My primary research interests include machine learning, computational semantics, natural language processing, cognitive science, and the application of these fields to educational technology, clinical informatics, companion robots, and end-user programming.

I am very interested in Machine Learning theory and application. I have researched methods to improve the predictions of class probability estimates and I am furthering this research to make advances in semi-supervised and active learning from large unlabeled corpora. I am particularly interested in self-training and co-training techniques.

My primary research focuses on computational models intended to facilitate machine understanding of text and spoken dialogue. This includes generating semantic representations (semantic facets, linguistic dependencies, predicate argument structure, discourse relations, etc.), extracting lexical and conceptual relations from distributional statistics of large corpora, and recognizing presupposition, implicature and entailment.

I am applying much of my research in the area of Intelligent Tutoring Systems, which provides an excellent platform to investigate both computational and human learning theory. In the context of a known reference answer to a tutor's question, I extract a knowledge representation of the fine-grained facets of the reference answer and classify each according to whether you can infer from the student response that they understand the facet, contradicted it, left it unaddressed, or expressed something related that is perhaps a misconception. The goal of this fine-grained analysis, classifying more precisely the student's apparent understanding of detailed facets, is to facilitate improved pedagogical dialogue and eventually Socratic tutoring. To that end, I am also researching automatic question generation and question answering.

To support this work, I had a corpus annotated to indicate elementary school students' apparent understanding of a broad spectrum of science concepts. This corpus, comprised of 15,357 student responses and 142,451 facet annotations for questions from 16 different science areas, can be downloaded from my Resources page.

I am currently investigating the issues involved in developing an end-to-end question answering and data mining system to support clinical researchers and physicians in a clinical setting. I am also actively involved in the Question Generation Challenge community and have two proposals pending that will incorporate both question generation and question answering.

I have a dual Ph.D. in Computer Science and Cognitive Science and have studied psycholinguists and human learning theory. I intend to incorporate findings from these areas throughout my future research in computational semantics, educational technologies, and machine learning.

I am also planning to apply my ML research and software engineering experience to end-user programming.