Neuro-symbolic Computing Research (NES) Lab at the School of Computing at the University of Georgia explores the integration of symbolic reasoning and neural network approaches to enhance Artificial Intelligence systems. By combining the strengths of rule-based logic and machine learning, it aims to develop models with improved knowledge representation, interpretability, and the ability to perform complex reasoning tasks. The research focuses on creating hybrid architectures that leverage both symbolic and neural components for more robust and adaptable AI systems.
Our broad research interests include informatics, AI models, and how to bridge these two. More specifically we are interested in human-brain-inspired AI, explainable AI, knowledge graphs, knowledge-infused and multi-modal AI, foundation models, forecasting techniques, and their applications to pandemic forecasting.
Our active research projects include:
- Improving COVID-19 Forecasts
- COVID-19 Temporal Knowledge Graph
- Building a Time-Series Foundation Model
- Multi-modal Time-Series Forecasting
Why is our logo an octopus?
An Invitation to Students and Potential Collaborators
We also strongly believe in interdisciplinary research and the importance of the social impact of our research. We are particularly interested in working with researchers in biomedical sciences and public health, bioinformatics, neuroscience, and physical and social sciences (this is not a complete list). If you have an interest in one of our research areas, and would like to discuss potential collaboration opportunities please contact one of the faculty members.