Online papers here.

My primary line of research involves the building and study of psychocomputational     models of first language acquisition. In other words, I use computational techniques to model the process by which children learn the grammar of  their  native (first) language.

Most of these models consist of three core components:

One key research question is: 
Given a framework and an algorithm, what properties of the linguistic environment are most/least conducive to efficient learning?

An important focus is the effect of cross-language ambiguity on learning efficiency. Many sentence forms (for example Subject Verb Object) occur across many languages. It is unclear how children, given the set of ambiguous forms they are exposed to, are so efficiently able to determine the correct grammar that generates their  native language. (A second, equally important target of investigation, just underway, involves within-language ambiguity, e.g. She gave the dog biscuits, and its effect on learning efficiency.)

I have demonstrated that for a variety of proposed learning algorithms (within Chomsky's principles and parameters framework) there is a narrow range of linguistic conditions that support efficient learning and that these conditions are quite different for different algorithms. This leads to the (perhaps not so surprising) conclusion that the success of any computer model of human language acquisition must be measured against the match between the abstract computational/linguistic environment the model 'lives in' and the actual facts that delineate true human grammars.

Very recently, I have begun a secondary line of research which involves the use of machine learning techniques to garner information from corpora of natural language discourse (e.g. email and chats) in order to detect keywords that can be used to link sections of the discourse to relevant online documents.