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I'm in my sixth year as a PhD student, working in the general area of machine learning.
My current work focuses on making fundamental methods fast/scalable for massive datasets using Monte Carlo
principles. For instance, we have fast Monte Carlo versions of the nonparametric kernel methods (KDE, kernel regression, etc.),
and are working on fast Monte Carlo linear algebraic operations such as the SVD (which would apply to PCA, etc.).
Other work includes prediction in time series and dynamical systems, the Netflix prize competition (collaborative
filtering), and hidden state discovery.
My advisor is Charles Isbell, and I also do work
with Alex Gray.
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