The faculty of the Department of Computer Science is composed of energetic and creative professors whose interests cover many of the growing or emerging research areas in Computer Science. The department has various research groups and labs and several of them have significant external funding. Particular strengths include theory, systems, information systems, and artificial intelligence.
Below is a list of active research clusters in our department:
- Algorithms and Combinatorics
- Artificial Intelligence
- Bioinformatics and Health Informatics
- Computational Genetics
- Computational Intelligence
- Computer Vision and Image Processing
- Computer Networks
- Cortical Architecture Imaging and Discovery
- Databases and Distributed Information Systems
- Parallel and Distributed Computing
- Parallel Processing
- Real-time Systems
- Scientific Computation
- Semantic Web and Semantic Web Processes
- Operating Systems
The design and analysis of advanced algorithms is useful in a variety of applications. Combinatorial analysis of discrete structures is important in analyzing algorithms as well as in understanding the properties of the discrete structures themselves. Established research at UGA in this area has focussed on issues in complexity theory concerning exact (parameterized) and approximation algorithms; exact and asymptotic combinatorial enumeration; structural studies; loop-free algorithms; and graph algorithms. Recent studies have expanded to include randomized combinatorial algorithms, bioinformatics, quantum computation, and algorithms for counting and generating Feynman diagrams.
Artificial intelligence is the computer modeling of intelligent behavior, including but not limited to modeling the human mind. We see it as an interdisciplinary field where computer science intersects with philosophy, psychology, linguistics, engineering, and other fields. Example areas of AI expertise at UGA include natural language processing, logical reasoning and decision making, evolutionary computing, neural networks, robotics, intelligent information systems, vision, and expert systems to name a few.
Biology is increasingly considered to be a data-intensive discipline, replacing earlier hypothesis-driven and lab oriented approaches. A large mass of experimental data (e.g., genomic data at sequencing center, proteomic and glycomics data generated using high throughput experiments) is being generated by the academic and commercial institutions. Computational and informatics approaches are needed to identify features in the DNA sequences, to suggest hypotheses as to the function of specific sequences, or to develop new pathways. The research in bioinformatics by the computer science community at UGA mainly involves algorithms; models; visualization; data integrations; information systems (including mining and knowledge discovery); and high performance computing for computational problems in biology through collaborations with biologists. Researchers at computer science depeartment are significant parts of several large centers and multidisciplinary projects.
In the health informatics area, we are doing leading edge research to support Electronic Medical Records and improved quality of care, by addressing the technical issues of information integration and protocol (clinical pathway) support, using Semantic Web and database management approaches.
Recently, a number of technical advances in molecular biology, such as cloning and sequencing DNA fragments, have resulted in a new approach to genetics. Where traditionally genetics has proceeded from a phenotype to a DNA fragment (gene), the new genetics with its molecular tools often proceeds in reverse: from an anonymous DNA fragment to its biochemical function (phenotype).
Our research in this area has concentrated on developing an information system for the genome mapping. The system, called Fungal Genome Database (FGDB), used to create and store maps of of fungi (initially nidulans) is under development.
Also, we are interested in developing new algorithms and computational methods in various areas of genetic mapping.
In conjunction with the Artificial Intelligence Center, several studies in computational intelligence have been conducted. Genetic algorithms and simulation are used to find good (in many cases near-optimal) solutions to hard problems that are intractable using traditional techniques. Examples include: multiple fault diagnosis, battlefield communication network configuration, chromosome reconstruction, edge detection, equation development for describing relationships in complex data, and the snake-in-the-box problem.
A variety of problems in low- and high-level vision are studied.
The low-level vision (i.e. image processing) problems being addressed are edge detection, stereo correlation, contour grouping, image segmentation, and figure-ground discrimination. Various computational approaches such as genetic algorithms, simulated annealing, neural networks, and parallel and distributed processing are being investigated in the context of these low-level vision problems.
In high-level vision, the current research is focused on the identification and localization of objects in range and intensity images from prestored CAD models. Efficient recognition and localization algorithms based on graph theory such as subgraph isomorphism and hypergraph monomorphism are being investigated.
Issues related to efficient retrieval from large object model databases are also being addressed. In particular, hierarchical index and hash structures well suited for object models represented as attributed relational hypergraphs are being investigated.
The research in low- and high-level vision is being applied to several application areas such as automated industrial inspection, geographic information systems and multi-media systems.
Networks are becoming increasingly complex as the needs for speed, bandwidth, robustness, and security increase. The network research group focuses on the problem of building efficient, scalable and secure networks and applications. The research topics include building fast packet forwarding and inspections, designing methods to reduce deployment efforts for network protocols and applications, building scalable network services, and improving the accuracy and performance of network security systems. Examples of recent studies include asymmetric protocol modifications for streaming media and network storages, scalable online game servers, and network-based anti-SPAM systems.
The CAID (cortical architecture imaging and discovery) lab's research mainly focuses on the discovery of structural and functional architectures of the cerebral cortex via brain imaging and computational modeling. Our long-term goals are to discover the fundamental principles that sculpt the cerebral cortex from organizational, developmental and evolutionary perspectives, and to understand the relationship between cortical structure and function. We are interested in the cortical folding mechanisms, cortical structural connectivity and connectomes, higher-order cortical functional interactions, temporal and frequency dynamics of brain functions, and functional interaction of perception, cognition and environments. We mainly use multi-scale, multi-modal imaging data as the information source, and use a wide range of computational approaches to build models and develop theories. We have strong interests in applying the discovered principles and theories to better understand the dysfunctions of neurological, psychiatric, neurodevelopmental and neurodegenerative disorders including Alzheimer's disease, Schizophrenia, Prenatal Cocaine Exposure, Post-traumatic Stress Disorder, Autism, and Depression, among other brain conditions.
Today's information systems utilize a variety of sophisticated software tools and systems. Database systems form the core technology supporting modern information systems. Previous work in this area has focused on semantic data models, knowledge-based systems, transaction management, GUI query tools, and state-of-the-art database systems (object-oriented, distributed and federated). Ongoing efforts include work in interoperable information systems (with emphasis on transactional workflow management), global information systems (with emphasis on infrastructure for managing heterogeneous data, meta-data for digital media, and information brokering), and intelligent information systems (with emphasis on integrating knowledge, data and models).
The parallel processing group is pursuing both the advanced use and the development of parallel processing systems. Since parallel processing systems are being used in the most compute-intensive applications, we have been investigating the implications of parallel processing in the areas of interest to us: image processing, robot vision, satellite data processing, matrix reduction, nonlinear wave equations, banded, circulant, and Toeplitz systems of equations, multivariable partial differential equations, and VLSI physical design.
Since parallel systems are often awkward to quite difficult to implement applications on, we have an interest in improved programming, networking, and development environments for parallel systems. We have implemented parallel algorithms on pipeline systems, hypercube systems, and SIMD systems (the MasPar). We have proposed a new parallel systems architecture (the Reconfigurable MultiRing) that is more efficient, easier to program, and lower cost for certain applications.
In real-time systems, many events have specific timing constraints. If these constraints are violated, a system failure occurs. These types of systems are used in many applications incuding airplane autopilot systems and powerplant controllers. Because these systems are often used in safety critical applications, it is essential that we can guarantee the timing requirements will be met before the system is used. To this end, we analytically develop tests to guarantee all jobs will meet their deadlines.
The main focus of the robotics research group is the development of autonomous mobile robots (AMRs). With AMRs there are two primary issues to deal with: (1) cognitive behavior, and (2) motion. Cognitive behavior addresses problem solving using sensory inputs and desired goals. Motion deals with aspects of movement from simple robotic arm movement to autonomous rovers in unknown environments. Cognitive behavior is the current focus of the research group. Two projects currently underway involve on-board image processing of video camera inputs for decision making, and the development of an evolutionary computing approach to controller configuration (possibly using field programmable gate arrays). In addition, the controller evolution project is attempting to provide for automatic (rule directed) behavior specification.
Modern numerical analysis uses high performance computing machines to solve complex mathematical problems for which simple analytic solutions are not available.
Service Oriented Architectures, especially with the use of Web Services to provide loosely coupled approach to develop distributed systems, is gaining wide industry acceptance. The Semantic Web has emerged as the vision of the next generation of the web, in which meaning is associated with Web resources (data, documents and services) and represented in a machine processable form. LSDIS lab in our department is one of the oldest, largest and most significant research group in the world in the emerging area of Semantic Web and Semantic Web Services/Processes. Core faculty expertise comes from distributed databases and information systems, knowledge representation and AI, distributed systems and algorithms. This has resulted in substantial body of the work in ontology development, automatic metadata extraction and semantic annotation (with associated challenges in entity identification/recognition and resolution /disambiguation), semantic annotation of Web services (including a W3C submission on WSDL-S) and use of semantics in complete Web process life cycle, as well as scalable and high performance query processing and reasoning including RDF query processing and analysis of large RDF graphs for discovery of complex relationships (called semantic associations). Semantic applications in the areas of biology, health care, national security, financial services, and risk & compliance have been built. Emerging research now applying semantics to enable new capabilities at middleware and network levels. Researchers also have extensive collaboration with industry (e.g., IBM and CISCO), and are involved with many international bodies and initiatives including W3C, OASIS, and Eclipse.
Simulation involves the creation of a computer model of some real-world phenomenon and the execution of that computer model. Systems that are modeled include air-traffic, ground traffic, inetwork behavior, insect swarms and more. One aspect of work in this field is the creation of better models, those that more accurately reflect the real-world system. Another aspect of work in this field is the creation of the simulation system itself. Professor Miller has created JSIM, a Java-based simulation and animation environment supporting Web-Based Simulation, a rapidly emerging area of simulation research and development. Professor Hybinette's interests are in the area of interactive parallel computing in which end-users and the physical environment interact with running, distributed application programs (and affect the programs' execution). Her research has centered on large-scale, high performance, discrete event simulation. Work in cloning and merging contributes to improved performance on these systems. Currently, a Java-based optimistic parallel discrete event simulation is under development. Professor Eileen Kraemer collaborates with Professor Hybinette in this work.
The scope of the operating system research includes scheduling, caching, storage systems, distributed systems, security and performance. The operating system group in UGA studies the OS support for a variety of devices, ranging from battery powered sensors, to standard desktop PCs, and to supercomputers. Current on-going research project include building client-centered operating system modifications for mobile devices, energy efficient super-computing, and distributed caching for web services.