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.

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.

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.