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Discovery: Adaptive Learning Systems, Evolution, Data Mining

We are interested in a variety of forms of machine learning including, to mention a few topics, supervised and unsupervised algorithms, learning from humans (knowledge acquisition) as well as from data and environments (classifiers and pattern recognizers). Our interests cut across the basic research needed to develop better learning algorithms all the way to improving usage and outcomes from applying available toolsets to significant real world problems. A few example research project descriptions include:

Faculty
 
  • Nabil H. Farhat, PhD, New Types of Neural Network Algorithms

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  • John Holmes, PhD, Medical informatics in epidemiology, knowledge discovery and datamining

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  • Steven Kimbrough, PhD, Decision support systems; electronic commerce; artificial intelligence and computational rationality; logic modeling; evolutionary computation (including genetic algorithms and genetic programming)

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  • Tony Smith, PhD, Probabilistic models in spatial interaction behavior

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  • Santosh S. Venkatesh, PhD, Neural Networks, Computational Learning Theory and Information Theory, Pattern Recognition

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  • Iraj Zandi, PhD, Neural Network Studies