Call for Applications (Closed)

AIM-AHEAD All of Us Training Program

Traineeship in Advanced Data Analysis using the All of Us Database

Program Description

The application of artificial intelligence and machine learning (AI/ML) to large datasets is dramatically expanding the capacity for hypothesis testing impacting the biomedical and socioeconomic domains. However, underrepresented communities, particularly those at heightened risk of socioeconomic and health disparities, are not receiving AI/ML’s benefits. Training a diverse workforce of researchers proficient in the application of AI/ML represents an opportunity to address a critical unmet need by potentially extending the benefits of AI/ML to underrepresented, at-risk communities. 

The central goal of the AIM-AHEAD All of Us Training Program is to promote researcher diversity in AI/ML by training individuals from diverse backgrounds, such as those from underrepresented racial and ethnic groups, who are committed to gaining proficiency in AI/ML data analysis and applying their expertise to benefit communities underrepresented in biomedical research.

View the AIM-AHEAD All of Us Training Program Infographic linked below which offers a visual overview of the first cohort's academic achievements, acquired competencies, and substantive evaluations provided by trainees regarding their personal experiences throughout this extensive training program.

AIM-AHEAD All of Us Training Program Infographic.

Program Objectives

The AIM-AHEAD consortium (Data Science Training Core and Communications Hub), All of Us, and RTI are partnering to offer AIM-AHEAD stakeholders, trainees, mentees, and consortium partners a training opportunity designed to increase researcher diversity in AI/ML by leveraging the All of Us data and infrastructure (Researcher Workbench).

The Researcher Workbench is a cloud-based platform where registered researchers can access Registered and Controlled Tier data. Its powerful tools support data analysis and collaboration. Researchers use the Workbench to access, store, and analyze data for specific research projects. Researchers can perform high-powered queries and analysis within the All of Us datasets using R or Python via the integrated, cloud-based Jupyter Notebook environment.  

Using the AIM-AHEAD Connect Platform, this 8-month training program will engage a diverse group of 25 graduate students, postdocs, early-career faculty, healthcare professionals, and other non-academic professionals from underrepresented populations. Trainees will use the Dataset Builder to search, extract, and organize health information from the All of Us database, and use the Cohort Builder to create, review, and annotate data from All of Us human subject cohorts. Trainees will also receive training and technical assistance related to R, Python, Jupyter Notebook, and model development for All of Us data subsets in the Researcher Workbench. Training will include:

• • Merging/validating data across All of Us sources
  • Building a supervised model
  • Splitting data into subsets for model training and testing
  • Considering biases that may be present and detected or missed by the model
  • Validating the model

The training, which utilizes All of Us data collected from communities historically underrepresented in biomedical research, is directed particularly toward investigators conducting research at the intersection of AI/ML and health disparities. Trainees can work independently, with another trainee who has similar interests, or a community partner. 

Potential research topics that could be addressed include, but are not limited to:

• • Examining statistical variation in the social determinants of health and intersectionality
  • Examining statistical interactions between family health history and lifestyle factors
  • Using statistical analysis to identify socioeconomic, environmental, and heritable determinants of clinically significant diseases and syndromes

Please refer to the following resources to determine if your desired research topic is viable to pursue in the All of Us training program:

Having received advanced practical training in coding, model development, hypothesis testing, and data cleaning and analysis, trainees completing this program will be well prepared to harness AI/ML approaches to conduct hypothesis-driven analysis of complex datasets. The trainee will join the community of AI/ML professionals passionately committed to extend the benefits of AI/ML to communities underrepresented in biomedical research.

Trainee Expectations

• • Attend all training sessions, both synchronous and asynchronous, including webinars and seminars
  • Work on the program an average of at least 8 hours per week
  • Engage with an AIM-AHEAD Mentor (to be assigned through the program)
  • Engage in learning communities and peer networking
  • Access the All of Us Researcher Workbench
  • Complete the provided training related to R, Python, and Jupyter Notebook, available via AIM-AHEAD Connect
  • Complete the supervised training on model building for analysis
  • Complete the provided training on data splitting for algorithm training and testing, addressing biases in model development, and model validation
  • Utilize concierge services on the Workbench and R/Python coding
  • Utilize AIM-AHEAD HelpDesk support
  • Work individually or in partnership with another trainee to present a work-in-progress research poster at the AIM-AHEAD Annual Meeting in summer 2025
  • Work individually or in partnership with another trainee to generate an abstract suitable for submission to a conference, and/or a manuscript suitable for peer-reviewed publication
  • Play an active part in the AIM-AHEAD community

Trainees Will Receive

• • An $8,000 stipend upon successful completion of trainee milestones
  • A $2,000 allowance to attend the AIM-AHEAD Annual Meeting 2025
  • Support and guidance from an experienced AIM-AHEAD mentor
  • Support from the AIM-AHEAD Data Science Training Core
  • Direct 1:1 guidance, virtual office hours, HelpDesk support, and concierge services supporting users of All of Us Researcher Workbench, R, and Python coding
  • Training on:
    • Data analysis using All of Us Researcher Workbench, R, Python, and Jupyter Notebook
    • Use and applications of R, Python, and Jupyter Notebook
    • Data merging and validation across All of Us sources
    • Building and validating models for analysis
    • Data splitting methods for model training vs. testing
    • Detecting and addressing biases in model development
    • Hypothesis development for testing by analysis of All of Us data
    • Assistance with obtaining a Data Use and Registration Agreement (DURA), if needed
    • Using the All of Us database of de-identified medical data from >1 million Americans

Trainee Program Stipend

Each trainee will receive a stipend of $8,000 which will be disbursed in four installments based on trainee completion of required milestones. Each trainee will also be provided an allowance of $2,000 to cover the cost of airfare, hotel accommodations, local transportation and per diem to attend the AIM-AHEAD Annual Meeting 2025.

*Trainees may apply to multiple AIM-AHEAD training programs and fellowships, but if accepted into more than one, they must select only one AIM-AHEAD training program to participate in per funding cycle. Additionally, AIM-AHEAD-funded Coordinating Center Personnel or AIM-AHEAD-funded Awardees (MPIs, PIs, Co-Investigators, other personnel) are eligible to apply, but if accepted, they will not receive the stipend and allowance.*

Trainee Mentorship

Each awarded trainee will receive research and career mentorship from experienced, skilled investigators selected from AIM-AHEAD core members. The online mentoring platform AIM-AHEAD Connect (https://connect.aim-ahead.net) will be used to match mentors with awarded trainees and for mentor/trainee engagement and progress tracking.

1. Applicants must be:
   1. U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals
      • U.S. Citizen: An individual who is a citizen of the United States by law, birth or naturalization (https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=42-USC-630966247-802284531&term_occur=1&term_src=title:42:chapter:99:section:9102)
      • Permanent Resident: An immigrant/non-citizen who can legally reside in the United States in perpetuity (https://www.law.cornell.edu/wex/lawful_permanent_resident_(lpr))
      • Non-Citizen National: A person born in an outlying possession of the United States on or after the date of formal acquisition by the United States (https://www.law.cornell.edu/uscode/text/8/1408)
   2. Able to submit Form W-9 (Request for Taxpayer Identification)
   3. Affiliated with one of the following entities:
      • Higher Education Institutions
        • Public/State Controlled Institutions of Higher Education8
        • Private Institutions of Higher Education
        Individuals affiliated with the following types of Higher Education Institutions are always encouraged to apply:
        • Hispanic-serving Institution
        • Historically Black Colleges and Universities (HBCUs)
        • Tribally Controlled Colleges and Universities (TCCUs)
        • Alaska Native and Native Hawaiian Serving Institutions
        • Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)
      • Nonprofits Other Than Institutions of Higher Education
        • Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education)
        • Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)
      • For-Profit Organizations
        • Small Businesses
        • For-Profit Organizations (Other than Small Businesses)
      • From an institution that holds an active Data Use and Registration Agreement (DURA) with All of Us. Confirm DURA.
      • Willing to sign the Data User Code of Conduct (DUCC). This agreement outlines the program’s expectations for researchers who use the Researcher Workbench and describes how program data may be used. View the DUCC.
2. Education: Applicants can be post-baccalaureate or graduate students, postdoctoral fellows, medical students or residents, allied health trainees, early-career investigators, or early-career employees of non-academic institutions as defined in item 1C above. Applicants must hold at a minimum a bachelor’s degree from an accredited U.S. institution in one of the following or related fields:
   • Physical sciences (e.g. chemistry, physics)
   • Biological or life sciences (e.g. biology, zoology, biochemistry, microbiology)
   • Mathematics or statistics
   • Data science
   • Engineering
   • Health sciences (e.g. pharmacy, psychology, health information technology, nurses, therapists, social workers)
   • Public health (epidemiology, biostatistics, health administration, clinical implementation specialists)
3. Recommended Applicant Knowledge, Skills, and Experience:
   To ensure success in the training program, applicants must already possess certain skills, knowledge and experience. These include:
   • Prior programming experience
   • Basic understanding of statistics
   • A working command of English, as courses are taught in English
   Additionally, it is strongly recommended that applicants have some of the following skills and experiences to optimize their learning experience and better prepare them for the challenges of the program.
   • Successfully completed an undergraduate or graduate course in probability and statistics
   • Has practical experience in coding/programming with R or Python
   • Has experience in data manipulation and management gained through coursework and/or research projects
   • If the trainee plans to conduct a project using the All of Us genomics data, the trainee must have
     • Prior experience with genetics or computational biology
     • Practical experience with Bayesian analysis and maximum likelihood estimation
   Introductory or refresher courses on these topics will be available to successful applicants at the start of the traineeship, via the AIM-AHEAD Connect platform.

Applications are now closed.

A review committee comprised of AIM-AHEAD Consortium members will apply the following criteria to evaluate and prioritize applications. In assigning priority scores, reviewers will apply the standard NIH 1-9 scoring range to Criteria 1, where a score of 1 indicates highest enthusiasm, and a score of 9 indicates lowest enthusiasm, based on NIH Simplified Review Framework - https://grants.nih.gov/grants/guide/notice-files/NOT-OD-24-010.html

Criteria 1: 

1. 1. Articulation of Expectations and Reasons for Participation: Evaluate the clarity and depth with which the applicant articulates personal expectations and motivations for joining the program. Consider how convincingly the applicant communicates the necessity and significance of the training for their career or academic ambitions.
   2. Research Background and Motivation for Training: Assess the extent of the applicant’s relevant background, professional experience, or academic qualifications that support their readiness for this program. Gauge their motivation and potential to actively participate in and derive meaningful benefits from the training.
   3. Long-term Application of Training: Examine the specificity and feasibility of the applicant’s plans to apply AI/ML training in their research or professional development. Look for detailed strategies that indicate a commitment to integrating the training into their long-term career or academic objectives.
   4. Support from Supervisors or Mentors: Determine if the letter of support provides strong and unequivocal commitment from the supervisor, faculty, or mentor. It should confirm the provision of sufficient protected time for the applicant to fully engage with the training.
   5. Reference(s) and Assurance of Success: Critique the letter(s) of reference for their effectiveness in providing a persuasive argument that the applicant is well-prepared and likely to succeed in the program. The reference(s) should highlight relevant skills, accomplishments, and the applicant's capacity for advanced training.
   6. Community Engagement and Collaboration: Evaluate how well the applicant expresses a readiness to actively engage with the AIM-AHEAD community. Look for a demonstrated commitment to contributing to communal resources, empowering new users, and promoting a culture of diversity and inclusivity within the community.

Criteria 2: 

Prior Data Science Experience: Assess applicant data science experience by reviewing education, work history, programming proficiency, project involvement, and understanding of math/statistics applied in data analysis. 

To be evaluated by selecting one of the following options from a drop-down menu.

• • Beginner
  • Intermediate 
  • Expert

Applicants should expect notification of their acceptance status on Monday, January 6, 2025. Accepted applicants will receive an invite from PaymentWorks requesting:

• a valid tax ID (either an EIN or SSN) via W9 for U.S. vendors
• to upload a Bank Validation file for ACH/EFT or Wire Payments (https://community.paymentworks.com/payees/s/article/What-Is-A-Bank-Validation-File)

If an applicant applies and is accepted into more than one AIM-AHEAD training program, they must select only one to participate in per funding cycle. Additionally, AIM-AHEAD-funded Coordinating Center Personnel or AIM-AHEAD-funded Awardees (MPIs, PIs, Co-Investigators, other personnel) are eligible to apply, but if accepted, they will not receive the stipend and allowance.

The first informational webinar was Thursday, October 31, 2024

Watch the webinar recording here

Open the webinar slides here

AIM-AHEAD All of Us Training Program Co-Directors

Robert Mallet, PhD, Legand (Lee) Burge, PhD, Toufeeq Syed, PhD

Frequently Asked Questions

Please refer to the FAQs below before submitting a help desk ticket:

AIM-AHEAD All of Us Frequently Asked Questions

Please feel free to submit a help desk ticket if you have any questions:

AIM-AHEAD Training Programs HelpDesk