University of Texas, 2014
Ph.D. in Neuroscience
Doctoral Advisor: Richard W. Aldrich
Santa Fe Insitute, 2012
Complex Systems Summer School
Washington and Lee University, 2009
B.S. in Physics
magna cum laude
Traditional statistical methods: Multiple regression, logistic regression, survival analysis, etc.
Computational Statistics/Machine Learning methods: Bootstrap, Bayesian inference, Markov chain Monte Carlo, LASSO/LARS shrinkage, Nonparametric Bayes, time series analysis, Hierarchical Dirichlet Process Hidden Markov Models, clustering/segmentation, Dirichlet process mixture models, Neural Networks, genetic algorithms, principal component analysis, deep learning, ConvNets, Network Theory, Natural Language Processing, Topic Modeling, Latent Dirichlet Allocation.
Technical: R, Python, Spark, Scala, HTML, javascript (d3), AWS, git. I also love LaTeX.
Best Abstract Award, Austin Conference on Learning and Memory, 2013
Student Research Achievement Award Finalist, Biophysical Society, 2012
Complex Systems Summer School, Santa Fe Institute for Complex Systems, 2012
Graduate Dean's Prestigious Fellowship Supplement, University of Texas at Austin, 2012
Predoctoral Fellowship, American Heart Association, 2012 - 2014
Dean's Excellence Award, University of Texas at Austin, 2009
Edward O. Levy Fellowship, Washington and Lee University, 2008
APS Student Leadership Scholarship, American Physics Society, 2008
Walter Leconte Stevens Award, Washington and Lee University Physics Department, 2008
NSF REU Research Scholarship, National Science Foundation, 2007
R. E. Lee Research Fellowship, Washington and Lee University, 2006
Hines, K., A Primer on Bayesian Inference for Biophysical Systems. Biophysical Journal, in press.