Machine learning is an application-driven field backed by a beautiful theory. I love finding out what machine learning can do to solve real-world problems just as much as I love exploring the depths of statistical learning theory. Most of all, I enjoy working on problems where theory meets practice, whether it's a theorem suggesting a new practical method or an application pushing the boundaries of theory.

Current Projects

I am currently working on algorithms for active and semi-supervised learning and domain adaptation.


Note: author names are listed in alphabetical order.

Conference and Journal Papers

Workshop Contributions