My research interests are in machine learning, artificial
intelligence, and computational biology.
My PhD thesis focused on two novel approaches in probabilistic
models. Each of these is described on its
own page. Each page also includes links to download software
packages for the Mathematica programming langauge, and
factor graphs (a superset of Bayesian networks and Markov
random fields). I used them to implement a package that,
among other things, automatically generates runnable source
code for the process of performing Metropolis-Hastings
sampling on arbitrary
formalizes a method of
representing probability distributions that evolve
continuously in time. The software package leverages
Dependency Diagrams to optimize GCCD models.
My thesis is also available as a