Program in Neuroscience

About

My research focuses on language learning, comprehension, and knowledge representation in humans and machines. I employ a combination of computational and experimental techniques to examine large-scale statistical structure of certain environments (such as language corpora) with the goal of understanding how this structure could be learned and represented with the mathematical capabilities of human learning and memory. This line of my research has applications in machine learning and intelligent systems. The overall premise of my work is that complex behavior often naturally emerges as a product of many simple processors working together at a large scale in response to statistical redundancies in a complex environment.

Under the same unified theme of large-scale statistical learning, I study human associative and recognition memory, categorization, decision making, and the role of attention in reading and perception. I am particularly interested in the temporal dynamics of learning in all these domains, and how to model the time course of knowledge acquisition. My secondary interests involve the application of these models to practical problems in text mining, intelligent search algorithms, and automated comprehension and scoring algorithms.