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Intelligent and Complex Systems
The techniques developed by researchers in Algorithm Complexity, Expert Systems, Neural Networks, Evolutionary Computation, Dynamical Systems, Embedded and Hybrid Systems, Natural Language Processing, Knowledge Engineering, Automated Reasoning, Data Warehousing and Data Mining are a major driving force in the development of highly sophisticated, intelligent information technology and management approaches. As the quantity and diversity of interacting information systems continues to develop, and as the intricacy of the interactions of these systems with the environment increases, advanced structural and quantitative multi-scale descriptions are required to understand, develop, control and maintain these systems. Such systems must contend with emergent phenomena, and shifts between meta-stable configurations and behavior patterns.
The synthesis of such systems and their ability to learn can exploit self-organization principles and techniques. The existing expertise in the scientific community that is positioned to develop detailed understanding of complex information processing systems lies largely in the communities of researchers in the above listed areas. These areas naturally cut across traditional disciplines within computer science, mathematics, physics, and biology. The area of knowledge discovery, with applications in mining knowledge from biological data, readily interfaces with Bioinformatics and Internet applications, which are recognized as important components of modern Computer Science programs.
Since the advent of the Internet and the information age, the focus in computing paradigms has shifted from stand-alone systems to millions of loosely connected “distributed systems.” Interest in studying interactions among such systems and software components has been growing rapidly. Interconnected devices such as desktop PCs, supercomputers, hand-held computers, mobile phones, and digital cameras will be an integral part of human life. The President's Information Technology Advisory Committee's (PITAC) 1999 Report to the President emphasize the importance of an “anytime, anywhere” Internet infrastructure (Page 37 of PITAC Report). Furthermore, an AAAI's report, The Role of Intelligent Systems in the National Information Infrastructure , emphasizes the pivotal role of Artificial Intelligence (AI) in meeting major challenges of the National Information Infrastructure. Adequate research on algorithm complexity, multiagent systems, neural networks, evolutionary algorithms, dynamical systems, game theory, data mining, etc. will be strongly needed to achieve the fore mentioned goals in Internet domains.
Faculty with Expertise in this Area
- Howard Blair (EECS): Logic in Computer Science, Knowledge Representation, Complex Systems.
- Amrit Goel (EECS): Data Mining Applications, Radial Basis Function Models.
- Can Isik (EECS): Applications of Neural Nets and Fuzzy Logic, Control Theory, Computational Intelligence, Robotics.
- Kishan Mehrotra (EECS): Neural Networks, Genetic Algorithms, Reliability Theory, Time Series Analysis, Analysis of Algorithms.
- Chilukuri Mohan (EECS): Artificial Intelligence, Neural Networks, Evolutionary Algorithms, Optimization, Pattern Recognition, Uncertainty.
- Jae Oh (EECS): Cooperation in Multi-Agent Systems, Internet Information Retrieval, Resource Sharing and Allocations in the Internet and Distributed Real-Time Computer Systems, Machine Learning, Distributed Artificial Intelligence, Theoretical Aspects of Machine Learning Algorithms, Evolutionary Game Theory, Evolutionary Algorithms.
- Pramod Varshney (EECS): Signal and Image Processing, Multisensor Data/Information Fusion, High Performance Computing and Communications, Detection Theory.
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