Federal Grant Award Summary

The National Science Foundation (NSF), Division of Computing and Communication Foundations, awarded $200,000 to Stony Brook University under the Computer and Information Science and Engineering program (CFDA 47.070) on July 15, 2026, with an ultimate completion date of June 30, 2030. This collaborative research project focuses on advancing graph-based vector database algorithms to improve efficiency, reliability, and computational resource utilization. Vector databases store and enable efficient search and filtering over vector embeddings—numerical representations generated by machine learning systems that encode information about documents, images, videos, and other media. The project aims to develop faster, more scalable algorithms that reduce computational costs, thereby enabling broader adoption of vector database technologies by small businesses, scientists, and organizations with limited cloud computing or hardware budgets.

The research deliverables are expected to guide the design of next-generation vector database systems capable of handling large-scale data processing while maintaining performance and accessibility. By improving core algorithmic approaches, the project addresses a critical gap between advances in machine learning and engineers' ability to efficiently implement vector database infrastructure. The research will support applications across business (including web and product search), scientific data management, generative artificial intelligence, and other commercial and research domains where vector embeddings have become integral to modern computational workflows.