This $557,158 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports research at the University of Connecticut (UConn) to develop novel mathematical operators that can efficiently process large, sparse graph-based data for advanced AI applications. The project aims to address computational bottlenecks and performance scaling challenges for processing graph models on massively parallel hardware. Key goals include devising sparsity-optimized matrix operators that leverage vectorization and high-core-count processors to unlock sustainable and scalable AI performance, particularly for applications like autonomous systems, traffic forecasting, and semiconductor chip design. This research will also integrate the findings into computer science curriculum and disseminate the results through industry collaborations to enable practical adoption of emerging AI technologies. The award period runs from March 1, 2025 to February 29, 2028.
Generated 3/18/25, 4:17 AM