Sci.; Sys. Cont. Lett.; Science Advances; Int. J. Rob., Non. Cont.; Phys. Rev. E.; Theor.Comp. Fluid Dyn.

Advisory committee for data analysis of underwater video data at PNNL. [2016]

Review committees: Mary Gates Research Scholarship Application Review [Fall, 2013]

UW committees: ME Qualifying exam reform committee (chair), ME Faculty hiring committee,ME S&D committee, eScience education curriculum committee (co-chair).

Event manager: New Jersey Science Olympiad [2009, 2010]

Thesis committees: Mikala Johnson, Xing Fu, Bethany Lusch, Susie Sargsyan, Yian Ma, DonsubRim, Trevor Harrison, Wei Guo, Emma Cotter, Siavash Alemzadeh, Alexander Hoang, Rose Hendrix,Tony Piaskowy, Behnoosh Parsa.

Session chair at conferences: APS DFD (2013, 2014), SIAM CSE (2013), SIAM DS (2013), SIAMCSE (2015), SIAM DS (2015), SIAM CT (2015), SIAM UQ (2016), APS DFD (2016), SIAM CSE(2017).

UW Hyperloop Team: Faculty advisor.

UW Math Academy: Participate in demonstrations of real-world uses of mathematics to high-school students from under-represented groups.

UW State Academic Red Shirt (STARS): Faculty mentor.

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Organized Seminars, Symposia, Workshops, and Summer Schools

15. IPAM Long WorkshopLearning physics and the physics of learningw/ Cecilia Clementi, Frank Noe, Marina Meila, Francesco Paesani, and Yann Lecun3-month long program of workshops funded by NSF [Upcoming, Winter 2019]

14. Physics Informed Machine Learning Workshopw/ J. Nathan Kutz [2019]

13. SIAM CSE19 MinisymposiumData methods for complex systemsw/ J. Nathan Kutz and Sam Rudy [2019]

12. Boeing Executive Data Science WorkshopBoeing Defense and Space, Washington DC, May 2018

11. Sparse and Compressive Sensing,w/ Bing Brunton2-hour workshop as part of State of the Art Review (SOAR), Seattle Washington, May 2018

10. Boeing Executive Data Science WorkshopBoeing Commercial Aircraft, Seattle WA, January 2018

9. Rome Workshop and Summer Schoolw/ J. Nathan Kutz, Claudio Conti, Eugenio Del Re, Silvia Gentilini, and Giulia Marcucci [2017]

8. SIAM CSE17 MinisymposiumData methods for complex systemsw/ Joshua Proctor And J. Nathan Kutz [2017]

7. SIAM Uncertainty Quantification 2016 MinisymposiumData-driven dynamical systemsw/ Nathan Kutz [2016]

6. SIAM Control and Applications 2015 MinisymposiumMachine learning methods to control complex systemsw/ Bernd Noack [2015]

5. SIAM DS15 MinisymposiumExtensions and applications of dynamic mode decompositionw/ Jonathan Tu [2015]

4. SIAM CSE15 MinisymposiumData methods for complex systemsw/ Joshua Proctor And J. Nathan Kutz [2015]

3. SIAM CSE13 MinisymposiumData-driven model reductionw/ Joshua Proctor And J. Nathan Kutz [2013]

2. Boeing Distinguished Lectures in Applied Mathematics, University of WashingtonHost seminar speakers and organize visit.w/ Joel Zylberberg. Speakers selected and invited by J. Nathan Kutz. [Fall 2012-present]

1. Princeton Dynamical Systems and Applied Mathematical Modeling Lunchtime SeminarInterdisciplinary seminar for graduate students; meets weekly with biweekly lecturesw/ Joshua Proctor [Fall 2008, 2009]

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Video Outreach on YouTube

• ∼ 15, 000 Subscribers and ∼ 1, 000, 000 views

• Over 100 hours of educational content, including four courses, two bootcamps, and researchabstracts

Mathematical Art

2. S. L. Brunton, “Mathematical Mountains,”Princeton Art of Science Gallery, May 2011.http://crispme.com/art-of-science-2011/http://butdoesitfloat.com/Mathematical-Mountains, 2011

1. S. L. Brunton and C. W. Rowley, “Stirring Faces,”Princeton Art of Science Gallery, May 2010.http://phys.org/news193333630.html, May 2010.Time Photo Essays, “Seeking Art in Science,” June 2010.http://arxiv.org/abs/1210.3747APS DFD Gallery of Fluid Motion, 2012.(http://www.youtube.com/watch?v=l3rtloOyh3I)Princeton Alumni Weekly, “Science as art,” April 2013.

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