My research focus in graduate school has been in modeling the biophysics of protein signalling and conformational change. I've been particularly interested in approaching basic questions of protein biophysics with methods developed in the statistics and machine learning communities.
I am currently working on applying nonparametric Bayesian methods to the analysis of single molecule time series. Below are some finished projects.
Analyzing Single-Molecule Time Series via Nonparameteric Bayesian Inference
Keegan Hines, John Bankston, Rick Aldrich
Biophysical Journal, February 2015
Bayesian Approaches for Modeling Protein Biophysics
My disseration, a real page turner.
Determination of parameter identifiability in nonlinear biophysical models: A Bayesian Approach
Keegan Hines, Tom Middendorf, Rick Aldrich
Journal of General Physiology, March 2014
Inferring subunit stoichiometry from single molecule photobleaching
Journal of General Physiology, June 2013