BIOGRAPHY
I am primarily interested in developing computer programs that solve so-called combinatorial problems which include some problems, such as recognizing objects and comprehending language, that humans solve quite easily. Combinatorial problems generally require searching among a large number of possibilities formed by combining elements from a set of primitive components (lines and surfaces in the case of objects, and words in the case of language). Humans appear to solve these problems by exploiting structure in the underlying problem, example, regularities in how objects reflect light or the rules that govern human languages. Presumably this ability to exploit structure must be encoded in the brain. How the brain solves seemingly difficult combinatorial problems, and which problems are reducible, is of great theoretical and practical interest. If we could identify and reverse-engineer these encodings we might be able to develop better programs for recognizing objects and comprehending language. These encodings might also generalize to enable us to solve other combinatorial problems such as those involved in learning to navigate in cities, planning trips, or scheduling appointments.
As an undergraduate majoring in mathematics and philosophy at Virginia Polytechnic Institute and State University, I became interested in this line of research. The knotty conundrums about the human mind presented by philosophy are also questions that apply to the development of artificial intelligence. I received my doctorate in computer science from Yale University.
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