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Kevin Denis Reilly
University of Alabama at Birmingham
Birmingham, Alabama
 

Kevin D. Reilly was appointed professor of computer and information science at the University of Alabama at Birmingham (UAB) in 1984. He began his UAB career in 1970. Earlier work was in the research track at UCLA and as a lecturer at UCLA and at the University of Southern California (in business statistics). Other positions at UAB have included a secondary appointment as professor of psychology. Transit to emeritus professor status is expected soon, with continued, reduced activity across the spectrum of teaching, research, and service.

A philosophical view that logic and statistics (probability) are the two great epistemologies of science has significantly influenced Kevin’s approach to scientific work. A programming and software systems interest provides a means to achieve computational goals in researching topics such as modeling and simulation of human and animal behavior; software for combined discrete and continuous simulation; logic programming as a nonnumeric simulation modeling mode to accompany numerical modes; a logic-based approach to theory and development of simulation languages and environments; neural network modeling in psychology; fuzzy systems, including fuzzy neural networks, genetic algorithm solution methods, and Web system modeling applications; expert systems on handheld devices; and clustering and sequence analysis methods in proteins and other biological systems. He has published almost 200 research papers on these topics. In parallel, he made over 125 professional presentations. Over 125 review articles are listed in his resume, with his most recent ones being in Computing Reviews.

Kevin is listed in several Who’s Who documents, including, for example, Who’s Who in America and American Men and Women of Science. He is a senior member of the Modeling and Simulation Society, a Monbusho fellow (Japan), and a member of the European Academy of Sciences. He is a Phi Kappa Phi Honor Society designee, a Woodrow Wilson Fellow, and a US Public Health Service Postdoctoral Fellow. He spent a sabbatical period at the National Institute of Mental Health. He obtained his PhD from the University of Chicago, after an MS from the University of Nebraska and a BS, summa cum laude, from Creighton University.

His spare time revolves around travel and arguing topics wiser people know to avoid: religion and politics! He’s an animal lover, at least to the extent that he’s never experienced a period without a dog or cat, or both.


     

On Peirce’s pragmatic notion of semiosis--a contribution for the design of meaning machines
Queiroz J., Merrell F. Minds and Machines 19(1): 129-143, 2009.  Type: Article

Work on semiotics--the theory of signs and meaning--and Charles Peirce’s contributions to it are referenced in some artificial intelligence (AI) texts, though not frequently covered in depth. A notable excep...

 

Exploration of configural representation in landmark learning using working memory toolkit
Wang X., Tugcu M., Hunter J., Wilkes D. Pattern Recognition Letters 30(1): 66-79, 2009.  Type: Article

Exploration consists of computer runs--experimenting on a reinforcement learning-based model that employs a working memory (WM), embodied in a toolkit (WMtk)--aimed at learning landmarks to be used, for example, in na...

 

Genetic algorithm based multi-agent system applied to test generation
Meng A., Ye L., Roy D., Padilla P. Computers & Education 49(4): 1205-1223, 2007.  Type: Article

The major thrusts of this paper are identified in its title: genetic algorithm (GA), multiagent system (MAS), and test generation (TG) (a GAMASTG system)....

 

An architectural model of conscious and unconscious brain functions: global workspace theory and IDA
Baars B., Franklin S. Neural Networks 20(9): 955-961, 2007.  Type: Article

Baars has registered at least 25 years of active pursuit of “conscious contents” as “coherent, global information” in brain-like behavior. Global workspace theory (GWT) postulates that the role o...

 

Evolving dynamic Bayesian networks with multi-objective genetic algorithms
Ross B., Zuviria E. Applied Intelligence 26(1): 13-23, 2007.  Type: Article

Genetic algorithms are employed to generate sparsely connected dynamic Bayesian networks (DBNs), which are defined precisely in this paper. Sparseness joins accuracy in the multiple objectives procedure (MOP), accompanied by additional...

 
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