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• Expert and his colleagues at TEMI have assessed specific emerging technologies for clients, including the present state-of-the-art, future prospects and anticipated technical capabilities, plus identification of firms having a position in the technologies and their level of capability, market potential, and economic evaluation.
• He has experience training in-house staff in skills such as technology forecasting and assessment, competitive intelligence analysis, or engineering management.
• TEMI's clients have included Fortune 100 firms and small businesses as well as the U.S. government and the academic community. Expert has worked with a wide variety of technologies and for clients in industries as diverse as mineral resources, computers, telecommunications, electronics, and meteorology (including aviation weather).
• In 1992, Expert and his firm won a Small Business Innovation Research (SBIR) contract from the Naval Surface Warfare Center. Recent theoretical advances had been made in identifying the spectral components of the echo return of wide band sonar signals reflecting from submarine-like targets. The Navy solicited proposals for experimental studies to explore methods for evaluating the vibrational patterns of submarines, which could then be used for early detection and classification of submarines.The Navy originally believed that the most promising techniques for addressing this problem would be found in the developing area of neural networks applied to pattern classification. Expert's experience in operations research led him and his team to conclude that an optimization-based approach to pattern classification, with which they were well familiar, represented a potentially promising alternative approach. They proposed and were awarded the contract for a program of basic research and exploratory development into an improved signal interpretation technique for classifying acoustic signals based on an approach known as linear programming discriminant analysis (LP-Discrim). Utimately, they were able to demonstrate that the LP approach to discriminant analysis and pattern classification is generally superior to conventional, statistically-based classifiers, and that it yields success rates comparable to those of neural network approaches while possessing some superior operating characteristics.