Computational Sciences and Data Analysis
Modern research and operational decision-making depend on the ability to process, analyze, and extract insight from large and complex datasets. This work advances computational sciences and data analysis through high-performance computing, scalable architectures, and advanced data science methodologies. By leveraging powerful cyberinfrastructure and parallel computing systems, researchers develop and deploy AI-driven models that support faster analysis, improved prediction, and more informed decision-making. These capabilities enable innovation across domains while addressing critical challenges such as algorithmic fairness, bias mitigation, and equitable data use.
Key capabilities include:
- High-performance computing resources, including the XSEDE-enabled supercomputing environment
- Parallel algorithms and advanced computing architectures for large-scale AI workloads
- High-performance computing clusters supporting machine learning and data-intensive research
- Data science and AI research focused on social impact, algorithmic fairness, and bias mitigation
- Distributed AI systems leveraging national cyberinfrastructure resources such as NSF ACCESS and FABRIC
- Scalable data processing and modeling for complex, multi-domain datasets
- Advanced analytics supporting predictive modeling and decision support systems
Adaptive Interception in Dynamic Domains: Exploration of Hybrid Reinforcement Learning in Pursuit-Evasion Tasks
Akinmolayan, M., Josyula, D., Casbeer, D. Adaptive Interception in Dynamic Domains: Exploration of Hybrid Reinforcement Learning in Pursuit-Evasion Tasks, AAAI Spring Symposium, April 2026. Affiliation: Bowie State University (To appear).
A Unified Naming and Addressing Scheme for Hybrid DTN/NDN Communication Protocols
Langrin, R., & Blackstone, J. (2026). A Unified Naming and Addressing Scheme for Hybrid DTN/NDN Communication Protocols. Proceedings of the 2026 AAAI Symposium Series, San Francisco, CA. Affiliation: Howard University, Research Institute For Tactical Autonomy (To appear).
