While I haven't settled on my research interests at this early stage of my academic career, I can identify some interesting avenues that I may pursue. Generally, I'm interested in Bayesian statistical methods, economics and applications of the former to the latter. Within pure statistics, I'm interested in improving Markov Chain Monte Carlo computational methods through, e.g., exploiting the massively parallel computing power of GPUs. I'm currently writing CUDA C code to perform covariate selection in the standard linear model by fitting all possible models in parallel on a GPU. You can view this project as it progresses on github.
In economics, I'm interested in improving the methodologies employed in experimental economics, particularly through the use of more sophisticated statistical models and Bayesian methods. For example, in my master's thesis I constructed a class of models to analyze an experiment with possible dependence across time and replications, then used model averaging to average inferences over a plausible subset of this class of models.