- Ph.D. in Molecular Biology and Neuroscience, Princeton University, 2012.
- B.S. in Biology, California Institute of Technology (Caltech), 2006.
I am an Assistant Professor in the Biology department at UW.
I am supported in part by the Washington Research Foundation
(WRF) through the new UW Institute of Neuroengineering (UWIN).
I am also a Data Science Fellow of the UW eScience Institute and a faculty member of the Graduate Program in Neuroscience.
I consider myself primarily a computational neuroscientist working at the intersection of big data,
dynamical systems, and neuroscience.
I am also broadly interested in methods of visualizing, manipulating,
and understanding complex, dynamic data, particularly from biologically relevant systems.
We are actively recruiting talented new graduate students and postdocs to join the group!
If you are excited about doing research in big data, applied mathematics, machine learning, and neuroscience, email Bing (please include a copy of your CV).
Recent Lab News
- Data-driven dynamic models of large-scale neural data
- Efficient algorithms for closed-loop neuroengineering
- Sparse sensors for biological and engineered systems
- Neural mechanisms of decision-making
- B. W. Brunton, S. L. Brunton, J. L. Proctor, J. N. Kutz (2016),
Sparse sensor placement optimization for classification;
SIAM Journal on Applied Mathematics 76(5), 2099--2122;
open access article and
- Z. Bai, S. L. Brunton, B. W. Brunton, J. N. Kutz, E. Kaiser, A. Spohn, B. R. Noack (2017),
Data-driven methods in fluid dynamics: Sparse classification from experimental data;
chapter in: Whither Turbulence and Big Data in the 21st Century?, pp 323--342.
- S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, J. N. Kutz (2016),
Chaos as an intermittently forced linear system;
Submitted; arXiv preprint.
- N. X. R. Wang, J. D. Olson, J. G. Ojemann, R. P. N. Rao, B. W. Brunton (2016),
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations;
Frontiers in Human Neuroscience; doi: 10.3389/fnhum.2016.00165.
- S. L. Brunton, B. W. Brunton, J. L. Proctor, J. N. Kutz (2016),
Koopman observable subspaces and finite linear representations of nonlinear dynamical systems for control;
- B. W. Brunton, J. A. Johnson, J. G. Ojemann, J. N. Kutz (2016),
Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition.
J Neuroscience Methods 258, 1--15;
earlier arXiv version.
- C. D. Kopec, J. C. Erlich, B. W. Brunton, K. Deisseroth, C. D. Brody (2015),
Cortical and subcortical contributions to short-term memory for orienting movements.
Neuron 88 (2), 367-377.
- T. D. Hanks, C. D. Kopec, B. W. Brunton, C. A. Duan, J. C. Erlich, C. D. Brody (2015),
Distinct relationships of parietal and prefrontal cortices to evidence accumulation.
Nature 520, 220-223.
- J. C. Erlich, B. W. Brunton, C. A. Duan, T. D. Hanks, C. D. Brody (2014),
Distinct behavioral effects of prefrontal and pareital cortex inactivations on an accumulation of evidence task in the rat.
- J. L. Proctor, S. L. Brunton, B. W. Brunton, J. N. Kutz (2014),
Exploiting sparsity and equation-free architecture in complex systems.
European Physical Journal Special Topics 223, 2665-2684.
- B. W. Brunton, S. L. Brunton, J. L. Proctor, J. N. Kutz (2013),
Optimal sensor placement and enhanced sparsity for classification;
In Review; arXiv preprint.
- B. W. Brunton, M. M. Botvinick, C. D. Brody (2013),
Rats and humans can optimally accumulate evidence for decision-making.
Science 340 (6128), 95-98.
- J. Kubanek, L. H. Snyder, B. W. Brunton, C. D. Brody, G. Schalk (2013),
A low-frequeny oscillatory neural signal in humans encodes a developing decision variable.
NeuroImage 83, 795-808.
- A. E. Granstedt, B. W. Brunton, L. W. Enquist (2013),
Imaging the transport dynamics of single alphaherpesvirus particles in intact peripheral nervous system explants from infected mice.
- K. C. Huang, R. Mukhopadhyay, B. Wen, Z. Gitai, N. S. Wingreen (2008) ,
Cell shape and cell-wall organization in Gram-negative bacteria.
Proceedings of the National Academy of Sciences 105(49), 19282-19287.
To give you a more multimedia idea of what the lab is up to, here are some videos:
- Biology 419/519, Data Science for Biologists (Winter 2017)
- Biology 300 (with Bill Moody), Intro to Neuroscience (Spring 2016)
- MOOC: Data Science for Biologists, YouTube channel (with Steve Brunton and Nathan Kutz)
- Biology 419/519, Data Science for Biologists (Winter 2016)
- Biology 419/519, Data Science for Biologists (Spring 2015)
My husband Steve Brunton and I met when we
were undergrads and have migrated together ever since. We live in Seattle with our two kids and our dog Mordecai.