R&D

IET Consulting Collaborators

IET maintains consulting relationships with pioneers and the rising stars in the fields of scientific inquiry that directly contribute to the successful development of our technology. IET continually seeks strategic relationships of mutual benefit to our partners, customers and stockholders.


Thomas O. Binford, Ph.D.
Dr. Binford was a research professor at Stanford University for 30 years before becoming professor emeritus in January 2000. His research contributions span many disciplines, including computer vision, robotics, and artificial intelligence. In particular, Dr. Binford, working with IET CEO Dr. Tod Levitt, has developed the theory of quasi-invariants, or features that are slowly varying with respect to change of viewpoint. Quasi-invariants have predictable probabilistic behavior, which allows IET to apply Bayesian inference to utilize available evidence effectively, and to make robust and accurate matching decisions with tunable thresholds in the areas of computer vision, curve matching, automatic target recognition and situation-robust fingerprint matching.

Michael Bienvenu, Ph.D.
Professor Bienvenu is an Associate Research Professor with the System Engineering/Operations Research Department and Visiting Research Scientist with the C3I Center, School of Information Technology and Engineering at George Mason University. Dr. Bienvenu has supported IET on efforts for the Missile Defense Agency involving functionality that provides automated support for the process of setting technical objectives and goals and evaluating alternative system concepts against projected adversary capabilities to determine how well they meet those objectives and goals.

KuoChu Chang, Ph.D.
Professor Kuo-Chu Chang is an Associate Research Professor at GMU and a recognized expert in multi-sensor tracking, information fusion and Bayesian network reasoning technologies. Dr. Chang is currently supporting IET in developing a new approach to multiple-object tracking and discrimination: Hybrid Stochastic Clustering (HSC) for ballistic missile defense (BMD) exo- and endo-atmospheric object tracking and discrimination in high object density, time-varying sensor resolution, and object spawning environments. The set of applicable algorithms includes multiple hypothesis tracking, Janossy density function (JDF), and Poisson point process approximation (P3A) algorithms, all based on the theory of random finite sets.

Paul Cohen, Ph.D.
Professor Cohen is one of the nation's foremost researchers in intelligent systems and behaviors. He has collaborated with IET on multiple efforts, including the evaluation of knowledge-based systems, semi-automated elicitation of expert knowledge and inference and control for tactical saturation understanding systems.

David F. Davis, Ph.D.
Professor Davis is Director of George Mason University's Program on Peacekeeping Policy, which has been working for over a decade on modeling peace operations. Professor Davis' Conceptual Model of Peace Operations (CMPO) explains the overall processes that accompany the intervention of a third party for the purposes of maintaining/restoring justice and order in a country or a theatre of operations. IET has worked with Professor Davis to combine CMPO with our computational inference and modeling technology to develop techniques for representing organizational behavior in Operations Other Than War.

Ward Edwards, Ph.D.
Until his retirement in 1995, Dr. Edwards was Director of the Social Science Research Institute and Professor of Psychology and of Industrial and Systems Engineering at the University of Southern California. Edwards' research interests have been in the fields of behavioral decision theory and decision analysis since 1948. Dr. Edwards has collaborated for many years with IET on diverse applications including wide-area radar surveillance, medical image understanding and automatic target recognition. His contributions have often involved rendering theory to practice, especially in the realms of hierarchical Bayesian inference and elicitation of probability distributions from subjective expert judgment.

Kathryn Blackmond Laskey, Ph.D.
Professor Laskey, a professor at George Mason University, is a recognized expert in representation, instantiation, and maintenance of complex knowledge schema. She has published extensively in the area of integrating symbolic and probabilistic knowledge. Dr. Laskey has been working with IET scientists to develop and implement Multi-Entity Bayesian Networks (MEBNs) for a variety of challenging applications, to include indications and warning of asymmetric attack, modeling complex cognitive human behavior and dynamic intelligent generation of scenarios for operational training.

David A. Schum, Ph.D.
Professor Schum is a world-class expert on inference and hierarchical Bayesian reasoning. He has collaborated with IET since 1994 to provide breakthrough models of tactical human intelligence, pedigree of evidential information and the semi-automated elicitation of subjective probability distributions.

Prakash P. Shenoy, Ph.D.
The Ronald G. Harper Distinguished Professor of Artificial Intelligence, Professor Shenoy is currently a member of the staff of the School of Business at the University of Kansas. His areas of expertise include belief networks, valuation-based systems and expert systems. Professor Shenoy works closely with IET in missile defense applications focusing on the challenge of discrimination of armed re-entry vehicles in the midst of decoys and other means of obscuration and deception.