Wireless sensor networks, mobile ad hoc networks, wireless sensor and actor networks, wireless mesh networks and wireless security.

Dr. Akkaya's research mainly focuses on the problems at the network and application layer of various wireless ad hoc networks including wireless sensor networks, wireless sensor and actor networks, wireless mesh networks and underwater acoustic sensor networks. Particularly he has been working on connectivity, positioning, clustering, coverage, fault-tolerance, and QoS problems in wireless sensor and actor networks where powerful actors perform certain actions based on the received data. Recent research of Dr. Akkaya has focused redundancy elimination issues in wireless multimedia sensor networks while providing the necessary event coverage from multiple perspectives. Dr. Akkaya's other research interests include channel assignment and QoS routing in wireless mesh networks where multiple radios can be available to use. He studies dynamic channel assignment schemes which can be used to provide certain service guarantees based on the application needs. Dr. Akkaya has also been active in wireless security research. Particularly he is interested in security of future vehicular ad hoc networks. His recent focus has been on anonymous authentication and group security in vehicular ad hoc networks.

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Multi-agent systems, distributed problem solving, sensor interpretation, knowledge-intensive control of AI systems.

Dr. Carver's research is mainly in the area of distributed problem solving (DPS). This is a subfield of multi-agent systems (MAS) that studies how to solve large-scale problems using distributed systems of intelligent software agents. Key issues include: the effects of problem decomposition and system organization on system performance, methods for designing coordination strategies that limit agent communication while still providing high-quality solutions, and the design of systems whose performance degrades gracefully as agents and/or communication links fail. Much of Carver's research has focused on distributed sensor networks, an application for DPS/MAS whose importance is increasing rapidly as it becomes practical to build networks of hundreds or even thousands of microsensors. His work involves both empirical and theoretical approaches, and is increasingly focused on methods for dealing with the effects of scale in very large agent systems. One long-term project is using a MAS testbed to develop empirical data that links the performance of various DPS strategies to different classes of sensor network problems. The goal is to identify useful classes of sensor network problems and build a library of DPS strategies that are appropriate for each. Much of the theoretical work has been based on the use of Decentralized Markov Decision Processes (DEC-MDPs) for modeling MAS problems and producing minimum communication coordination strategies. This work has received funding from two grants by the National Science Foundation.

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Database, structured document management, bioinformatics.

Dr. Che's main research interest is in the area of database technology. He is especially interested in XML data management in a database environment. XML has become the ubiquitous and "standard" language for web data/document publications; huge amounts of data/documents formatted in XML have kept mounting up at a tremendous speed. Effective management and efficient querying of XML data are in great demand. His work in this area focuses on developing new storage models suitable for XML and novel and specialized techniques for efficient XML query processing. Che's other research interests include data mining and bioinformatics. With data mining, his interest is in a new type of mining approach - applying mobile intelligent mining agents (called intelligent spiders). With bioinformatics, his focus is on rationalizing the drug discovery process, which is traditionally conducted by trial-and-error, via application of proper data mining technologies to the accumulated (gene and protein) sequence data repositories.

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Signal and image processing, statistical learning theory, biomedical and healthcare informatics, and their applications.

Dr. Cheng's research interests include Feature extraction and selection from high-dimensional and/or large scale dataset; correlation clustering; optimal configuration on graphs; efficient/optimal representation for high-dimensional data and massive data for acquisition, transmission, visualization, and classification; signal/image processing and pattern recognition problems in biomedicine, engineering, healthcare, etc. He worked on multimedia information forensics, security, and pattern recognition. His current research focuses mainly on biomedical information/image processing. Due to the extensive use of medical images for disease prognosis and diagnosis, and health promotion in healthcare and biomedicine, biomedical imaging/processing and bioinformatics become increasingly important. The research questions include how to fuse multiple image modalities and data sources to promote early diagnosis and improve the accuracy, how to represent and transmit huge amount of medical information, and how to extract the most salient information or obtain the sparse representations from large data set, etc. He studies these issues by using computer techniques including machine learning and signal/image processing.

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Distributed systems, cluster computing, mobile computing, and computer networks.

Cluster computing has recently enjoyed an increase in popularity as a distributed computing architecture for its applications including supercomputing, large scale code coupling etc. The design of fault-tolerant algorithms for distributed cluster computing environment is a new challenging area. Dr. Gupta's research activity mainly concentrates on designing such algorithms. Besides, the growing importance of cellular, mobile ad-hoc, and sensor networks for their many applications, designing energy efficient communication protocols for such networks is a demanding area of research in which he is also currently involved.

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Artificial intelligence, multi-agent systems, cognitive science, mobile robotics, knowledge representation and reasoning.

Dr. Hexmoor's basic research on cognitively inspired models has been funded. In addition to spawning numerous theses, his work has been significantly transitioned to mission critical projects of national priority. Hexmoor has pioneered interdisciplinary research that builds on social science models as tools for validation of large agent-based systems in use in space, as well as U.S. military applications. Hexmoor's research laboratory has the mission to promote practical education and research in intelligent agency and multi-agent systems.

His long range interest is to design and implement robotic and software agents and systems with the properties for autonomy, self-adaptation, sociality, and cognition, as well as safety and predictability. Sponsors include DoD, Air Force, Army and private companies.

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Statistical databases, query optimization, data stream processing, spatial data structures, XML databases.

Dr. Hou's research is mainly in the area of database, including XML, data streams, query size estimation, transaction management, and data mining. His current focus is on XML query processing, where he seeks to find efficient query evaluation algorithms for XML data and devise synopses for accurate query result size estimation. He is also interested in clustering streaming data and data mining over data streams, which require quite different techniques from those used in traditional static database environments.

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Development, deployment and evaluation of a Learning Objects and its ramifications for e-learning.

Dr. Mogharreban's research involves the introduction of intelligence in data manipulation and analysis. In particular, he has been working with researchers in other areas such as psychology and management, looking into how fuzzy logic can be applied in these areas for better and more intuitive data representation and analysis. Based on this approach he has developed a prototype of a decision support system in the form of a medical diagnostic system. He is also interested in applying intelligence in the new area of knowledge management and learning objects. A learning object is a unit of knowledge which would contain the necessary information to be an independent entity. Several of these independent units can be put together to create an instructional module. How these units are represented and the meta data required for their selection is the subject of much interesting research.

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Computational intelligence and soft computing, multi-agent systems, distributed computing.

Dr. Rahimi's research interest is in the general area of Computational Intelligence and Soft Computing. Soft computing refers to a collection of computational techniques that study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Soft Computing uses soft techniques contrasting it with classical artificial intelligence hard computing techniques (bound by NP-Complete). Components of soft computing include: Neural networks (NN), Fuzzy systems (FS), Evolutionary computation (EC), Swarm intelligence, Probabilistic Computation, and Chaos theory. Dr. Rahimi is also an expert in Multi-Agent systems (MAS), a core area of Computational Intelligence, in both theory and application. An example of his theoretical research was the development of the API-Calculus in 2002. API-Calculus is a formal modeling tool for design and development of multi-agent systems. API-Calculus includes verification and performance evaluation infrastructure that distinguish it from other modeling languages.

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Computer graphics, agile software development and testing, HCI.

Dr. Wainer's research interests lie in the areas of software development, computer graphics and human computer interaction. He is particularly interested in interdisciplinary work which utilizes the computer as a tool for design and visualization. Within software development, he seeks to study how new techniques in software design (test driven development and agile techniques) might be brought into the classroom and supplemented by software visualization and design aids. In other disciplines, he has explored early design in architecture, and visualization in areas of bioinformatics and GIS. Adapting algorithms normally run on general purpose computers to computer graphics hardware is a related interest that provides new opportunities for interactive design and visualization.

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Pattern recognition, clustering, soft computing, data mining.

Dr. Zargham's research focuses on development of real time vector/quantization dynamical systems and adaptive decision systems. He has been able to develop an expert system for non-traditional applications such as financial market prediction. The system uses adaptation based on assignment of weights to rules. Based on past performance of a market, such a system can "learn" to provide a higher level of performance in future situations. His current work is on the development of a dynamical system for vector quantization or clustering based on ordinary differential equations (ODEs) with potential for a real-time implementation. This set of ODEs generates prototypes (or clusters) by creating valleys in its Lyapunov function. Each valley represents a cluster of similar input patterns.

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Distributed systems and bioinformatics.

Dr. Zhu's research interests include the distributed systems and computational biology. She designs and develops a prototype system that provides users with capabilities to visualize, monitor, and steer real time large scale scientific computations executed on a remote supercomputer with optimal network performance. She also conducts research on various topics in computational biology including microarray analysis, gene network, phylogenetic tree and probe design etc.

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