Paul McNamee Senior Research Scientist Johns Hopkins University Applied Physics Laboratory Laurel, Maryland 20723-6099 Phone: (443) 778-3816 Fax: (443) 778-6904 Email: paulmac@apl.jhu.edu

Abbreviated Biography

I am a computer scientist with Johns Hopkins University and I have joint appointments with the Applied Physics Laboratory (APL) and the Human Language Technology Center of Excellence (HLTCOE). My recent work has been in multilingual text retrieval and information extraction (mining information from unstructured text). Some projects I have worked on are:

• HAIRCUT: The Hopkins Automated Information Retriever for Combing Unstructured Text. HAIRCUT is a prototype Information Retrieval system that uses a variety of indexing terms (e.g., words, stems, or character n-grams). I have participated in numerous international evaluations including: the Text REtrieval Conference (TREC), CLEF, NTCIR, and FIRE.
• An Army Research Office (ARO) project investigating an optimization technique called automated memoization. This software engineering tool enables programmers to improve the run-time performance of their code by storing results of repetitive calculations without changing a single line of code. Downloads are available for C++ and Common Lisp.
• The DARPA-sponsored Integrated Submarine Stealth Information Processing System (ISSIPS). This project modelled complex tactical and oceanographic environments and provided real-time advisement to a submarine crew on how to better plan and manage submarine operations.

I am a member of the Association for Computing Machinery (ACM) and the Association for Computational Linguistics (ACL).

Selected Publications

• Paul McNamee, Textual Representations for Corpus-Based Bilingual Retrieval, PhD Thesis, University of Maryland Baltimore County, December 2008.
• Paul McNamee, Rion Snow, Patrick Schone, and James Mayfield, Learning Named-Entity Hyponyms for Question Answering. Proceedings of the 3rd International Joint Conference on Natural Language Processing (IJCNLP-2008), Hyderabad, India, pp. 799-804, January 2008.
• Paul McNamee and James Mayfield, Translating Multiword Expressions Using Parallel Suffix Arrays. Proceedings of the 7th Biannual Conference of the Association of Machine Translation in the Americas (AMTA-06). Boston, MA, pp. 100-109, 2006.
• James Mayfield and Paul McNamee, The HAIRCUT Information Retrieval System. APL Technical Digest, 26(1):2-14, 2005.
• James Mayfield, Paul McNamee, Christine Piatko, and Claudia Pearce, Lattice-based Tagging Using Support Vector Machines. Proceedings of the Twelfth International ACM Conference on Information and Knowledge Management (CIKM 2003), pp. 303-308, November 2003.
• Paul McNamee and James Mayfield, Comparing Cross-Language Query Expansion Techniques by Degrading Translation Resources. In the Proceedings of the 25th Annual International Conference on Research and Development in Information Retrieval (SIGIR-2002), Tampere, Finland, pp. 159-166, 2002.
• Paul McNamee and Marty Hall, Developing a Tool for Memoizing Functions in C++, ACM SIGPLAN Notices' C++ Toolbox, pgs 17-22, August 1998.

A much longer list of my scholarly publications is available here.

Teaching

I have taught a variety of courses as an adjunct faculty member in the Computer Science Department of the Johns Hopkins University's Engineering and Applied Science Program for Professionals, The program offers students the opportunity to earn a masters degree by taking courses at several centers in the Baltimore and Washington D.C. areas.

• 605.744: Information Retrieval covers the automatic indexing and retrieval of unstructured textual information.
• 605.435: Distributed Development on the World Wide Web covers web topics such as HTML, Applets, CGI scripts and servlets, and JavaScript while teaching intermediate-level Java programming.
• 605.201: Introduction to Programming Using Java is a course designed for students without the programming experience needed to continue studies in the JHU part-time program. Topics include the basic syntax, control methods and data structures of the language. This course is a replacement for 605.201: Introduction to C++ Programming
• 605.451: Principles of Artificial Intelligence introduces various methods, languages, and techniques in Artifical Intelligence including search, natural language, logic, Lisp, Prolog, and neural networks.
• 605.452: Artificial Intelligence Programming expands on 605.451 by covering examples of AI programs in greater detail and having students work on projects in search, natural language, simulation, game-playing, all using the Common Lisp language.

Personal

I enjoy paddling, playing board games, and walking with Betsy.