REU Site: Undergraduate Research Experience in Edge Intelligence

This project establishes a Research Experiences for Undergraduates (REU) site at the University of Nevada, Reno, to provide undergraduate students nationwide with immersive research experiences in the field of edge intelligence. Edge intelligence, where data collection, processing, and analysis are performed close to the data source rather than on centralized cloud-based systems, is a rapidly advancing paradigm critical for applications such as autonomous vehicles, smart cities, industrial automation, and healthcare systems. However, deploying artificial intelligence (AI) on resource-limited edge devices presents substantial challenges in accuracy, efficiency, and robustness, with significant implications for public health, national security, and economic competitiveness. By engaging students in hands-on research and equipping them with the skills to address these challenges, this project aims to develop a highly skilled STEM workforce capable of advancing practical AI technologies. The program will foster sustained interest in research and encourage participants to pursue graduate studies and careers in STEM. Through the development of accurate, efficient, and trustworthy AI systems, the project will contribute to technological innovation, strengthen public trust in AI, and support societal well-being and national security. This REU site develops undergraduate research capacity in edge intelligence by leveraging the interdisciplinary expertise of University of Nevada, Reno faculty in AI, robotics, and cyber-physical systems. The research is structured around three core thrusts in edge-based machine learning (ML) systems: (1) algorithms and architectures for resource-constrained devices, (2) scalable and robust learning in heterogeneous edge environments, and (3) resource management for energy-efficient and sustainable operation. Representative projects include accelerating federated learning in heterogeneous wireless networks, developing memory-efficient training of large language models for edge devices, designing decentralized federated learning with workload balancing, federated fine-tuning of vision models for wildfire detection, and collaborative multi-robot deep reinforcement learning. Guided by experienced faculty and graduate student mentors, students will engage in cutting-edge research and contribute to the development of ML systems for real-world applications such as smart Internet-of-Things devices, autonomous vehicles, and robotics. The program is complemented by training in research methodology, scientific communication, and career development, preparing participants for graduate study and careers in STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.