I'm extremely interested in the analysis of large-scale scientific datasets with spatial-temporal variation (or dynamical simulations that can model this spatial-temporal variation). In particular, I'm interested in datasets in atmospheric science, astronomy, systems biology (esp. metabolomics), computational chemistry, and computational neuroscience. While I'm ideally interested in trying to get at the leading edge of a rising field, this is not the most important thing, and certainly not as important as pursuing one's competencies. I'm a fan of scripting languages (like MATLAB and Python) and I love to catalyze my research by asking questions (both high-level and low-level) off sites like Stack Exchange and Quora.
My current project (under Dargan Frierson) is to work with the CERES dataset in order to analyze the factors that affect the variation in total amount of radiation (both shortwave and longwave) entering/exiting the atmosphere. How much of this variation can be explained by large-scale modes of variability like NINO34, SAM, and NAM? What regions of the globe contribute more to this variation, and what regions of the globe contribute less to this variation? I'm particularly interested in atmospheric science because of its relative openness, and also because it has been ahead of most other fields in data science (not to mention that my home school is extremely strong in it).
In the past, I've worked on projects that involved the large-scale analysis of the distribution of stars in the Milky Way galaxy, as well as projects using the CAM 3.0 model to model how changing various parameters of an idealized aquaplanet (rotation rate, orbital tilt) affect large-scale properties like the pole-to-equator temperature gradient and the maximum average wind speeds.
I'm also extremely interested in movements such as digital preservation and maximizing the long-term impact of scientific research. I'm passionate about research techniques that maximize the generalizability of research (between academic areas and also across decades of time - I want what I do to still be relevant 10 years from now). I also aspire to make my science as open as possible, although there is a fine line with sharing that I'm still trying to learn.
Just as much, I'm also interested in promoting student interest in the computational sciences (where there is significant potential for innovation). I have been involved in advising students under the Cognito Mentoring program. I'm also passionate about personal genomics, and am a participant in the Personal Genome Project.
You can also visit my old Homepage for a hint of what I used to be.