What I'm Interested In
I'm fascinated by the question of how to figure out the underlying systems in a language based on the observable data. Because it's hard. Lots of the system components interact, and the data are often noisy. What information needs to already be available, and what needs to be learned from the observable data? How could someone (or something, if we're thinking of computational systems) figure out the complexity of information involved in a system like this?
The majority of my work uses computational modeling as the tool of exploration, with as much empirical grounding in available data as I can muster. This applies to computational models of human language learning (especially ones incorporating discrete representations with probabilistic learning methods like Bayesian updating), models of population-level language change driven by language learning, and more applied computational work that tries to extract real information about the world from its linguistic signature in available corpora.
In the end, though, what really drives me is that I think language is a really cool system.
And a quote that resonates with me:
"Everything's a story." - Sara Crewe, Frances Hodgson Burnett's A Little Princess
Research is like writing stories. Sometimes, they're stories of science, and sometimes of science fiction. (Hopefully, more often we hit upon the ones of science.)