Ph.D., Cornell University (2003)
- Mathematical modeling and inference in complex biological systems
- Data-driven inverse problems in neuroscience, systems biology, and physics
- Statistical mechanical tools for dynamical models with many parameters (“Sloppy Models”: obtaining robust predictions even with poorly constrained parameters)
- Bayesian and nonparametric statistical methods for inference of underlying network structure from data
Reviewer: Neuroimage, BMC Systems Biology, J. Royal Soc. Interface Focus, Human Brain Mapping
Guest Assistant Editor: PLoS Computational Biology
Member: Cognitive Neuroscience Society, Organization for Human Brain Mapping
|2011-2012||Assistant Project Scientist, Department of Physics and Institute for Collaborative Biotechnologies, University of California, Santa Barbara|
|2007-2011||Postdoctoral Researcher, Department of Physics and Institute for Collaborative Biotechnologies, University of California, Santa Barbara|
|2004-2007||Helen Hay Whitney Foundation Postdoctoral Fellow, Department of Molecular and Cellular Biology, Harvard University|
Awards & Honors
|2004||Helen Hay Whitney Foundation Fellow, Harvard University|
|2001||National Institutes of Health Trainee in Molecular Biophysics, Cornell University|
|1998||National Science Foundation Graduate Research Fellow, Cornell University|
Insulation for daydreams: a role for tonic norepinephrine in the facilitation of internally guided thought, J. Smallwood, K. S. Brown, B. Baird, M. Mrazek, M. Franklin, and J. W. Schooler, PLoS ONE 7(4), e33706 (2012)
Learning, memory, and the role of neural network architecture, A. M. Hermunstad, K. S. Brown, D. S. Bassett, J. M. Carlson, PLoS Computational Biology 7(6), e1002063 (2011).
Pupillometric evidence for decoupling of attention from perceptual input during ofﬂine thought, J. Smallwood*, K. S. Brown*, C. Tipper, B. Giesbrecht, M. S. Franklin, M. D. Mrazek, J. M. Carlson, and J. W. Schooler (*equal authorship), PLoS ONE 6(3), e18298 (2011).
Validation of coevolving residue algorithms via pipeline sensitivity analysis: ELSC and OMES and ZNMI, oh my!, C. A. Brown and K. S. Brown, PLoS ONE 5, e10779 (2010).
Improving human brain mapping via joint inversion of brain electrodynamics and the BOLD signal, K. S. Brown, S. Ortigue, S. T. Grafton, and J. M. Carlson, Neuroimage 49, 2401-2415 (2010).
Universally sloppy parameter sensitivities in systems biology, R. N. Gutenskunst, J. J. Waterfall, F. P. Casey, K. S. Brown, C. R. Myers, J. P. Sethna, PLoS Computational Biology 3, e189 (2007).