Media and Public Relations

Where Innovation Is Tradition

Media Sources Guide

CATEGORY: Information Technology and EngineeringClear

SUB-CATEGORY: Artificial IntelligenceClear

Kenneth De Jong

Professor of Computer Science

Expertise: Genetic algorithms, Evolutionary computation, Machine learning, Artificial intelligence, Complex adaptive systems

De Jong came to Mason in 1984. He is head of the Evolutionary Computation Laboratory and associate director of the Krasnow Institute. His research interests include genetic algorithms, evolutionary computation, machine learning, and adaptive systems. He also is interested in experience-based learning in which systems must improve their performance while actually performing the desired tasks in environments not directly in their control or the control of a benevolent teacher. Support for these projects is provided by the Defense Advanced Research Projects Agency, the Office of Naval Research, and the Naval Research Laboratory. A member of the evolutionary computation research community, De Jong has been involved in organizing many of the workshops and conferences in this area. He is the founding editor in chief of the journal Evolutionary Computation and a member of the board of the Association for Computing Machinery Special Interest Group for Genetic and Evolutionary Computation.

Donald Gantz

Professor and Chair, Department of Applied Information Technology

Expertise: Information technology, Forensics, Computer simulation, Handwriting analysis

Gantz has taught courses  on basic statistics, probability, stochastic systems, computer simulation, case studies in applied statisticsat the undergraduate and graduate levels. His research interests are mathematical economics, applied statistics, flight test analysis, computer performance engineering and capacity planning, computer simulation and management decision systems.

He is an active researcher and practitioner in the application of geographic information systems, modeling systems and decision support systems to transportation demand management and traffic mitigation. Throughout his years as an applied statistician, he has been involved with survey design, analysis and reporting. He has considerable experience in the development of management decision systems and in litigation related analyses. He has done research, published papers and made presentations about the relationship between tuberculosis and demographic and socioeconomic factors in Northern Virginia.

Media Contact: Preston Williams, 703-993-9376, pwilli20@gmu.edu

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Gheorghe Tecuci

Professor of Computer Science; Director of the Learning Agents Center

Expertise: Artificial intelligence, Machine learning, Knowledge engineering, Knowledge acquisition and problem solving

Tecuci's research is focused on creating a theory for the development of knowledge-based agents by typical users who do not have knowledge engineering experience. The envisioned theory will allow these typical users to develop intelligent assistants that incorporate their problem solving expertise, and will thus contribute to a new revolution in the use of computers (where typical users will no longer be just users of programs developed by others, but agent developers themselves).

As part of this long-term research effort,  Tecuci has originated or contributed to several important concepts in intelligent agents, machine learning and knowledge acquisition, including: multistrategy learning, learning agent shell, plausible explanations, plausible version spaces, plausible justification trees, understanding-based knowledge extension, consistency-driven knowledge elicitation, integrated teaching and learning, and mixed-initiative reasoning. These contributions have led to the “Disciple” agent development approach where a subject matter expert teaches a Disciple learning agent to become a knowledge-based assistant, in a way that is similar to how the expert would teach a human apprentice, through specific problem solving examples and explanations, and by supervising and correcting agent’s problem solving behavior.

 

Media Contact: Preston Williams, 703-993-9376, pwilli20@gmu.edu

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Harry Wechsler

Professor of Computer Science

Expertise: Facial recognition, Machine learning/intelligence, Intelligent decision making systems

Wechsler received the PhD degree in computer science from the University of California, Irvine, in 1975. Currently, he is a Professor of computer science and Director for the Center of Distributed and Intelligent Computation at George Mason University.

His research in the field of intelligent systems focuses on computational vision, image and signal processing, data mining, machine learning and pattern recognition, with applications for ATR, biometrics/face recognition, intelligent HCI, performance evaluation, temporal data mining, and video processing and surveillance. He has published more than 200 scientific papers, serves on the editorial board for major scientific publications  and is the author of Computational Vision and Reliable Face Recognition Methods, which breaks new ground in biometrics and applied modern pattern recognition.

Wechsler also directed the development of FERET, which has become the standard facial data base for benchmark studies and experimentation.

Media Contact: Preston Williams, 703-993-9376, pwilli20@gmu.edu