Kevin Murphy is currently an associate professor at the University of
British Columbia in Vancouver, Canada, in the departments
of computer science and statistics, which he joined in 2004.
He holds a Canada research chair in machine learning/ computational statistics.
Prior to coming to UBC, Kevin did a postdoc at MIT, his PhD at UC
Berkeley, his MSc at U. Pennsylvania, and his BSc at
U. Cambridge. Kevin is best known for his work in the area of
Bayesian networks/ graphical models. He is currently
interesting in model selection in the N << D regime (when
the number of data samples is much less than the number of variables),
in semi-supervised learning (particularly with applications to
computer vision), and in data fusion (particularly with applications
to bioinformatics).