Anitha Kannan
Researcher
1065 La Avenida,
Mountain View,
CA, USA – 94043
ankannan AT microsoft.com
I am at Microsoft Research
Search Labs, working on interesting search related research problems. My research focuses on machine learning, with
emphasis on probabilistic models. I am
currently excited to be contributing to big happenings: Live Search and the .Net library (Infer.Net) for
machine learning. My other interests include computer vision and computational
biology.
Previously, I was an associate researcher
at MSR
Cambridge in the machine
learning and perception group. I was also a Fellow of the Darwin College, University of Cambridge
during 2007-2008. I did my PhD at University of Toronto
where I was a member of Brendan Frey’s Probabilistic and Statistical Inference group.
Publications:
Epitomic
location recognition [PDF]
K. Ni, A. Kannan, A. Criminisi and J. Winn
CVPR, 2008 [Best student paper
runner-up]
Accounting for non-genetic factors improves the power
of eQTL studies [PDF]
O. Stegle, A. Kannan, R. Durbin, and J. Winn
RECOMB, 2008
Bayesian association of haplotypes and non-genetic
factors to regulatory and phenotypic variation in human populations [PDF]
J.
Huang, A. Kannan and J. Winn
ISMB, 2007. Also to appear in Bioinformatics, 2007
Hybrid learning of large jigsaws [PDF][Quick overview PDF]
J. Lasserre, A.
Kannan and J. Winn
CVPR, 2007
A Bayesian model that links
microarray mRNA measurements to mass spectrometry protein measurements [PDF][slides in PDF]
A. Kannan, A. Emili
and B.J. Frey
RECOMB, 2007. Also
appears in Lecture Notes in
Bioinformatics, LNBI 4453, Springer 2007
Integrated proteomic and transcriptomic profiling of
mouse lung development and Nmyc target genes [PDF]
B. Cox(*), T.
Kislinger(*), D.A. Wigle, A. Kannan, K.
Brown, T. Okubo , B. Hogan, I. Jurisica, B.J. Frey, J. Rossant, & A. Emili
Molecular
Systems Biology 3:109, 2007 [(*): joint first authors]
Generative models for 2-D images of 3-D scenes (abstract and thesis)
Anitha Kannan,
Ph.D. Thesis, Department of Computer Science,
University of Toronto, 2006.
Clustering
appearance and shape by learning jigsaws [PDF] [Quick overview PDF]
A. Kannan, J. Winn
and C. Rother
NIPS, 2006
Global survey of organ and organelle protein
expression in mouse: combined proteomic and transcriptomic profiling [link]
T. Kislinger(*), B. Cox(*), A.
Kannan(*), C. Chung, A. Ignatchenko , M.S. Scott, A. Gramolini, Q. Morris, T.
Hughes, J. Rossant, B.J. Frey, & A. Emili
Cell, April 2006
[(*): joint first authors]
Layers of appearance and deformation [PDF][web]
A. Kannan, N. Jojic
and B.J. Frey
AISTATS, 2005
Comprehensive survey of tissue and
organelle selective protein expression in mouse: Integrated global proteomic,
bioinformatic and genomic analysis
T. Kislinger(*), B.
Cox(*), A. Kannan(*), C. Chung, J. Rossant, B.J. Frey, & A. Emili
Keystone Symposia:
Proteomics and Bioinformatics,
A generative model for dense optical flow in layers [PDF]
A. Kannan, B.J. Frey
and N. Jojic
Workshop on Spatial
Coherence for Visual Motion Analysis, In conjunction with ECCV 2004
Epitomic analysis of appearance and shape [PDF]
[web]
N. Jojic, B.J. Frey
and A. Kannan
ICCV, 2003
Layered density models and unsupervised video analysis [PDF]
B.J. Frey, N. Jojic
and A. Kannan
CVPR, 2003
Fast transformation-invariant factor analysis [PDF][web]
A. Kannan, N. Jojic
and B.J. Frey
NIPS, 2002. Also to
appear in International Journal of
Computer Vision: Special Issue on Learning for Vision and Vision for Learning,
2007
Product analysis - Learning observations as products
of hidden variables [PS]
B.J. Frey, A. Kannan
and N. Jojic
NIPS, 2001
Product analysis - Learning observations as products
of hidden variables (abstract
and thesis)
Anitha Kannan,
M.Math. Thesis, Department of Computer Science,
University of Waterloo, 2001
Accumulator networks: Suitors of local probability
propagation [PS]
B.J. Frey and A.
Kannan
NIPS, 2000