About

Bing W. Brunton is a Professor and Richard & Joan Komen University Chair in the Department of Biology at the University of Washington. She joined the faculty in 2014 as part of the Provost Initiative in Data-Intensive Discovery to develop an interdisciplinary research program at the intersection of biology and data science. Her training spans biology, biophysics, molecular biology, neuroscience, and applied mathematics, with degrees including a B.S. in Biology from Caltech and a Ph.D. in Neuroscience from Princeton. Her research focuses on understanding the complex network of neurons in the brain and how network activity relates to behavior. She is interested in system-level questions in neuroscience, such as describing the multi-scale connective topology of brain areas and differentiating neuronal network functions before and after learning. To address these questions, she leverages mathematical advances in dimensionality reduction and compressive sensing. Her group develops data-driven analytic methods inspired by neuroscience questions, aiming to find interpretable patterns in large-scale, complex neural and behavioral data.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Neuroscience
• Biology
• Anatomy
• Computer vision
• Algorithm

Selected publications

• Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

  Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2022 · 268 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identification of the nonlinear dynamics (SINDy) algorithm. First, an ensemble of SINDy models is identified from subsets of limited and noisy data. The aggregate model statistics are then used to produce inclusion probabilities of the candidate functions, which enables uncertainty quantification and probabilistic forecasts. We apply this ensemble-SINDy (E-SINDy) algorithm to several synthetic and real-world datasets and demonstrate substantial improvements to the accuracy and robustness of model discovery from extremely noisy and limited data. For example, E-SINDy uncovers partial differential equations models from data with more than twice as much measurement noise as has been previously reported. Similarly, E-SINDy learns the Lotka Volterra dynamics from remarkably limited data of yearly lynx and hare pelts collected from 1900 to 1920. E-SINDy is computationally efficient, with similar scaling as standard SINDy. Finally, we show that ensemble statistics from E-SINDy can be exploited for active learning and improved model predictive control.

  DOI

• Anipose: A toolkit for robust markerless 3D pose estimation

  Cell Reports · 2021 · 293 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.

  DOI

• Numerical Differentiation of Noisy Data: A Unifying Multi-Objective Optimization Framework

  IEEE Access · 2020 · 116 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science

  Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control. Unfortunately, the mathematical formulation of numerical differentiation is typically ill-posed, and researchers often resort to an ad hoc process for choosing one of many computational methods and its parameters. In this work, we take a principled approach and propose a multi-objective optimization framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. Our framework has three significant advantages. First, the task of selecting multiple parameters is reduced to choosing a single hyper-parameter. Second, where ground-truth data is unknown, we provide a heuristic for selecting this hyper-parameter based on the power spectrum and temporal resolution of the data. Third, the optimal value of the hyper-parameter is consistent across different differentiation methods, thus our approach unifies vastly different numerical differentiation methods and facilitates unbiased comparison of their results. Finally, we provide an extensive open-source Python library pynumdiff to facilitate easy application to diverse datasets (https://github.com/florisvb/PyNumDiff).

  DOI

• The Balance Hypothesis for the Avian Lumbosacral Organ and an Exploration of Its Morphological Variation

  Integrative Organismal Biology · 2020 · 24 citations

  Senior authorCorresponding
  • Biology
  • Anatomy
  • Neuroscience

  Birds (Aves) exhibit exceptional and diverse locomotor behaviors, including the exquisite ability to balance on two feet. How birds so precisely control their movements may be partly explained by a set of intriguing modifications in their lower spine. These modifications are collectively known as the lumbosacral organ (LSO) and are found in the fused lumbosacral vertebrae called the synsacrum. They include a set of transverse canal-like recesses in the synsacrum that align with lateral lobes of the spinal cord, as well as a dorsal groove in the spinal cord that houses an egg-shaped glycogen body. Based on compelling but primarily observational data, the most recent functional hypotheses for the LSO consider it to be a secondary balance organ, in which the transverse canals are analogous to the semicircular canals of the inner ear. If correct, this hypothesis would reshape our understanding of avian locomotion, yet the LSO has been largely overlooked in the recent literature. Here, we review the current evidence for this hypothesis and then explore a possible relationship between the LSO and balance-intensive locomotor ecologies. Our comparative morphological dataset consists of micro-computed tomography (μ-CT) scans of synsacra from ecologically diverse species. We find that birds that perch tend to have more prominent transverse canals, suggesting that the LSO is useful for balance-intensive behaviors. We then identify the crucial outstanding questions about LSO structure and function. The LSO may be a key innovation that allows independent but coordinated motion of the head and the body, and a full understanding of its function and evolution will require multiple interdisciplinary research efforts.

  PublisherDOI

Recent grants

• NCS-FO: Understanding Neural Processing in Long-Term, Naturalistic Human Brain Recordings Using Data-Intensive Approaches

  NSF · $899k · 2016–2021

• CRCNS: Collaborative Research: Dynamic Models of Human Auditory Perceptual Switching Informed by Large-Scale ECoG Recordings

  NSF · $146k · 2015–2019

Frequent coauthors

• Steven L. Brunton

  Dynamic Systems (United States)

  57 shared

• Rajesh P. N. Rao

  43 shared

• J. Nathan Kutz

  42 shared

• Joshua L. Proctor

  Seattle University

  36 shared

• Satpreet H. Singh

  25 shared

• Steven Peterson

  University of Michigan–Ann Arbor

  25 shared

• Jeffrey C. Erlich

  East China Normal University

  17 shared

• Carlos D. Brody

  Howard Hughes Medical Institute

  15 shared

Education

• B.S., Biology

  California Institute of Technology

  2006

• Ph.D., Neuroscience

  Princeton University

  2012

Awards & honors

• Alfred P. Sloan Research Fellowship in Neuroscience (2016)
• UW Innovation Award (2017)
• AFOSR Young Investigator Program award (2018)

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