Specific Aims:

Assess Needs and Capacities: Conduct a detailed assessment of workforce AI/ML training needs and the University of Houston's AI/ML data and infrastructure capacity.
AI/ML Training: Provide training and capacity-building services to train trainers for staff at the proposed resource center.
Foster Partnerships: Nurture, engage and expand the multi-disciplinary and multi-institutional partnerships between the University of Houston, Savannah State University, UTHealth School of Biomedical Information, industry, AIM-AHEAD ACC, cores, stakeholders and other DICB Awardees.

Impact: Our findings will significantly enhance the UH capacity through a mutually beneficial collaboration with AIM-AHEAD. Our research provides a generalizable framework for tailored implementation of training to include:

Training and Capacity Building: AIM-AHEAD will provide training and capacity-building opportunities for UH staff and researchers.
Knowledge and Expertise Exchange: Partnering with AIM-AHEAD provides an opportunity to tap into their extensive knowledge and expertise in AI/ML applications for healthcare and health disparities research. This collaboration will facilitate the exchange of ideas, best practices, and innovative approaches, enabling UH to enhance its understanding and capabilities.
Policy and Advocacy: Leverage AIM-AHEAD's expertise in AI/ML to support UH's efforts in influencing policy decisions related to health disparities. We will significantly enhance UH's capacity by providing access to expertise, training and policy support to reduce health disparities within its community dramatically.