Part 2: PATENTS, BOOKS , and SELECTED JOURNAL ARTICLES

Patent for "Sensor Fusion Apparatus and Method," (No. 5,850,625, GRANTED 12/15/98). Patent selected as New York Times Patent of the Week (January, 1999),

Maren, A.J., Harston, C., & Pap, R. (1990) Handbook of Neural Computing Applications (Academic Press).

Maren, A.J. (in preparation). Handbook of Personalization Technology (Edited).

Maren, A.J., Pap, R.M., Priddy, K.L., & Akita, R.M. (1995). “Neural network applications speed the Navy’s warfighting ability,” Naval Research Review.

Bulsara, A., Maren, A.J., & Schmera, G. (1993). “Single effective neuron: dendritic coupling effects and stochastic resonance,” Bio. Cybernetics, 70, 145-156.

Minsky, V., & Maren, A.J. (1990). “A multilayered, cooperative-competitive neural network for segmented scene analysis,” J. Neural Network Computing, 1, Issue 3 (Winter), 14-33.

Maren, A.J., Lin, S.H., Langley, R.H., & Eyring, L. (1984). “A theoretical model for hysteresis in solid state phase transitions,” J. Solid State Chem., 53, 329-343.

Part 3: SPECIAL AWARDS

1998: Inventor of the Year, IEEE, Chattanooga Chapter

Part 4: WORKSHOPS, SELECTED SPEAKING ENGAGEMENTS

Feb., 2001: City College of New York, Biomedical Engineering Center, invited by Dr. D.B. Karron

Oct., 2000: City College of New York, Biomedical Engineering Center, invited by Dr. D.B. Karron

1991: Conceived concept, organized, and co-chaired workshop on Enhanced Human-Computer Interfaces: A Multidisciplinary Approach, funded by four National Science Foundation Directorates (Engineering, Computer and Information Science, Education and Human Resources, and Adv. Technologies (now closed)). Workshop convened members from neural networks, symbolic logic, intelligent tutoring, and adaptive user interface research communities. Workshop was predecessor to later NSF workshop on innovative methods for education and a later Learning initiative.

Part 5: PROFESSIONAL SOCIETIES AND MEMBERSHIPS

1993 – 1996: Member, Board of Governors, International Neural Network Society.

Part 6: PROJECTS LED AS PRINCIPAL INVESTIGATOR

·Precision Logistics Support System, Phase I and II Small Business Innovative Research (SBIR) projects, 1996 - 1998, funded by Naval Facilities Engineering Support Center, Pt. Hueneme, on behalf of the U.S. Marine Corps. (Phase I $100,000, Phase II $750,000; contract number N47408-97-C-0208, to Accurate Automation Corporation (AAC).)

·Novel methods for precision logistics forecasting for force sustainability. Developed neural network approach using both current operational context and recent history.

Used Unified Modeling Language (UML) and ‘use cases’ in Object-Oriented Analysis / Design (OOA/D). Initiated project with detailed requirements analysis and detailed technical architecture developing client-server (web-based interaction) system. (System designed to enable forward-deployed units to request commodities, and enable rear logistics support units to adaptively predict and anticipate resupply needs.

·Neural Network Method for Figure of Merit, ONR Phase I ($100,000) and II ($750,000) SBIR contracts N00014-93-C-0265, N00014-95-C-0092; 1993 – 1998.

·Innovative approach to measuring quality of multisource fused information, applied to aircraft target recognition.

·Invented novel methods to identify aircraft uniquely, combining information from short-term behavior (target track), symbolic information (responses from aircraft Identification-Friend-or-Foe transponders), and long-term context (target trajectory over time); generated identification figures-of-merit.

·Developed novel neural network to adaptively estimate Dempster-Shafer belief intervals applied to target identification.

·Signal Processing Chips / Electronics, ONR Phase I ($75,000, 1993) and II ($500,000, 1995, N00014-95-C-0323) Small Business Technology Transfer Research (STTR) contracts, 1993 – 1998.

·Invented and demonstrated new method for combining sensor information from two or more sources, yielding fused target tracks / reports based on computational model of biological sensor fusion in superior colliculus.

·Received Patent for Sensor Fusion Method and Apparatus, Patent number 5,850,625, Dec. 15, 1998. Patent selected as New York Times Patent of the Week (January, 1999), and led to award as IEEE Inventor of the Year (Chattanooga Chapter).

·Robust Methods for System Identification, NSF Phase I ($50,000, 1996) and II ($350,000, 1997).

·Developed method to use neural network “black-box” to learn and estimate differences between analytic “white-box” model and true system observations, resulting in two-part “gray-box” system identification model.

·Applied method to aircraft flight control model.

·Biologically-Based Multisensor Fusion (ONR Phase I SBIR, $100,000; 1997, N00014-97-C-0425).

·Logistics Forecasting Phase I U.S. Navy NAVICP ($100,000, 1996).

·Used Linear Vector Quantization (LVQ) neural network (iterative form of K-means clustering) to create groups of commodities exhibiting similar behaviors for Naval logistics modeling

·User Modeling Phase I NSF SBIR, ($50,000, 1997).

·Developed pilot models emphasizing perceptual and cognitive individual differences among pilots. Evaluated eight mishap reports from NTSB, including flight data recorder information. Developed and presented innovative approach to pilot modeling using adaptive critic neural networks

Part 7: ADDITIONAL PROJECTS AS SENIOR RESEARCHER

·Innovative mathematical and computer algorithms for modeling target behavior with high noise and variability (aircraft target tracking):Invented, developed and demonstrated innovative approach to automatic tracking of fast maneuvering air targets, showing performance improvements over traditional predictive technology (a-b filters, Kalman filters); particularly when target trajectory vectors evasively and suddenly from its expected path. Method is able to distinguish between simple increase in variance vs. changes from expected behavior; adjusts weighting of prediction vs. observation accordingly. (Obtained funding from ONR Phase I and II SBIR contracts, 1988 – 1998.)

·Clustering / Classification / Segmentation: Developed novel applications using both Bayesian and back-propagating neural networks to perform classification of radar feature data for Nuclear Missile Defense (NMD) application.

·Telerobotics: Developed equations of range and motion for modeling NASA space shuttle robotic arm, funded under NASA Phase II SBIR.

Part 8: WORK EXPERIENCE

January 2001 – Current: Chief Operations Officer, Computer Aided Surgery, Inc.

March 2000 – Current: Principal – EagleForce Associates

July 1999 – March 2000: Technology Manager, XonTech, Inc., Huntsville, AL.

1993 – 1999: Senior Scientist, Accurate Automation Corp, Chattanooga, TN. (working with San Diego office, led by Mr. Richard Akita, Vice President Adv. Technology, San Diego (858) 623-9812.)

1992 - 1993: Vis. Assoc. Prof., Computer Sci. Dept., Radford U., Radford, VA

1987 - 1992: Vis. Assoc. Professor, Dept. Computer Sci., Univ. Tenn. Space Inst.,Dept. Nuc. Eng., Univ.. Tenn. / Knoxville

1986: Scientist , PRL (independent research lab), Princeton, NJ

1984 - 1986: Research Scientist, Honeywell Systems and Research Cntr, Image & Sig. Proc. Group, Minneapolis, MN

1979 - 1984: Instructor / Visiting Ass’t Prof., Dept. Computer Science / Mathematics, Univ. Wisc. / River Falls

Part 9:CONSULTING

Patent Analysis: Assessed patent strengths and ways in which crucial first patent could be superceded for an innovative database company, under confidentiality understanding, Oct. 2000.
Business Development: Created new opportunities for Federally-funded subcontract work for Global Computing Associates, a woman-owned Small Disadvantaged Business (2000 – 2001).

Part 10:DISSERTATIONS/ THESES MENTORED; CLASSES TAUGHT

Extracting sequences of symbolically-encoded temporal sequences from large database: Mentored Ph.D. dissertation using novel Selection Recognition Automata (SRA) neural network to extract common sequences within symbolically-encoded database. Method applied to nuclear plant event report databases. Dissertation: A. Gacem, U. Tenn./Knoxville, 1990.
Novel approach to representing image structure: Master’s Thesis: V. Minsky, U. Tenn. Space Institute, 1988,
Graduate level classes in pattern recognition and digital signal processing with neural network applications. Established new syllabus wherein the class conducted comparative studies of different neural networks as an instructor-led project. Univ. Tenn. Space Institute. 1987 – 1989.