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.

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.

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.

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.

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.

For an Artificial Intelligence Area of Emphasis for Undergraduates, please see https://computing.uga.edu/news/stories/2025/area-emphasis-ai-provides-preparation-similar-emerging-ai-degrees

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, among others. 

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.