Machine Learning and Statistical Pattern Recognition.
Dimensionality Reduction and Feature Selection.
Learning Theory and Mathematical Statistics, especially non-asymptotic (finite sample) theory.
Random Matrix Theory.
Measure Concentration.
Applications of all of the above. In particular using theory to better understand existing techniques, and to develop efficient, effective, and principled methods for big data settings with performance guarantees