Project Grant 2348480

The National Science Foundation (NSF) has awarded a $174,878 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Texas Rio Grande Valley (UTRGV). The grant, which runs from September 1, 2024 to August 31, 2026, supports research and development of innovative algorithms for contrastive self-supervised learning of universal time series representation. Specifically, the project aims to integrate the concept of time series motifs into the contrastive self-supervised learning framework to address challenges in preserving semantic meaning during data augmentation and sampling high-quality negative samples. The research outcomes are expected to deepen the understanding of self-supervised time series representation and contribute to a wide range of domain applications, such as smart manufacturing systems. Additionally, the project plans to integrate the research findings into existing courses and develop new STEM education courses at UTRGV, as well as promote the participation of Hispanic students in related research activities and outreach events.