Our research focuses on the computational foundations of intelligent behavior. We develop theories and systems pertaining to intelligent behavior using a unified methodology -- at the heart of which is the idea that learning has a central role in intelligence.
We attempt to understand the role of learning in supporting reasoning and other high-level cognitive tasks and use this understanding to develop systems that learn and make intelligent inferences in complex domains. Such systems would acquire the bulk of their knowledge from raw, real world data, and behave robustly when presented with new, previously unseen, situations. These systems can be studied at various levels of abstraction and from various viewpoints. We have concentrated on developing the theoretical basis within which to address some of the obstacles and on developing an experimental paradigm so that realistic experiments (in terms of scale and resources) can be performed to validate the theoretical basis. The main area of concentration in this realm has been intelligent human computer interaction; specifically, natural language processing and intelligent information access.
Our work spans several aspects of this problem -- from theoretical questions in machine learning, knowledge representation and reasoning to experimental paradigms and large scale system development -- and draws on methods from theoretical computer science, probability and statistics, artificial intelligence, linguistics and experimental computer science.