machine planning, machine learning, knowledge representation, stochastic modeling, and automated reasoning
I received my computer science Ph.D. from MIT in 1996, working with David McAllester. My research is in several areas: machine planning, machine learning, knowledge representation, stochastic modeling, and automated reasoning. I have previously published in the areas of automated reasoning, formal methods, type inference, programming languages, and logic in computer science. Graduate students interested in PhD and/or MS research projects in any area of artificial intelligence are encouraged to contact me by email for an appointment to discuss possibilities. From time to time I have RA funding available for suitable PhD level projects (and in some cases for MS thesis projects). Undergraduates with strong interest in artificial intelligence research are encouraged to visit my office hours to inquire about research opportunities. My PhD thesis was on developing fast (polynomial-time) inference procedures that are effective in undecidable domains---this topic spans many sub areas of artificial intelligence and has a wide range of applications. The application discussed in my thesis is the task of inferring properties of computer programs in highly expressive property languages when limited to polynomial time, and I presented a program capable of rapidly inferring the functionality and correctness of simple sorting programs.
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