CAP: Developing a Machine Learning Lab for AI-Powered Training and Research

This project is an ExpandAI Capacity building pilot (CAP), which focuses on establishment of a robust Artificial Intelligence (AI) infrastructure at Auburn University, Montgomery (AUM) thereby enhancing the research capacity of the institution and facilitating AI-focused educational curriculum development and training. Towards this goal, the project will establish a Machine Learning (ML)-Lab at AUM, with essential hardware and software components necessary for instruction and research in AI and ML.

The project introduces an AI-powered tool to guide both new and experienced users in the implementation of their AI/ML research projects across campus. The goal is to establish a new degree program and to create opportunities for students and faculty to carry out practical hands-on research guided by AI. The project will also build community and new centers of excellence in AI where such activities were not previously well developed.

The ML-Lab is a comprehensive approach that integrates AI education with applications in diverse academic areas. By providing access to high performance computing resources and customized ML and AI algorithms, the intellectual productivity of students and faculty researchers will be significantly enhanced. The ML-Lab will foster interdisciplinary collaborations as researchers share information about the application of ML and AI techniques in their areas of expertise.

Faculty from computer science, biology, chemistry and other academic areas will learn from one another through interactions made possible by the ML-Lab. Research immediately impacted by the ML-Lab will span topics ranging from natural language processing to sky image classification and bioimage analysis, and the ML-Lab will strive to enhance research and teaching in any academic area at AUM.

The research thrust will explore the development of novel AI methodologies for the analysis of complex image data sets, including cortex-inspired contextually guided deep networks that draw on cross-modal learning principles. The algorithms developed by this project could significantly enhance diagnostic technologies in neuroscience and other clinical settings such as quantitative sensory testing and early detection of neuropathies.

An additional broader impact of this initiative is community outreach, which aims to democratize AI knowledge by extending educational resources to local schools and community groups.

The ExpandAI Program supports AI-powered education and workforce development, infrastructure and research at Minority Serving Institutions to strengthen and diversify U.S. research and education pathways and provide historically marginalized communities with new opportunities in STEM 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.