Anitha Kannan
RESEARCHER
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I am a researcher at Microsoft Research Search Labs.
My research focuses on machine learning, and in particular probabilistic models. I work on several applications domains - search, computer vision and computational biology, in that order.
Iam also excited to be contributing to Infer.NET, the .NET framework for machine learning, and in particular for Bayesian inference. Infer.NET is now publicly available for download. Check it out!
Previously, I spent two years at MSR Cambridge in the Machine learning and perception group.During 2007-08, I was also a Fellow of the Darwin College, University of Cambridge. I did my PhD at University of Toronto where I was a member of Brendan Frey’s Probabilistic and Statistical Inference group.
Publications
- Ariel Fuxman, Anitha Kannan, Andrew B Goldberg, Rakesh Agrawal, Panayiotis Tsaparas, and John Shafer, Improving Classification Accuracy Using Automatically Extracted Training Data, in International Conference on Knowledge Discovery and Data Mining, June 2009
- Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Michael Taylor, Yi-Min Wang, and Christos Faloutsos, Click Chain Model in Web Search, in WWW'09: Proceedings of the 18th International World Wide Web Conference, Association for Computing Machinery, Inc., April 2009
- Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI special issue), IEEE, 2009
- Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in Proc IEEE Conference on Computer Vision (CVPR). Winner of BEST STUDENT PAPER RUNNER UP AWARD., IEEE Computer Society, 2008
- Oliver Stegle, Anitha Kannan, Richard Durbin, and John M. Winn, Accounting for Non-genetic Factors Improves the Power of eQTL Studies, in International Conference on Research in Computational Molecular Biology, 2008
- Julia Lasserre, Anitha Kannan, and John Winn, Hybrid learning of large jigsaws, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007
- Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in Advances in Neural Information Processing Systems, MIT Press, 2007
- Jim C. Huang, Anitha Kannan, and John M. Winn, Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations, in International Conference on Intelligent Systems for Molecular Biology, 2007
- Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in NIPS, 2006
- Anitha Kannan, Brendan J. Frey, and Nebojsa Jojic, A generative model for dense optical flow in layers, in In Workshop on Spatial Coherence for Visual Motion Analysis, 2004
