About IET

The objective of the IET Fellows Program is to enhance IET’s technology development and commercial market success through close and mutually beneficial association with members of the academic and educational communities. Fellows are provided corresponding opportunities to influence the direction of IET products and services, to access and contribute to IET technology, personnel and capabilities, and to participate in helping IET technology reach fruition in real world, practical applications of critical value to our customers and society. The IET Fellow Program is designed specifically and solely for academic and education professionals.

The following individuals are currently part of IET's Fellow Program:

KC Chang
Professor Kuo-Chu Chang is a recognized expert in multi-sensor tracking and fusion and in Bayesian network technologies. In last twenty years, Dr. Chang has conducted research on wide range of multi-sensor tracking and fusion and Bayesian network technologies. Recent projects include developing efficient inference algorithm for mixed Bayesnet and working with Nova research to develop predictive Kalman filters for situation awareness. He also worked in a cooperative sensor management project where he developed the MHT algorithm, which integrated dynamic and ID tracking using Bayesnet.

Professor Chang received his M.S. and Ph.D. degrees in Electrical Engineering from the University of Connecticut in 1983 and 1986 respectively. From 1983 to 1992, he was a senior research scientist in Advanced Decision Systems (ADS) division, Booz-Allen & Hamilton. In 1992, he joined the Systems Engineering department, George Mason University as an associate professor. He has published more than one hundred papers in the areas of multitarget tracking, distributed sensor fusion, and in the area of Bayesian Networks technologies. He was the Editor on Tracking/Navigation Systems of IEEE Transactions on Aerospace and Electronic Systems from 1993 to 1996. He is currently the Editor on Large Scale Systems of the same transaction and the associate editor of IEEE Transactions on Systems, Man, and Cybernetics.

Kathryn Blackmond Laskey
Dr. Laskey is an Associate Professor of Systems Engineering and Operations Research at George Mason University, where she teaches and performs research on the use of decision theoretic methods in artificial intelligence. Prior to joining George Mason University, Professor Laskey was Principal Scientist at Decision Science Consortium, Inc.

For many years, Professor Laskey has been a leader in the application of Bayesian methods to automated support for multi-source fusion and situational awareness. She has developed methods for knowledge-based construction of problem-specific Bayesian belief networks, specifying Bayesian belief networks from a combination of expert knowledge and observations, and flexible adaptation of models to anomalous situations. Professor Laskey has worked with domain experts to develop Bayesian belief network models for a variety of decision and inference support problem areas.

She has published extensively on Bayesian decision theoretic approaches to multi-source fusion and situation awareness. Dr. Laskey was a key member of IET’s knowledge engineering team and developed a method for evaluating probabilistic situation assessments against ground truth for programs. For another project, she developed a decision theoretic information architecture for missile defense.

Professor Laskey has been an associate editor of IEEE Transactions on Systems, Man, and Cybernetics. She is a member of the Board on Mathematical Sciences and their Applications of the National Academy of Sciences, and has served on several National Academy of Sciences panels. She was co-program chair in 1999 and general chair in 2000 for the Conference on Uncertainty in Artificial Intelligence. Professor Laskey earned a Ph.D. in Statistics from Carnegie Mellon University, an M.S. in Mathematics from the University of Michigan, and a B.S. in Mathematics from the University of Pittsburgh.