AIC Seminar Series

Do you prefer Leno to Letterman? Pat Metheny to Kenny G? Basketball to Baseball? Jodie Foster to Demi Moore? If you answer ``yes'' to all of these questions, you probably have preferences very similar to mine. But exactly how similar? How can we define and measure similarity of preferences, given limited available information? In this talk I will discuss my experiences in attempting to answer this question. Along the way, I will show how decision theory, collaborative filtering, case-based reasoning, Markov chain Monte Carlo simulation, and kernel methods all have a little something to help me in this effort.

Vu Ha is currently a research scientist at Honeywell's Information and Decision Technology Center in Minneapolis, MN. His main research interest is automated decision making, and his dissertation addresses issues that arise in representing, eliciting, and reasoning with incomplete preferences. He is also interested in probabilistic reasoning and statistical machine learning. Vu Ha obtained a Ph.D. in computer science from the University of Wisconsin-Milwaukee, and B.S. and M.S. degrees in computer science respectively from Kossuth University and Eotvos University in Hungary. At the age of fifteen, he earned a gold medal at the International Mathematics Olympiad in Warsaw, Poland.

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