My primary approach to developing phylogenetic tools is to apply new statistical models as priors using Bayesian inference methods. The implementation of complex models in a Bayesian framework provides a flexible way to model evolutionary processes and obtain reliable estimates of biological parameters. When coupled with numerical methods––such as Markov chain Monte Carlo (MCMC)––for approximating the posterior probability distribution of parameters, Bayesian inference methods can be extremely powerful and have revolutionized the range of evolutionary questions that can be tackled.

Statistical phylogenetic inference is heavily computational, thus the software I develop is written in C++ (a compiled language). I also write small accessory programs for summarizing MCMC, simulation, or plotting data in Python.

I am dedicated to contributing open-source software products for academic research. My current software projects are available on GitHub.

RevBayes is a Bayesian inference framework. It is primarily for phylogenetic inference using integrative models and MCMC.

DPPDiv is a program for estimating species divergence times and lineage-specific substitution rates on a fixed topology. The prior on branch rates is a Dirichlet process prior which clusters branches into distinct rate classes. Alternative priors including the global molecular clock and the independent rates model are also available. The priors on node ages include the birth-death (and Yule) model and the uniform distribution.

DPPDiv user discussion email list:

Descriptions of models and implementation

FossilGen is a program for simulating phylogenies and fossil occurrence times under the serial-sampled birth-death process of Stadler (J. Ther. Biol, 2010).