Misha Bilenko


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Update: I moved to Moscow where I lead the Machine Intelligence and Research (MIR) division in Yandex. We have all sorts of serious fun, including but not limited to: speech recognition and synthesis, computer vision, machine translation, personal assistants, machine learning algorithms and platforms, and research in all of the above.

Before that, I led the Machine Learning Algorithms team in Cloud+Enterprise division at Microsoft. Our ML tools are used in many products, from Microsoft Azure ML, to SQL Server, to numerous others across all divisions of the company. We collaborate extensively with MSR and applied ML/Data Science groups.

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.

Before that, 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.


  • Learning from large datasets
  • Learnable similarity functions and their applications in information integration (e.g., record linkage/identity uncertainty) and text mining

  • Semi-supervised clustering

    • Probabilistic Semi-Supervised Clustering with Constraints
      Sugato Basu, Mikhail Bilenko, Arindam Banerjee, and Raymond J. Mooney. In Semi-Supervised Learning, O. Chapelle, B. Schölkopf, and A. Zien (eds.), MIT Press, 2006.
      Note: this chapter summarizes the KDD and ICML papers below

    • A Probabilistic Framework for Semi-Supervised Clustering
      Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), pp.59-68, Seattle, WA, August 2004.
      (Winner of Best Research Paper Award)

    • Integrating Constraints and Metric Learning in Semi-Supervised Clustering
      Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney. In Proceedings of the 21st International Conference on Machine Learning (ICML-2004), pp.81-88, Banff, Canada, July 2004.

    • A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields
      Mikhail Bilenko and Sugato Basu. In Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), pp.17-22, Banff, Canada, July 2004.

  • Indirect learning in information integration (record linkage, information extraction), text classification, and clustering

    • Two Approaches to Handling Noisy Variation in Text Mining
      Un Yong Nahm, Mikhail Bilenko, and Raymond J. Mooney. In Proceedings of the ICML-2002 Workshop on Text Learning (TextML'2002), pp.18-27, Sydney, Australia, July 2002.

In my leisure time I enjoy applying hill-climbing search and gradient descent algorithms to real-world domains, which are almost as cool as the cool stuff that my sister does.
Contact Info

Email misha@bilenko.com