Dept of Engineering Physics
Email: Amy Magnus
Ph.D., Electrical Engineering, with M.E. Oxley, Air Force Institute of Technology, Inquisitive Pattern Recognition, 2003
M.S., Electrical Engineering, Air Force Institute of Technology, Three-dimensional localization of lesions from digital mammography, 1995
B.S., Electrical Engineering,Rochester Institute of Technology, 1990
Maj Magnus investigates the contribution of physics-based codes to machine intelligence. Her overarching goal is to build comprehensive summaries of data collections by coupling sensitivity/specificity studies with information retrieval techniques. In keeping, she designs self-supervised pattern recognition algorithms for use in collaborative computational environments that aid multi-physics analyses. Other interests include:
Robotic Sensing. Designs sensing capabilities that interrogate dynamic environments for actionable information. Actionable information is information of consequence requiring re-planning, retraining, or learning. Investigations explore querying competencies in sensor management systems, assimilation methodologies for actionable information, and representations of situations and transitional cues.
Distributed Intelligence. Investigates mathematics for expert discovery, access, and evaluation in sensor laden environments. Networks have changed the dynamics of work. They enable rapid, highly coordinated mobilizations; improve planning through the assimilation of new information; and accomplish these advantages on compressed time scales unimaginable a generation ago. To leverage these synergetic effects in sensor management systems, she investigates information retrieval algorithms appropriate for subsymbolic computations.
PATTERN RECOGNITION
REMOTE SENSING
MACHINE INTELLIGENCE
OPTICS
MATERIALS CHARACTERIZATION
SIGNAL PROCESSING TO PROBLEMS IN INTELLIGENCE COLLECTION AND EXPLOITATION
14. B. Ulicny, K. Baclawski, and A.L. Magnus, “New Metrics for Newsblog Mining,” in Proceedings of SPIE, Data Mining, Intrusion Detection, Information Assurance and Data Networks Security, 6570 (Orlando, FL), April 2007.
13. M.E. Oxley and A.L. Magnus, “Categories of Situations for Level 2 Fusion,” in Proceedings of the Ninth International Conference on Information Fusion (Florence, Italy), July 2006.
12. A.L. Magnus and M.E. Oxley, “Information Forensics and the art of inquiry,” in Proceedings of SPIE, Intelligent Computing: Theory and Applications IV, 6229 (Orlando, FL), April 2006.
11. A.L. Magnus and M.E. Oxley, “Expert classifiers and the ordered veracity experience response (OVER) curve,” in Proceedings of SPIE, Applications and Science of Neural Networks, Fuzzy Systems, & Evolutionary Computation VI, 5200 (San Diego, CA), August 2003.
10. A.L. Magnus and M.E. Oxley, “Artificial symbols and the essence of intelligent computing,” in Proceedings of SPIE, Intelligent Computing: Theory and Applications, 5103 (Orlando, FL), April 2003.
9. M.E. Oxley and A.L. Magnus, “Measuring the Generalization Capabilities of Arbitrary Classifiers,” in Proceedings of SPIE, Intelligent Computing: Theory and Applications, 5103 (Orlando, FL), April 2003.
8. A.L. Magnus and M.E. Oxley, “Arrogance in Classification,” in Proceedings of the 2003 IEEE Aerospace Conference, 5, pp. 2037–2048 (Big Sky, MT), March 2003.
7. A.L. Magnus and M.E. Oxley, “Fusing and filtering arrogant classifiers,” in Proceedings of the Fifth International Conference on Information Fusion (Annapolis, MD), July 2002.
6. A.L. Magnus and M.E. Oxley, “The Generalization Capability of a Multilayer Perceptron,” presented at the SIAM 50th Anniversary and Annual Meeting (Philadelphia, PA), July 2002.
5. A.L. Magnus and M.E. Oxley, “Generalization tools for multilayer perceptrons,” in Proceedings of the 2002 International Joint Conference on Neural Networks (Honolulu, HI), May 2002.
4. A.L. Magnus and M E. Oxley, “Quantifying the expertise of classifiers using 4-value logic,” in Proceedings of SPIE, Applications and Science of Computational Intelligence V, 4739, pp. 129–140 (Orlando, FL), April 2002.
3. M.E. Oxley and A.L. Magnus, “Confusion-based fusion of classifiers,” in Proceedings of SPIE, Applications and Science of Computational Intelligence V, 4739, pp. 120–128 (Orlando, FL), April 2002.
2. A.L. Magnus and M.E. Oxley, “The Theory of Confusion,” in Proceedings of SPIE, Applications and Science of Neural Networks, Fuzzy Systems, & Evolutionary Computation IV, 4479, pp. 105-116 (San Diego, CA), August 2001.
1. A.L. Magnus and S.C. Gustafson, “Inquisitive Pattern Recognition,” in Proceedings of the Third International Conference on Information Fusion, 1 (Paris, France), July 2000.