Andrew McCallum Talks

Selected Upcoming & Past Talks

MCMC in Probabilistic Databases and Cluster Computing
NIPS Workshop on Monte Carlo Methods for Bayesian Inference in Modern Day Applications, Whistler, British Columbia, Canada. December 10, 2010.
Probabilistic Programming and Probabilistic Databases with Imperatively-defined Factor Graphs
UT Austin, Departmental Seminar, Austin Texas, October 2010. (pdf)
Probabilistic Programming
Google Research, Mountain View, CA. August 2010.
MCMC Inference Inside the DB for Extraction, Resolution, Alignment, Provenance and Queries
First International Workshop on Automated Knowledge Base Construction (AKBC), May 2010.
Discovering Patterns in Text and Relational Data with Bayesian Latent-Variable Models
Data-Intensive Research Workshop, Edinburgh, Scotland. March 18 2010.
Inference and Learning in Large-scale Relational Conditional Random Fields
PASCAL European Network of Excellence Strategy Meeting. March 16 2010.
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs
Paris Workshop, March 2010.
Information Extraction, Information Integration and Joint Inference
NAACL/HLT Invited Talk, 2009.
Injecting Prior Domain Knowledge into Machine Learning
Carnegie Mellon University, 2008.
Information Extraction, Data Mining and Joint Inference
UC Irvine Distinguished Lecture Series, 2008.
Bayesian Models of Social Networks and Text with Application to Political, Legal and Bibliometric Data
Kennedy School of Government, 2007.
Accurate, Automated Citation Extraction and Reference Linking with Conditional Random Fields
National Library of Medicine, 2005.
Information Extraction, Social Network Analysis and Joint Inference
Stanford University, 2005.

	Professor Andrew McCallum Computer Science Department University of Massachusetts Amherst mccallum@cs.umass.edu +1 413 545-1323 (vox) +1 413 545-1789 (fax)
Contact Bio Vita Publications Talks Projects Lab People Code Data Teaching Personal	Selected Upcoming & Past Talks MCMC in Probabilistic Databases and Cluster Computing NIPS Workshop on Monte Carlo Methods for Bayesian Inference in Modern Day Applications, Whistler, British Columbia, Canada. December 10, 2010. Probabilistic Programming and Probabilistic Databases with Imperatively-defined Factor Graphs UT Austin, Departmental Seminar, Austin Texas, October 2010. (pdf) Probabilistic Programming Google Research, Mountain View, CA. August 2010. MCMC Inference Inside the DB for Extraction, Resolution, Alignment, Provenance and Queries First International Workshop on Automated Knowledge Base Construction (AKBC), May 2010. Discovering Patterns in Text and Relational Data with Bayesian Latent-Variable Models Data-Intensive Research Workshop, Edinburgh, Scotland. March 18 2010. Inference and Learning in Large-scale Relational Conditional Random Fields PASCAL European Network of Excellence Strategy Meeting. March 16 2010. FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs Paris Workshop, March 2010. Information Extraction, Information Integration and Joint Inference NAACL/HLT Invited Talk, 2009. Injecting Prior Domain Knowledge into Machine Learning Carnegie Mellon University, 2008. Information Extraction, Data Mining and Joint Inference UC Irvine Distinguished Lecture Series, 2008. Bayesian Models of Social Networks and Text with Application to Political, Legal and Bibliometric Data Kennedy School of Government, 2007. Accurate, Automated Citation Extraction and Reference Linking with Conditional Random Fields National Library of Medicine, 2005. Information Extraction, Social Network Analysis and Joint Inference Stanford University, 2005.