I am interested in learning from data. My work is primarily inspired by the fields of statistics, machine learning, data mining, and artificial intelligence. My recent work has focused on query suggestion for search, online advertising, spam filtering, time-series analysis, scalable learning algorithms, boosting, personalization for handwriting recognition, clustering and mixture modeling, video-tooning, and smart text input methods. I have a long standing interest in model selection, Bayesian networks, as well as graphical models in general.

Publications

Jingrui He, Bo Thiesson. Asymmetric Gradient Boosting with Application to Spam Filtering August 2007
Kenneth Church, Bo Thiesson. The Wild Thing Goes Local July 2007
Kenneth Church, Bo Thiesson, Robert Ragno. K-Best Suffix Arrays April 2007
Bo Thiesson, Jesper Lind. Mining cross-predicting stochastic ARMA time series in SQL server 2005 May 2006
Kenneth Church, Bo Thiesson. The Wild Thing! May 2005
Bo Thiesson, Christopher Meek. Efficient gradient computation for conditional Gaussian models January 2005
Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek. ARMA Time-Series Modeling with Graphical Models July 2004
Jue Wang, Bo Thiesson, Yingqing Xu, Michael Cohen. Image and Video Segmentation by Anisotropic Kernel Mean Shift May 2004
Bo Thiesson, Christopher Meek. Discriminative Model Selection for Density Models January 2003
Christopher Meek, Bo Thiesson, David Heckerman. Staged mixture modeling and boosting August 2002
Christopher Meek, Bo Thiesson, David Heckerman. The learning-curve method applied to clustering August 2001
Christopher Meek, Bo Thiesson, David Heckerman. The learning-curve sampling method applied to model-based clustering February 2001
Bo Thiesson, Christopher Meek, David Heckerman. Accelerating EM for large databases January 2001
David Maxwell Chickering, David Heckerman, Christopher Meek, John Platt, Bo Thiesson. Goal-oriented clustering May 2000
Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman. Computationally efficient methods for selecting among mixtures of graphical models May 1999
Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman. Learning mixtures of DAG models August 1998
Bo Thiesson. Score and information for recursive exponential models with incomplete data August 1997
Bo Thiesson. Accelerated quantification of Bayesian networks with incomplete data August 1995
Søren Højsgaard, Bo Thiesson. BIFROST - Block recursive models Induced From Relevant knowledge, Observations and Statistical Techniques January 1995
Steffen L. Lauritzen, Bo Thiesson, David J. Spiegelhalter. Diagnostic systems by model selection: a case study January 1994

Research Interests

Publications