Wireless sensor networks, mobile ad hoc networks, wireless sensor and actor networks, wireless mesh networks, smart grid communications and security, wireless multimedia sensor networks, wireless ad hoc network 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 (WSNs), wireless sensor and actor networks (WSANs), wireless mesh networks (WMNs), wireless multimedia sensor networks (WMSNs), vehicular ad hoc networks (VANETs) and underwater acoustic sensor networks (UWSNs). These wireless ad hoc networks came with their own challenges which are di fferent than the traditional networks. For instance, due to limited battery life and resources, energy-efficient protocol design is a must. MANETs, VANETs, or WSANs may deploy mobile nodes whose mobility may affect the design of protocols at various layers. In addition to energy conservation and mobility handling, these networks posed other non-traditional challenges such as self-configuration, self-healing, topology control, node deployment, and energy, security or interference-aware quality of service (QoS) provisioning. Self-configuration, self-healing, and node deployment require eff ective collaboration among the nodes, yet the approaches should be scalable, low-cost and rapidly applicable. Similarly, QoS provisioning approaches should be lightweight while being able to handle mobility, prevent interference among the nodes and incorporate security without additional overhead. Dr. Akkaya's research investigates solutions for several aspects of these challenges.

References:

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.

References:

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.

References:

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.

References:

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 is also active in the area of secured and energy efficient communication in mobile ad-hoc, and wide area networks.

References:

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.

References:

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.

References:

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.

References:

Security & Cryptography, Game Theory & Economics, and Software Engineering.

References:

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.

References:

Software development, HCI, Computer graphics.

Dr. Wainer's research interests lie in the areas of software development, human computer interaction and computer graphics. He is particularly interested in interdisciplinary work which utilizes the computer as a tool for design and visualization. Within HCI and software development, he seeks to study how new techniques in software design (agile techniques like refactoring and test driven development) might be brought into the classroom. Aids for designing, visualizing and communicating software concepts are parallel topics of research. In other disciplines, he has explored early design in architecture, and visualization in areas of computer security, bioinformatics and GIS. Adaptation of algorithms normally run on general purpose computers to systems utilizing computer graphics hardware is a related interest that provides new opportunities for interactive design and visualization.

References:

Mobile learning, pattern recognition, data mining.

Dr. Zargham's research focuses on development of smart phone applications, real time vector/quantization dynamical systems, and adaptive decision systems. Currently, he is working on Mobile Learning using smart phone platforms to deliver some of the core courses in computer science. In many countries, almost every person owns a mobile phone. The majority of students carry mobile phones wherever they go; they play, eat, and sleep with mobile phones. So we have to find effective ways to attract, teach and keep their interest in what we would like them to learn by utilizing this technology. He is also working 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.

References:

Parallel and Distributed Computing, High Performance Networking, Remote Visualization and bioinformatics.

Dr. Zhu's research interests include the high performance computing, distributed system and computational biology. She proposes a remote computational steering system that employs analytical models to estimate the cost of computing and communication components and optimizes the overall system performance in distributed environments with heterogeneous resources. The computing pipeline configuration problems are formulated into different classes according to system constraints and are proved to be NP-complete. She presents several heuristic approaches to maximize the reliability and minimize the end-to-end delay. The superior performance of the proposed solution is demonstrated with extensive simulation results in comparison with existing algorithms and is further evidenced by experimental results collected on a prototype implementation deployed over the Internet. On distributed sensor network topic, she propose a binary decision fusion rule that reaches a global decision on the presence of a target by integrating local decisions made by multiple sensors. Without requiring a priori probability of target presence, the fusion threshold bounds derived using Chebyshev's inequality ensure a higher hit rate and lower false alarm rate compared to the weighted averages of individual sensors. She also conducts research on various topics in computational biology including microarray analysis, gene network, phylogenetic tree and probe design etc. Her research works are funded by Department of Energy, Oak Ridge National Laboratory, National Science Foundation and Illinois State Board of Education etc.

References:

Obesity diagnosis and treatment, soft computing, patient education, information technology in the outpatient medical practice, human/machine interface, occupational medicine and industrial accident management, international medical education, osteoporosis diagnosis and treatment.

Dr. Sean Bozorgzad's research interest falls in all aspects of outpatient medical care. He considers obesity in childhood a key predictor for obesity in adulthood and therefore causes other health problems. His research includes detection and treatment of obesity in children in its early stages.

Quantum error prevention methods, simulating quantum systems with quantum systems, quantum information theory.

Currently and in the recent past, Dr. Byrd's research has primarily focused on the prevention, correction and suppression of errors which arise in proposed quantum information processing devices. This includes quantum error correcting codes, decoherence-free subsystems (a.k.a. noiseless subsystems) and dynamical decoupling controls as well as combinations of these methods of quantum error prevention. The objective is to develop practical quantum error prevention protocols for a variety of proposed prototypical quantum information processing devices. Other research has investigated algorithms for simulating quantum systems with quantum systems. This is a promising area of future applications of quantum computing devices since it is known to provide an exponential speed-up over classical computing systems.

Quantum control, decoherence, quantum algorithms in the presence of noise, quantum error correcting codes, fault-tolerant quantum computing.

Dr. Gaitan's research interests fall within the area of quantum computing. Quantum computing is a recently established, strongly interdisciplinary field of research that is attempting to develop a technology that can harness the inherent capacity of quantum systems to do massively parallel processing of information. This capacity rests on the linear character of quantum dynamics, and on non-local correlations known as entanglement which only occur in multi-component quantum systems. Ideal quantum computers have been shown to be capable of carrying out a number of important information processing tasks more efficiently than existing digital computers. The field faces two central challenges: (1) protecting the computational data from errors due to noise and imperfectly applied quantum logic gates; and (2) protecting entanglement from a particular type of noise known as decoherence. It has been shown that concatenated quantum error correcting codes, together with fault-tolerant protocols for implementing quantum logic gates, measurements, and state-preparation, will allow a quantum computation of arbitrary duration to be done if sufficiently reliable quantum gates can be built. Gaitan's work over the past few years has focused on: (1) attempting to design a universal set of quantum logic gates that can approach the accuracy threshold for fault-tolerant quantum computing; and (2) simulating the performance of quantum algorithms in the presence of noise. This work is a mixture of analytical and numerical techniques, and has been funded by the National Science Foundation and the Army Research Office. Gaitan is also writing a book to be published by Taylor and Francis entitled, "Quantum Error Correction and Fault-Tolerant Quantum Computing".

References:

VLSI design automation, built-in self-test, communication networks, bioengineering.

In VLSI Design Automation, Dr. Kagaris develops methodologies for the efficient transistor-level implementation of digital circuits. This is a challenging area as, in contrast with gate-level implementations, there are no established algebraic tools for optimization. Issues here include transistor count reduction, power reduction, and delay minimization. In Built-in Self-Test, a small additional logic is included in the circuit to test against faults introduced after its VLSI fabrication. Dr. Kagaris's research in this area focuses on the design of hardware mechanisms that can generate quality random test vectors, or regenerate economically a precomputed set of test vectors. Research in this area concerns also efficient test architectures for Systems-on-Chip. In computer networks, Kagaris's research concerns issues in the network, data-link, and medium-access layers. Finally in bioengineering, current research deals with issues in microarray data analysis.

References:

Phonotics and neural networks.

Dr. Sayeh's research focuses on the photonic implementation of computing/information subsystems and understanding of information storage and clustering systems. Currently he is involved with the design and analysis of adaptive associative memory architectures based on ordinary differential equations. He is also exploring photonic architectures for building a terahertz A/D converter which will be used in digitizing RF signals.

References:

CAD for VLSI, design for testability, computer networks.

In integrated circuits, Dr. Tragoudas' research activities focus on analysis and defect behavior in deep-submicron technology. In networking, his activities focus on security, power management, and routing aspects in ad-hoc networks with special considerations in sensor networks. Sponsors include Intel, Qualcomm, NAVSEA Crane, and the National Science Foundation.

References: