I enjoyed building ML systems and tools, and working on large-scale prediction problems on behavioral, transactional and textual data. Specific applications on which I focused were high-throughput ML, click probability prediction, relevant ad selection, constructing user profiles for targeting, and improving search relevance with logs of user behavior. Earlier, I worked on semi-supervised clustering and record linkage (a.k.a. entity resolution a.k.a. de-duplication a.k.a. identity uncertainty a.k.a. co-reference resolution...).

Besides all kinds of ML applications, I'm generally interested in adaptive similarity(distance, kernel, divergence, embedding, ...) functions, implementing learning algorithms on parallel/distributed platforms, and tools for machine learning practitioners. And evaluation methodology/metrics for all of these, offline and online.