Jonathan Elsas

Third year PhD student at the Language Technologies Institute, School of Computer Science at Carnegie Mellon University.

CV (pdf)
Blog

Research:

Advisor: Jaime Carbonell
My research areas are:

• Information retrieval in complex, structured, social environments. This includes search over large blog corpora, email, or online message boards and forums.
• Machine learning for document ranking, particularly fast, scalable algorithms.

Publications:

• (to appear) Vitor R. Carvalho, Jonathan L. Elsas, William W. Cohen, Jaime G. Carbonell. "Suppressing Outliers in Pairwise Preference Ranking," Proceedings of the 17th annual ACM Conference on Information and Knowledge Management (CIKM 2008), 2008.

• Vitor R. Carvalho, Jonathan L. Elsas, William W. Cohen, Jaime G. Carbonell. "A Meta-Learning Approach for Robust Rank Learning," Proceedings of the SIGIR 2008 Workshop on Learning to Rank for Information Retrieval (LR4IR 2008), 2008. [pdf]

• Jonathan L. Elsas, Jaime Arguello, Jamie Callan, Jaime G. Carbonell. "Retrieval and Feedback Models for Blog Feed Search," Proceedings of the 31st Annual International ACM SIGIR Conference on Research & Development on Information Retrieval (SIGIR 2008), 2008. [pdf] [slides]

• Jaime Arguello, Jonathan L. Elsas, Jamie Callan, Jaime G. Carbonell. "Document Representation and Query Expansion Models for Blog Recommendation," Proceedings of the Second International Conference on Weblogs and Social Media (ICWSM 2008), 2008. [pdf]
  Best Paper award nominee

• Jonathan L. Elsas, Vitor R. Carvalho, Jaime G. Carbonell. "Fast Learning of Document Ranking Functions with the Committee Perceptron," Proceedings of the First ACM International Conference on Web Search and Data Mining (WSDM 2008), 2008. [implementation notes] [pdf] [slides .mov medium] [slides .mov large]

• Jonathan L. Elsas, Jaime Arguello, Jamie Callan, Jaime G. Carbonell. "Retrieval and Feedback Models for Blog Distillation," Proceedings of the 2007 Text REtrieval Conference (TREC 2007) [notebook version] [slides]
  Best performing group in the TREC 2007 Blog Distillation task

• Gary Marchionini, Stephanie W. Haas, Junliang Zhang, Jonathan L. Elsas. "Accessing Government Statistical Information," Computer, vol. 38, no. 12, pp. 52-61, December, 2005. http://doi.ieeecomputersociety.org/10.1109/MC.2005.393

• Jonathan L. Elsas. "An Evaluation of Projection Techniques for Document Clustering: Latent Semantic Analysis and Independent Component Analysis," Master's Thesis, UNC-CH School of Information and Library Science, July, 2005. http://hdl.handle.net/1901/208

• Efron, M., Elsas, J., Marchionini, G., and Zhang, J. 2004. Machine learning for information architecture in a large governmental website. In Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital Libraries (Tuscon, AZ, USA, June 07 - 11, 2004). JCDL '04. ACM Press, New York, NY, 151-159. http://doi.acm.org/10.1145/996350.996386

• Junliang Zhang, Tim Shearer, Gary Marchionini, Miles Efron, John [Jonathan] Elsas. Relation Browser++: an information exploration and searching tool., DG.O 2004. [pdf]

Other:

• Teaching Assistant: 15-493: Information Retrieval and Web Mining, fall 2007.
• Program Committee: SSM 2008
• Reviewer: WWW-08
• Co-organizer, Information Retrieval Discussion Series at CMU, 2007-present
• Contributor to the Analysis of Social Media Wiki.

About me:

I completed my Master's degree in Information Science from the School of Information and Library Science at the University of North Carolina in Chapel Hill. My master's thesis was on the evaluation of two different projection or dimensionality reduction techniques for use in document clustering: Latent Semantic Analysis and Independent Component Analysis. You can see a bit more about what I did while at UNC on my old home page.

Prior to my time at UNC, I worked as a software developer at a large telecommunications company and a large "global services" company on a project enabling VoiceXML services over the telecom network. I was laid-off in 2002 (along with most of the company), found myself back in school in 2003, and realized I really liked it.