My research focuses on developing and analyzing probabilistic and statistical models for natural language text applications such as document or sentence classification, and document clustering. I am more broadly interested in the application of machine learning techniques to tasks for which humans naturally perform well. [CV]

**A. Chambers**. Statistical models for text classification: Applications and analysis, Ph.D., University of California, Irvine (2013).*ProQuest Dissertations and Theses*. [pdf]- T. Rubin,
**A. Chambers**, P. Smyth, and M. Steyvers. Statistical topic models for multi-label document classification.*Machine Learning*, 2011. **A. Chambers**and P. Smyth. Learning concept graphs from text with stick-breaking priors.*Advances in Neural Information Processing Systems (NIPS)*, 2010.- C. Chemudugunta,
**A. Holloway**, P. Smyth, and M. Steyvers. Modeling documents by combining semantic concepts with unsupervised statistical learning.*Int'l. Semantic Web. Conf. (ISWC)*, LNCS 5318, Springer, 2008, pp.229-224. **A. Holloway**and T.-Y. Chen. Neural networks for predicting the behavior of preconditioned iterative solvers.*Proc. of the 26th Int'l. Conf. Compt'l. Science (ICCS)*, LNCS 4487, Springer, 2007, pp. 302-309