Suzanne M. Mahoney Senior Principal Analyst

     Ms. Mahoney leads efforts that model complex phenomena characterized by uncertainty, incomplete data, dynamic environments and a variety of evidence sources of varying credibility.  Applications range from object level identification and situation assessment, to the dynamic allocation of resources and aggregation of model estimates. She is particularly experienced in leveraging a combination of methods, including the elicitation of knowledge from experts, learning from available data, and automatically constructing situation specific models from a knowledge base.

EXPERIENCE

2004 - Present, Innovative Decisions, Inc., Senior Principal Analyst
1996 - 2004, Information Extraction and Transport, Inc. (IET), Senior Analyst
1989 - 1995, George Mason University, Researcher
1968 - 1989, Control Data, Analyst



 EDUCATION

Ph.D, George Mason University, Information Technology, 1999
M.A., University of Michigan, Mathematics, 1967
B.S., University of Michigan, Mathematics, 1966



SELECTED PUBLICATIONS AND PRESENTATIONS

Daniels, D.C., L. D. Hudson, K.B. Laskey, S. M. Mahoney,  B.S. Ware, E. J. Wright (2008) Terrorism Risk Management in O. Pourret, P. Naim, B. Marcot (eds) Bayesian Networks A Practical Guide to Applications, John Wiley and Sons, Chichester, West Sussex, England.

Mahoney, S.M., Buede, D., Tatman, J. (2005) Patterns of Report Relevance, UAI Applications Workshop, Edinburgh, Scotland.

Mahoney, S.M. and E. Wright (2002) "Bayesian Network Engineering for Modeling Missile Defense Decisions", Proc. 2000 MSS 2002 Meeting of the MSS Specialty Group on Missile Defense Sensors, Environment sand Algorithms, Monterey, CA, February

Laskey, K.B. and S. M. Mahoney (2000) Network Engineering for Agile Belief Network Models. In IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 7. July/August.

Mahoney, S.M. and K.B. Laskey (1998) Constructing Situation Specific Networks. In Cooper, G. and Moral, S. (eds) Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference, San Francisco, CA: Morgan Kaufmann.

Laskey, K.B. and S. M. Mahoney (1997) Network Fragments: Representing Knowledge for Constructing Probabilistic Models. In Gieger, D. and Shenoy, P. (eds) Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference, San Francisco, CA: Morgan Kaufmann.