Skills and Workforce Development

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This is a working page for the Skills and Workforce Development components of the Bridge2AI program

The Bridge2AI program will use this wiki to bring together resources relevant to the Skills and Workforce Development Modules within the Data Generation Projects and the Skills and Workforce Development Core within the BRIDGE Center.





New Investigators to Promote Workforce Diversity in Genomics, Bioinformatics, or Bioengineering and Biomedical Imaging Research (R01 Clinical Trial Optional)



NITRD STEM Portal. This Portal,, provides a one-stop resource for Federal cyberlearning, computational literacy, and information technology training opportunities at all education levels as we champion a diverse, inclusive, and well-trained workforce capable of future innovations. The resource provides details on programs targeting all levels of education and experience.

The future of data science jobs

Public Health Informatics & Technology (PHIT) Workforce Development Program (Funding Opportunity)


The importance of Increasing Involvement of Underrepresented Populations in Data Science with Biomedical Knowledge 

There is a diversity crisis in the AI sector across gender and race. Yet, diversity of AI engineers is critically needed, so the tools being developed do not merely represent the backgrounds and needs of select groups. Recent studies found only 18% of authors at leading AI conferences are women, and more than 80% of AI professors are men. This disparity is extreme in the AI industry: women comprise only 15% of AI research staff at Facebook and 10% at Google. There is no public data on trans -workers or other gender minorities. For black workers, the picture is even worse. For example, only 2.5% of Google’s workforce is black, while Facebook and Microsoft are each at 4%. Given decades of concern and investment to redress this imbalance, the current state of the field is alarming.   When reviewing STEM majors, compared to White peers, both Black and Latina/o students have significantly higher probabilities of switching out of a STEM major and completing a degree in a non-STEM field than persisting and earning a STEM degree. Specifically, the probability of a Black student switching majors rather than persisting in the major field is about 19 percentage points higher than the probability of a White student; the corresponding probability for a Latina/o student is about 13 percentage points higher than that of a White student.  Native American representation in AI is Minority and other underrepresented populations, such as women, poor, rural and sexual gender minorities (SGM), need special recruitment and retention efforts.  Data scientists must train in biomedical sciences in conjunction with AI. 

It is important to develop a biomedical and data science/AI training pipeline targeting minority and other underrepresented populations, examples include:

  • Pipeline to train underrepresented students starting from high school to community colleges, to colleges/universities, to initial careers. When developing the pipeline, consider barriers to participation

(academic issues, timing, cost, practical considerations, cultural considerations, and need for a student to help family financially and socio-culturally in multi-generational homes).

  • Minority and other underrepresented faculty data science training at Minority-Serving Institutions (MSI), tribal colleges, and community colleges
  • Highlight skills in computations, statistics, and mathematic models
  • Parallel knowledge track in biomedical sciences
  • Peer-Mentors provide support and comradery of like other amidst others not reflective of self.  
  • Compensated internships during the academic year and summers for students to encourage minority and other underrepresented population participation in AI projects
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