Wireless Sensor Networks, Mobile ad hoc Networks, Wireless Sensor and Actor Networks and Wireless Mesh Networks.

Dr. Akkaya's research mainly focuses on the problems at the network layer of wireless sensor networks (WSNs). He has been working on novel and energy efficient algorithms that can handle delay-constrained routing, in-network data aggregation, routing security, jamming, base-station and sensor relocation, and mobility of base-stations in severely resource constrained environments. In addition, he is working on positioning, clustering, coverage, fault-tolerane, and QoS problems in wireless sensor and actor networks (WSANs) where powerful actors perform certain actions based on the received data. Dr. Akkaya investigates new techniques which deal with non-traditional trade-offs and technical challenges associated with special characteristics of these wireless networks. Dr. Akkaya's other research interests include QoS and security provisioning in mobile ad hoc networks (MANETs), wireless mesh networks (WMNs), and wireless multimedia sensor networks (WMSNs).

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Multi-Agent Systems, Sensor Interpretation,
Knowledge-Intensive Control of AI Systems.

Dr. Carver's research is 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 is currently being funded by its second grant from 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, Image, and Multimedia Processing, Learning Theory, Multimedia Computing and Communications

Dr. Cheng's research interests include image, multimedia, signal processing, pattern recognition and machine learning, and their applications to multimedia, biomedical computing and communications,and healthcare. He worked on multimedia information forensics, security, and pattern recognition. His current research focuses mainly on biomedical information and image processing. Due to the extensive use of medical images for disease prevention, diagnosis and treatment, and health promotion in health care and biomedical research and practices, biomedical imaging and processing are becoming 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 machine learning, signal processing technics, etc.

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Distributed systems, fault-tolerant 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 very new and 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.

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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End user computing, computer training, computing in special populations, decision making, decision science.

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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Distributed computing, software agents, expert systems, fuzzy logic, soft computing.

Dr. Rahimi's research interest is in the area of multi-agent systems 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. Rahimi and his research group are developing a comprehensive API-based modeling tool that would provide the capability of graphically designing multi process/agent systems with features such as verification and performance evaluation of systems modeled in it. From the agents' application aspect of his research, they have developed several software systems including: an agent-based geospatial data integration system (1999-2002), massage passing interface-agent based for high performance computing (2003-2005), agent based system administration (2002-2004), and web-based multi-agent knowledge discovery (2005-now), to name a few.

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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, soft computing, data mining.

Dr. Zargham's research focuses on development of adaptive decision systems based on fuzzy logic. Fuzzy expert systems are considered more flexible and economical than the classical expert systems since fewer rules or combinations of rules are needed to cover more possible outcomes. Unlike a classical expert system, fuzzy systems can handle overlap or ambiguity between rules. His current work is on the development of expert systems 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.

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Remote Visualization System, Distributed High Performance Computing, Bioinformatics, Distributed Sensor Networks.

Dr. Zhu's current research interests are on the design and implementation of large-scale distributed computing framework that provides clients with capabilities to visualize, monitor, and steer real time scientific computations executed on a remote supercomputer. She also conducts extensive research on high-throughput computational genomics including microarray clustering, molecular docking for drug design, gene regulation network, and genomic DNA reference etc.

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