Research Activities

I am primarily interested in statistics and machine learning. My work has touched a large number of areas including the analysis of sequence data (e.g., web logs), probabilistic models for relational data, auction design, scalable algorithms, computational biology, data mining, collaborative filtering, recommendation systems, text classification, bioinformatics, clustering and mixture models. I have a long standing interest in model selection, learning and using graphical models, and learning causal relationships from non-randomized studies.

My research contributions have had an impact on a large number of systems/products during my tenure at Microsoft including Microsoft SQL Analysis Services (data mining), Microsoft AdCenter, Microsoft Bing, Microsoft Dynamics Live, Windows Tablet PC (handwriting recognition), Microsoft Commerce Server (recommender system).

I am an affiliate professor at the University of Washington. I am also an associate editor for the Journal of Machine Learning Research and for Statistics and Computing and was previously an associate editor for the Journal of Artificial Intelligence Research. I was the program chair for Uncertainty and Artificial Intelligence (UAI) in 2003 and the general chair for UAI in 2004.

Selected papers grouped by topic.

Machine Learning

Recommender Systems
Time Series
Adversarial Learning
Scalability

Dan Geiger, Christopher Meek, and Bernd Sturmfels, On the Toric Algebra of Graphical Models, in The Annals of Statistics, vol. 34, no. 3, pp. 1463-1492, 2006
D. Geiger, D. Heckerman, H. King, and C. Meek, Stratified exponential families: graphical models and model selection, in Annals of Statistics, vol. 29, pp. 505-529, 2001
D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie, Dependency Networks for Inference, Collaborative Filtering, and Data Visualization, in Journal of Machine Learning Research, vol. 1, no. MSR-TR-2000-16, pp. 49-75, Journal of Machine Learning Research, October 2000
David Heckerman, Christopher Meek, and Daphne Koller, Probabilistic Entity-Relationship Models, PRMs and Plate Models, in Introduction to Statistical Relational Learning, pp. 201-239, MIT Press, 2007

Graphical Models: Learning

David Maxwell Chickering, David Heckerman, and Christopher Meek, Large-Sample Learning of Bayesian Networks is NP-Hard, in Journal of Machine Learning Research, vol. 5, pp. 1287-1330, 2004
Christopher Meek, Finding a Path is Harder than Finding a Tree, in Journal of Artificial Intelligence Research, vol. 15, pp. 383-389, 2001
D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie, Dependency Networks for Inference, Collaborative Filtering, and Data Visualization, in Journal of Machine Learning Research, vol. 1, no. MSR-TR-2000-16, pp. 49-75, Journal of Machine Learning Research, October 2000
Max Chickering and Christopher Meek, On the incompatibility of faithfulness and monotone-DAG-faithfulness, in Artificial Intelligence, vol. 170, no. 8-9, pp. 653-666, 2006
D. Geiger, D. Heckerman, and C. Meek, Asymptotic model selection for directed networks with hidden variables, in Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, ® Portland, OR, pp. 283-290, Morgan Kaufmann, August 1996
B. Thiesson, C. Meek, D.M. Chickering, and D. Heckerman, Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models, in Bayesian Statistics 6, pp. 631-656, Oxford University Press, May 1999
D.M. Chickering, D. Heckerman, and C. Meek, A Bayesian approach to learning Bayesian networks with local structure, in Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, ® Providence, RI, no. MSR-TR-97-07, pp. 80-89, Morgan Kaufmann, August 1997
David Maxwell Chickering and Christopher Meek, Finding Optimal Bayesian Networks, in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, ® Alberta, Edmonton, Morgan Kaufmann, 2002
C. Meek, Strong completeness and faithfulness in Bayesian networks, in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, ® Montreal, QU, pp. 411-418, Morgan Kaufmann, August 1995

Graphical Models: Algebraic Geometry

Dan Geiger and Christopher Meek, Quantifier Elimination for statistical problems, in Proceedings of Fifteenth Conference on Uncertainty in Artificial Intelligence, ® Stockholm, Sweden, Morgan Kaufmann, August 1999
Dan Geiger, Christopher Meek, and Bernd Sturmfels, On the Toric Algebra of Graphical Models, in The Annals of Statistics, vol. 34, no. 3, pp. 1463-1492, 2006
D. Geiger, D. Heckerman, H. King, and C. Meek, Stratified exponential families: graphical models and model selection, in Annals of Statistics, vol. 29, pp. 505-529, 2001

Graphical Models: Causality

Christopher Meek and Clark Glymour, Conditioning and Intervening, in British Journal for the Philosophy of Science, vol. 45, pp. 1001-1021, 1994
C. Meek, Graphical models: selecting causal and statistical models, Pittsburgh, PA, 1997
Peter Spirtes, Thomas Richardson, Christopher Meek, Richard Scheines, and Clark Glymour, Using path diagrams as a structural equation modeling tool, in Sociological Methods and Research, vol. 27, no. 2, pp. 182-226, 1998
D. Heckerman, C. Meek, and G. Cooper, A Bayesian approach to causal discovery, in Computation, Causation, and Discovery, pp. 141-166, AAAI Press, Menlo Park, CA, 1999
P. Spirtes, C. Meek, and T. Richardson, An Algorithm for causal inference in the presence of latent variables and selection bias, in Computation, Causation, and Discovery, pp. 211-252, AAAI Press, Menlo Park, CA, 1999
C. Meek, Causal inference and causal explanation with background knowledge, in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, ® Montreal, QU, pp. 403-418, Morgan Kaufmann, August 1995
C. Meek, Strong completeness and faithfulness in Bayesian networks, in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, ® Montreal, QU, pp. 411-418, Morgan Kaufmann, August 1995
C. Meek and D. Heckerman, Structure and Parameter Learning for Causal Independence and Causal Interaction Models, in Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, ® Providence, RI, Morgan Kaufmann, August 1997
P. Spirtes, C. Glymour, R. Scheines, C. Meek, S. Fienberg, and E. Slate, Prediction and experimental design with Graphical causal models, in Computation, Causation, and Discovery, pp. 65-94, AAAI Press, Menlo Park, CA, 1999

Graphical Models: Inference

Christopher Meek and Ydo Wexler, Improved Approximate Sum-Product Inference Using Multiplicative Error Bounds, in Bayesian Statistics 9, Oxford University Press, 2011
Dan Geiger, Christopher Meek, and Ydo Wexler, A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints, in Journal of Artificial Intelligence Research, vol. 27, pp. 1-23, 2006
Dan Geiger, Christopher Meek, and Ydo Wexler, Speeding up HMM algorithms for genetic linkage analysis, in Bioinformatics, Oxford University Press, 2009
Ydo Wexler and Christopher Meek, Inference for Multiplicative Models, in Proceedings of Uncertainty in Artificial Intelligence, 2008
Ydo Wexler and Christopher Meek, MAS: a multiplicative approximation scheme for probabilistic inference, in NIPS, 2008

Machine Learning: Recommender Systems

Asela Gunawardana and Christopher Meek, Tied Boltzmann Machines for Cold Start Recommendations, in ACM International Conference on Recommender Systems, Association for Computing Machinery, Inc., October 2008
Asela Gunawardana and Christopher Meek, A Unified Approach to Building Hybrid Recommmender Systems, in ACM International Conference on Recommender Systems, Association for Computing Machinery, Inc., October 2009
D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie, Dependency Networks for Density Estimation, Collaborative Filtering, and Data Visualization, in Proceedings of Sixteenth Conference on Uncertainty in Artificial Intelligence, ® Stanford, CA, pp. 82-88, 2000
Andrew Zimdars, David Maxwell Chickering, and Christopher Meek, Using Temporal Data for Making Recommendations, in Proceedings of Seventeenth Conference on Uncertainty in Artificial Intelligence, ® Seattle, WA, pp. 580-588, 2001

Machine Learning: Time Series

Asela Gunawardana, Christopher Meek, and Puyang Xu, A Model for Temporal Dependencies in Event Streams, in Neural Information Processing Systems, Neural Information Processing Systems Foundation, December 2011
Guy Shani, Asela Gunawardana, and Christopher Meek, Unsupervised hierarchical probabilistic segmentation of discrete events, in Intelligent Data Analysis, IOS Press, 27 June 2011
Bo Thiesson, David Maxwell Chickering, David Heckerman, and Christopher Meek, ARMA Time-Series Modeling with Graphical Models, in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, AUAI Press, July 2004
Christopher Meek, David Maxwell Chickering, and David Heckerman, Autoregressive tree models for time-series analysis, in Proceedings of the Second International SIAM Conference on Data Mining, SIAM, Arlington, VA, April 2002
Heikki Mannila and Christopher Meek, Global Partial Orders from Sequential Data, in Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ® Boston, MA, ACM Press, 2000
Guy Shani, Christopher Meek, and Asela Gunawardana, Hierarchical Probabilistic Segmentation of Discrete Events, in IEEE International Conference on Data Mining, IEEE, 6 December 2009
Igor Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, and Steven White, Model-Based Clustering and Visualization of Navigation Patterns on a Web Site, in Data Mining and Knowledge Discovery, vol. 7, no. 4, pp. 399-424, 2003

Machine Learning: Adversarial Learning

Daniel Lowd and Christopher Meek, Adversarial learning, in KDD, pp. 641-647, 2005
Daniel Lowd and Christopher Meek, Good Word Attacks on Statistical Spam Filters, in Conference on email and anti-spam (CEAS), 2005

Machine Learning: Scalability

Bo Thiesson, Christopher Meek, and David Heckerman, Accelerating EM for large databases, in Machine Learning, vol. 45, pp. 279-299, Kluwer Academic, January 2001
Christopher Meek, Bo Thiesson, and David Heckerman, The Learning-Curve Sampling Method Applied to Model-Based Clustering, in Journal of Machine Learning Research, vol. 2, pp. 397-418, Journal of Machine Learning Research, February 2001

E-Commerce

Ece Kumar, Eric Horvitz, and Chris Meek, Mobile Opportunistic Commerce: Mechanisms, Architecture, and Application, in AAMAS, 2008
Christopher Meek, David Maxwell Chickering, and David Wilson, Stochastic and contingent payment auctions, in Workshop on Sponsored Search Auctions, ACM Electronic Commerce, 2005
Asela Gunawardana and Christopher Meek, Aggregators and Contextual Effects in Search Ad Markets, in WWW Workshop on Targeting and Ranking for Online Advertising, Association for Computing Machinery, Inc., April 2008
Asela Gunawardana, Christopher Meek, and Jody Biggs, A Quality-Based Auction for Search Ad Markets with Aggregators, in ACM EC Workshop on Ad Auctions, Association for Computing Machinery, Inc., June 2008
Greg Linden, Christopher Meek, and Max Chickering, The Pollution Effect: Optimizing Keyword Auctions by Favoring Relevant Advertising, in Fifth workshop on Ad Auctions, 6 July 2009

Computational Biology

Jim C. Huang, Christopher Meek, Carl Kadie, and David Heckerman, Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies, in PLoS ONE, PLoS, 12 July 2011
S. Bercovici, C. Meek, Y. Wexler, and D. Geiger, Estimating Genomewide IBD-sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping, in Bioinformatics, vol. 26, no. 12, pp. 175-182, Oxford University Press, 2010
Dan Geiger, Christopher Meek, and Ydo Wexler, Speeding up HMM algorithms for genetic linkage analysis, in Bioinformatics, Oxford University Press, 2009
Helge G. Gyllenberg, Mats Gyllenberg, Timo Koski, Tatu Lund, Heikki Mannila, and Christopher Meek, Singling out ill-fit items in a classification. Application to the taxonomy of Enterobacteriaceae, in Archive of Control Science, vol. 9, pp. 97-107, 1999
Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, and David Heckerman, Using epitomes to model genetic diversity: Rational design of HIV vaccines, in NIPS, 2005
Vladimir Jojic, Nebojsa Jojic, Christopher Meek, Dan Geiger, Adam Siepel, David Haussler, and David Heckerman, Efficient approximations for learning phylogenetic HMM models from data, in ISMB/ECCB (Supplement of Bioinformatics), pp. 161-168, 2004
Gregory F. Cooper, Constantin F. Aliferis, R. Ambrosino, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Clark Glymour, G. Gordon, B. H. Hanusa, Janine E. Janosky, Christopher Meek, Tom M. Mitchell, Thomas Richardson, and Peter Spirtes, An evaluation of machine-learning methods for predicting pneumonia mortality, in Artificial Intelligence in Medicine, vol. 9, no. 2, pp. 107-138, 1997

Papers (annotated bibliography)

Bibtex entries

Pictures

Contact Info

E-mail:meek@microsoft.com

FAX: 425-936-7329

Mail: One Microsoft Way, 99/3118, Redmond WA 98052-6399, USA

Recent Publications

Wen-tau Yih, Ming-Wei Chang, Christopher Meek, and Andrzej Pastusiak, Question Answering Using Enhanced Lexical Semantic Models, in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, August 2013
Alisa Zhila, Wen-tau Yih, Chris Meek, Geoffrey Zweig, and Tomas Mikolov, Combining Heterogeneous Models for Measuring Relational Similarity, in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-2013), Association for Computational Linguistics, 27 May 2013
Ankur P. Parikh, Asela Gunawardana, and Christopher Meek, Conjoint Modeling of Temporal Dependencies in Event Streams, in UAI Bayesian Modelling Applications Workshop, 18 August 2012
Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J.C. Burges, Ainur Yessenalina, and Qiang Liu, Computational Approaches to Sentence Completion, in ACL 2012, ACL/SIGPARSE, July 2012
Asela Gunawardana, Christopher Meek, and Puyang Xu, A Model for Temporal Dependencies in Event Streams, in Neural Information Processing Systems, Neural Information Processing Systems Foundation, December 2011
Jim C. Huang, Christopher Meek, Carl Kadie, and David Heckerman, Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies, in PLoS ONE, PLoS, 12 July 2011
Guy Shani, Asela Gunawardana, and Christopher Meek, Unsupervised hierarchical probabilistic segmentation of discrete events, in Intelligent Data Analysis, IOS Press, 27 June 2011
Wen-tau Yih, Kristina Toutanova, John Platt, and Chris Meek, Learning Discriminative Projections for Text Similarity Measures, in Proceedings of the Fifteenth Conference on Computational Natural Language Learning , Association for Computational Linguistics, 13 June 2011
Christopher Meek and Ydo Wexler, Improved Approximate Sum-Product Inference Using Multiplicative Error Bounds, in Bayesian Statistics 9, Oxford University Press, 2011
Jim Huang, Nebojsa Jojic, and Christopher Meek, Exact inference and learning for cumulative distribution functions on loopy graphs, in Advances in Neural Information Processing Systems 23, MIT Press, December 2010
Wen-tau Yih and Chris Meek, Learning Vector Representations for Similarity Measures, no. MSR-TR-2010-139, 25 October 2010
Christopher Meek and Bo Thiesson, Probabilistic Inference for CART network, no. MSR-TR-2010-40, April 2010
Asela Gunawardana, Tim Paek, and Christopher Meek, Usability Guided Key-Target Resizing for Soft Keyboards, in International Conference on Intelligent User Interfaces, Association for Computing Machinery, Inc., February 2010
S. Bercovici, C. Meek, Y. Wexler, and D. Geiger, Estimating Genomewide IBD-sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping, in Bioinformatics, vol. 26, no. 12, pp. 175-182, Oxford University Press, 2010
Guy Shani, Christopher Meek, and Asela Gunawardana, Hierarchical Probabilistic Segmentation of Discrete Events, in IEEE International Conference on Data Mining, IEEE, 6 December 2009
Guy Shani and Christopher Meek, Improving Existing Fault Recovery Policies, in Advances in Neural Information Processing Systems, MIT Press, December 2009
Asela Gunawardana and Christopher Meek, A Unified Approach to Building Hybrid Recommmender Systems, in ACM International Conference on Recommender Systems, Association for Computing Machinery, Inc., October 2009
Greg Linden, Christopher Meek, and Max Chickering, The Pollution Effect: Optimizing Keyword Auctions by Favoring Relevant Advertising, in Fifth workshop on Ad Auctions, 6 July 2009
Darko Kirovski and Christopher A. Meek, Tunneled TLS for Multi-Factor Authentication, no. MSR-TR-2009-50, 23 April 2009
Chong Wang, Bo Thiesson, Christopher Meek, and David Blei, Markov Topic Models, in D. van Dyk and M. Welling (Eds.), Proceedings of The Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, JMLR: W&CP 5, Journal of Machine Learning Research, April 2009
Guy Shani, Christopher Meek, Tim Paek, Bo Thiesson, and Gina Danielle Venolia, Searching large indexes on tiny devices: Optimizing binary search with character pinning, in Proc. IUI 2009, Association for Computing Machinery, Inc., February 2009
Dan Geiger, Christopher Meek, and Ydo Wexler, Speeding up HMM algorithms for genetic linkage analysis, in Bioinformatics, Oxford University Press, 2009
Alnur Ali and Christopher Meek, Predictive Models of Form Filling, no. MSR-TR-2009-1, January 2009
Asela Gunawardana and Christopher Meek, Tied Boltzmann Machines for Cold Start Recommendations, in ACM International Conference on Recommender Systems, Association for Computing Machinery, Inc., October 2008
Ming-Wei Chang, Wen-tau Yih, and Christopher Meek, Partitioned Logistic Regression for Spam Filtering , in Proceedings of The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 26 August 2008