Most of my research is in data mining and machine learning, and the application of these to problems in medicine, ecology, and microprocessor design. I do work on inductive transfer (a.k.a. multitask learning), ensemble learning, probabilistic prediction, model compression, and regression. In general, I like to work on real problems, and develop new learning methods by abstracting what is required to achieve good performance on those problems.