|Mailing Address:|| Machine Learning Department
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213-3891
My primary research interest covers the following four areas:
- Scalability of algorithms. This means pushing algorithms to internet scale, distributing them on many (faulty) machines, showing convergence, and modifying models to fit these requirements. For instance, randomized techniques are quite promising in this context. In other words, I'm interested in big data.
- Kernels methods are quite an effective means of making linear methods nonlinear and nonparametric. My research interests include support vector Machines, gaussian processes, and conditional random fields. Kernels are very useful also for the representation of distributions, that is two-sample tests, independence tests and many applications to unsupervised learning.
- Statistical modeling, primarily with Bayesian Nonparametrics is a great way of addressing many modeling problems. Quite often, the techniques overlap with kernel methods and scalability in rather delightful ways.