Bingni W. Brunton

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J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer EngineeringVerified email at uw.edu
Steven L. BruntonProfessor, University of WashingtonVerified email at uw.edu
Joshua L. ProctorPrincipal Research Scientist, Bill and Melinda Gates FoundationVerified email at idmod.org
Carlos BrodyPrinceton UniversityVerified email at princeton.edu
Rajesh P. N. RaoComputer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Jeffrey C. ErlichSainsbury Wellcome Centre, UCLVerified email at ucl.ac.uk
Eurika KaiserUniversity of WashingtonVerified email at uw.edu
Jeffrey OjemannProfessor of Neurological SurgeryVerified email at uw.edu
John TuthillUniversity of WashingtonVerified email at uw.edu
Lili KarashchukAllen Institute for Neural DynamicsVerified email at alleninstitute.org
Tim HanksUniversity of California-DavisVerified email at ucdavis.edu
Chunyu A DuanGroup Leader at Sainsbury Wellcome Centre, UCLVerified email at ucl.ac.uk
Steven M. PetersonContract research analyst, Meta Reality LabsVerified email at meta.com
Urban FaselImperial College LondonVerified email at imperial.ac.uk
Floris van BreugelAssistant Professor, University of Nevada, RenoVerified email at unr.edu
Krithika ManoharAssistant Professor, University of WashingtonVerified email at uw.edu
Satpreet H. SinghUniversity of WashingtonVerified email at uw.edu
Kathryn E StanchakUniversity of WashingtonVerified email at uw.edu
Matthew BotvinickGoogle DeepMind, Yale Law School, University College LondonVerified email at google.com
Nancy Xin Ru WangApplied Scientist at AmazonVerified email at amazon.de

Bingni W. Brunton

Professor, Richard and Joan Komen University Chair, University of Washington, Seattle

Verified email at uw.edu - Homepage

Computational neuroscience Neuromechanics Machine Learning Dynamical Systems


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Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems JN Kutz, SL Brunton, BW Brunton, JL Proctor Society for Industrial and Applied Mathematics, 2016	1599	2016
Rats and Humans Can Optimally Accumulate Evidence for Decision-Making BW Brunton, MM Botvinick, CD Brody Science 340 (6128), 95-98, 2013	650	2013
Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control SL Brunton, BW Brunton, JL Proctor, JN Kutz PLOS ONE 11 (2), e0150171, 2016	545	2016
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature Communications 8, 2017	531	2017
Distinct relationships of parietal and prefrontal cortices to evidence accumulation TD Hanks, CD Kopec, BW Brunton, CA Duan, JC Erlich, CD Brody Nature 520 (7546), 220-223, 2015	506	2015
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition BW Brunton, LA Johnson, JG Ojemann, JN Kutz Journal of neuroscience methods 258, 1-15, 2016	469	2016
Cell shape and cell-wall organization in Gram-negative bacteria KC Huang, R Mukhopadhyay, B Wen, Z Gitai, NS Wingreen Proceedings of the National Academy of Sciences 105 (49), 19282-19287, 2008	391	2008
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns K Manohar, BW Brunton, JN Kutz, SL Brunton IEEE Control Systems 38 (3), 63-86, 2018	377	2018
Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat JC Erlich, BW Brunton, CA Duan, TD Hanks, CD Brody Elife 4, e05457, 2015	233	2015
Anipose: a toolkit for robust markerless 3D pose estimation P Karashchuk, KL Rupp, ES Dickinson, S Walling-Bell, E Sanders, E Azim, ... Cell Reports 36 (13), 109730, 2021	172	2021
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control U Fasel, JN Kutz, BW Brunton, SL Brunton Proceedings of the Royal Society A 478 (2260), 20210904, 2022	163	2022
Cortical and Subcortical Contributions to Short-Term Memory for Orienting Movements CD Kopec, JC Erlich, BW Brunton, K Deisseroth, CD Brody Neuron 88 (2), 367-377, 2015	117	2015
Sparse Sensor Placement Optimization for Classification BW Brunton, SL Brunton, JL Proctor, JN Kutz SIAM Journal on Applied Mathematics 76 (5), 2099-2122, 2016	116*	2016
Exploiting sparsity and equation-free architectures in complex systems JL Proctor, SL Brunton, BW Brunton, JN Kutz The European Physical Journal Special Topics 223 (13), 2665-2684, 2014	79	2014
Numerical differentiation of noisy data: A unifying multi-objective optimization framework F van Breugel, JN Kutz, BW Brunton IEEE Access 8, 196865 - 196877, 2020	70	2020
Learning dominant physical processes with data-driven balance models JL Callaham, JV Koch, BW Brunton, JN Kutz, SL Brunton Nature Communications 12 (1), 1-10, 2021	67	2021
Centering data improves the dynamic mode decomposition SM Hirsh, KD Harris, JN Kutz, BW Brunton SIAM Journal on Applied Dynamical Systems 19 (3), 1920-1955, 2020	57	2020
Data-driven methods in fluid dynamics: Sparse classification from experimental data Z Bai, SL Brunton, BW Brunton, JN Kutz, E Kaiser, A Spohn, BR Noack Whither Turbulence and Big Data in the 21st Century?, 323-342, 2017	54	2017
Data-Driven Methods in Fluid Dynamics: Sparse Classification from Experimental Data Z Bai, SL Brunton, BW Brunton, JN Kutz, E Kaiser, A Spohn, BR Noack Whither Turbulence and Big Data in the 21st Century?, 323-342, 2017	54	2017
Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data TL Mohren, TL Daniel, SL Brunton, BW Brunton Proceedings of the National Academy of Science 115 (42), 10564--10569, 2018	53	2018

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