REU Site: Artificial Intelligence in Multidisciplinary Engineering Research for Guided Exploration (AI-MERGE)

The REU Site Artificial Intelligence in Multidisciplinary Engineering Research for Guided Exploration (AI-MERGE) at the University of Memphis is a 10-week summer program that provides undergraduate students with hands-on research experiences applying artificial intelligence (AI) to engineering problems. Advances in AI are transforming the design and operation of infrastructure, biomedical technologies, and sensing systems, creating a need for engineers with expertise in data-driven methods and computational tools. The program recruits undergraduate students, especially those from institutions with limited research opportunities. This REU program is open and available to all American undergraduate students. Participants are engaged in interdisciplinary, faculty-led teams addressing engineering challenges in biomedical systems, imaging, smart infrastructure, and resilient systems. The program integrates research with structured mentoring, training in AI tools, and professional development, and includes opportunities for students to present their work. By expanding access to research and strengthening pathways to graduate education, the project contributes to the development of a strong engineering workforce and supports the nation’s technological competitiveness. The AI-MERGE REU Site provides undergraduate research training in artificial intelligence methods applied to multidisciplinary engineering problems. Research is organized around applications in imaging and biomedical systems, smart infrastructure, and security and resilience, where AI techniques are used for data analysis, modeling, and system optimization. Students participate in faculty-led projects involving machine learning and computational methods, including problem formulation, algorithm development, and performance evaluation using real or simulated data. The program leverages the Vertically Integrated Projects (VIP) framework to support team-based research with tiered mentoring from faculty, graduate students, and experienced undergraduates. Students are embedded in ongoing research efforts and contribute to project outcomes, producing presentations and written reports. The REU Site supports annual cohorts and provides training through research activities, seminars, and professional development. Expected outcomes include student-authored presentations and contributions to engineering research. The project advances AI-enabled approaches for engineering systems while preparing undergraduate participants for graduate study and research careers. 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.