Scott C. Schmidler

This paper introduces a Bayesian variable selection method for identifying selection in phylogenetic datasets, showing it outperforms Dirichlet-process models and avoids phantom selection artifacts.

The paper develops a computational model to estimate the inducibility of specific antibodies during vaccine design by simulating context-dependent mutation rates in B cell lineages.

The paper introduces an algorithm to efficiently approximate helix-coil ensembles by enumerating the most probable configurations, enabling analysis of ensemble properties and folding mechanisms.

This paper introduces a Bayesian non-parametric model to analyze microbial growth data, accounting for random effects and improving accuracy in estimating biological effects.