Steve Hanneke is an Assistant Professor in the Computer Science
Department at Purdue University. His research explores the theory of
machine learning, with a focus on reducing the number of training
examples sufficient for learning. His work develops new approaches to
supervised, semi-supervised, active, and transfer learning, and also
revisits the basic probabilistic assumptions at the foundation of
learning theory. Steve earned a Bachelor of Science degree in Computer
Science from UIUC in 2005 and a Ph.D. in Machine Learning from
Carnegie Mellon University in 2009 with a dissertation on the
theoretical foundations of active learning. Steve's website can be
found at http://www.stevehanneke.com