I lead the Machine Learning Algorithms team in Cloud+Enterprise division. Our ML tools are used in many products, from Microsoft Azure ML to numerous others across the company, and we collaborate extensively with MSR and applied ML/Data Science groups. If you love both ML fundamentals and coding, and would enjoy working on creating state-of-the-art ML algorithms and systems with a fun group of excellent engineers and scientists, please ping me.
Before that, I was a researcher in the Machine Learning Department at Microsoft Research. I enjoy building ML systems and tools, and working on large-scale prediction problems on behavioral, transactional and textual data. Specific applications on which I focused recently are high-throughput ML, click probability prediction, relevant advertisement selection, constructing user profiles for targeting, and improving search relevance by mining logs of browsing behavior. In the past, I worked on semi-supervised clustering and record linkage (entity resolution, de-duplication, etc.). I am generally interested in adaptive similarity/distance functions, implementing learning algorithms on parallel/distributed platforms, and creating tools for machine learning practitioners.
I completed my Ph.D. in the Department of Computer Science at the University of Texas at Austin in 2006, where I was a member of the Machine Learning Group. Along the way, I spent the summer of 2002 at IBM T.J. Watson Research Center, and the summer/fall of 2004 at Google.
A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random FieldsMisha Bilenko, Sugato Basu, in Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), July 1, 2004,
March 27, 2006
University of Texas at Austin