I manage the Machine Learning and Optimization group, at Microsoft Research, in Redmond, Washington. My main research interests are machine learning, online prediction, algorithm engineering, statistical learning theory, and optimization.
Recently, I’ve been focusing on machine learning algorithms and technologies tailored for tiny resource-constrained computers, like the ones embedded in intelligent devices. I’m interested in algorithms that compress large existing models, such as deep neural networks. I’m also interested in new prediction models that are specifically tailored for resource impoverished platforms.
In the past, I have worked on online learning and bandit algorithms, support vector machines, boosting algorithms, online to batch conversion techniques, incentive compatible learning, learning from multiple teachers and crowdsourced data, growing and pruning decision trees, extreme classification, and other topics in learning and optimization.