I use visual aids in a lot of my classes to help students understand important concepts. Here are some examples that I created using R.
My current research involves computational statistics and Bayesian non-parameteric processes. As part of my dissertation research, I developed theory of a graphical version of the famous Dirichlet Process. Graphical in this sense refers to a set of independence relationships among observed variables. I apply this theory to make inference about Bayesian mixture models under conditional independence constraints. By comparing the marginal likelihood of a set of data under various graphical models, I determine the relationship between variables from a mixture of distributions. I have written code for these two applications, which is available here. In addition to working with the graphical Dirichlet Process, I am interested in expanding the theory to other extensions and applications, including the Hierarchical Dirichlet Process of Teh, et al., and Pitman-Yor Processes. Graphical versions of the Beta Process are also particularly exciting.
It may be a cliché, but I am generally eager to learn about almost any topic. I have worked at various times as a teaching assistant in a chemistry lab, a research assistant in a cognitive psychology lab, and as a proof-reader and solutions-writer for a topology textbook. I hope to become more involved in interdisciplinary research in the near future, especially regarding cognition and linguistics.