Data Science Training Core

Assess, develop, and implement data science training curriculum

The AIM-AHEAD consortium members will have a wide range of expertise and skill levels relevant to AI/ML and health disparities research. The A-CC will identify training needs and gaps as well as identify or develop training and workforce development resources to address these.

Training assessments should consider AI / ML and health disparities research as well as related competencies in, for example, cloud computing, distributed computing, data preparation, data science, biostatistics, modelling, epidemiology, clinical / biomedical informatics, data science policy, ethics, and health disparities and community engaged research methods.

Training offerings will be particularly useful for engaging early career investigators and researchers with diverse backgrounds into AI / ML research.The training offerings should consider the skills and competencies needed for the AIM-AHEAD projects as well as the professional development and potential career paths of the trainees.Internships and practical training opportunities with private sector partners may be leveraged.

It’s expected that application-based learning opportunities with large scale data will be an important aspect of training and workforce development.The Data Science Training Core therefore will work closely with other A-CC efforts to develop or leverage platforms to access AI/ML-ready data.

Contact Data Science Training Core


Legand L. Burge (Lead MPI)

Howard University

Alexander Libin, PhD (MPI; Communication, Knowledge Translation, and Dissemination)

MedStar Health Research Institute

Guodong (Gordon) Gao, PhD (MPI)

University of Maryland

Nawar Shara, PhD (MPI)

MedStar Health Research Institute

Peter McGarvey, PhD (MPI)

Georgetown University Medical Center

Ritu Agarwal (MPI)

Robert H. Smith School of Business, University of Maryland