AIM-AHEAD All of Us Training Program
Program Directors
Toufeeq Syed, PhD; Robert Mallet, PhD; Legand (Lee) Burge, PhD
Purpose
The AIM-AHEAD All of Us Training Program is intended to increase researcher diversity in AI/ML by leveraging the All of Us data and infrastructure (Researcher Workbench). This training program was designed to increase researcher diversity in AI/ML by training individuals from diverse backgrounds who are committed to gaining proficiency in AI/ML data analysis, and applying their expertise to benefit communities underrepresented in biomedical research.
About AIM-AHEAD
Artificial Intelligence/Machine Learning Consortium to Advance Health Equity And Researcher Diversity (AIM-AHEAD) is an initiative funded by the National Institutes of Health that is dedicated to enhancing diversity in the field of artificial intelligence and machine learning (AI/ML), with emphasis on reducing health disparities and promoting health equity. Led by the AIM-AHEAD Coordinating Center, this program has cultivated a diverse nationwide consortium that embraces equity and transparency, aimed at leveraging AI/ML to enhance health outcomes across diverse and under-resourced communities. Through the establishment of strategic partnerships, targeted research programs, and engagement with key stakeholders, AIM-AHEAD continues to bolster participation and foster meaningful involvement within the AI/ML and health equity field.
Participants
Through a collaborative partnership between AIM-AHEAD, All of Us, and RTI, Cohort 1 engaged 23 trainees from a diverse group of graduate students, postdocs, early-career faculty, healthcare professionals, and other non-academic professionals from underrepresented populations.
Cohort 1 Trainees
- Tadesse Abegaz: Florida Agricultural and Mechanical University
- Emily Bear: So’oh-Shinálí Sister Project
- Alaysia Brown: Harvard University
- Cecily Byrne: University of Illinois at Chicago
- Mia Campbell: Johns Hopkins Bloomberg School of Public Health
- Christian Cazares: University of California San Diego
- Sonya Dave: Voice of a Child
- Anita Esquerra-Zwiers: Hope College
- Wesley Godfrey-Chaparro: Johns Hopkins University
- Steven Golob: University of Washington Tacoma
- Yaritza Inostroza-Nieves: San Juan Bautista School of Medicine
- Laleh Jalilian: University of California Los Angeles
- Youjeong Kang: Emory University
- LaKeta Kemp: Howard University
- Jonathan Kim: Arizona State University
- Monica Ochapa: Morgan State University
- Michael Ortega: The Queen's Medical Center, Center for Biomedical Research
- Fadekemi Osaye: Alabama State University
- Oyomoare Osazuwa-Peters: Duke University
- Zahra Rahemi: Clemson University
- Lyndee Ward: University of North Texas Health Science Center
- Shayla Williams: Albany State University
- Robin Zhao: Weill Cornell Medicine
Cohort 1 Participant Data
Impact
Trainees leveraged the AIM-AHEAD Connect Platform, AIM-AHEAD Data Science Training Core, and All of Us Resources to navigate the program and complete a research project utilizing All of Us data subsets in the Researcher Workbench. Participants also received training and technical assistance related to R, Python, Jupyter Notebook, and model development. Graduates of this training program are well prepared to harness AI/ML approaches to conduct hypothesis-driven analysis of complex datasets.
All of Us Available Data Types
Cohort 1 Outcomes
The infographic below provides a snapshot of key metrics from the AIM-AHEAD All of Us Training Program for Cohort 1, which was comprised of 23 trainees. It highlights participation, feedback, task completion, and overall progress toward the program’s goals. (Some metrics may vary based on trainee evaluation response totals; variations have been indicated below).
View additional Cohort 1 Accomplishments here: AIM-AHEAD All of Us Cohort 1 Trainee Achievements
Program Inquiries
All questions and inquiries regarding this program can be directed to the AIM-AHEAD Training Programs HelpDesk. Please create a help ticket here: HelpDesk - Submit a Ticket