I'm a researcher in the Machine Learning Department of Microsoft Research, in Redmond, Washington. My main research interests are machine learning, online prediction, statistical learning theory, and optimization.
Recently, I've been focusing on learning in multi-teacher environments, learning on parallel architectures (multicore CPUs, clusters, GPUs), and learning predictors that are intelligible to humans. In the past I have written papers on support vector machines, boosting algorithms, online to batch conversion techniques, bandit problems, incentive compatible learning, and other aspects of statistical and online learning.
On the applied side, I work with the talented engineers in Bing and with colleagues in Microsoft Research to develop and implement large-scale learning techniques for web search applications.
In winter 2011 I taught a graduate course on statistical learning theory at the CS department in the University of Washington.
