Seed Grants for AI/ML in Public Health Research

The Celia Scott Weatherhead School of Public Health & Tropical Medicine (WSPH) in partnership with Connolly AlexanderInstitute for Data Science (CAIDS) ignites innovation at the intersection of technology and population health. We invite faculty-led teams to submit proposals for innovative, interdisciplinary AI/ML projects with direct public-health impact. These awards are designed to catalyze early-stage ideas, grow collaborations across schools, mentor Tulane students, and position teams for external funding.


This program is accepting proposals until Friday, October 17, 2025 (11:59 PM CDT). See below for eligibility and information about the program.
 

Submit a Proposal for Fall 2025 
 

Project Purpose

The WSPH and CAIDS invite faculty-led teams to submit proposals for seed grants supporting innovative, interdisciplinary research in artificial intelligence (AI) and machine learning (ML) with direct applications to public health. These grants aim to foster collaboration across schools, accelerate early-stage ideas, and position teams for external funding, while also targeting the meaningful engagement and mentoring of Tulane students. 


Project Sponsorship

The WSPH Seed Grant funds $2,000 to $30,000 for direct project costs and may request to have student RA funding support provided by CAIDS in addition to the seed ceiling. The project period will last 12–18 months, with the earliest possible start date of November 15, 2025.

Allowable costs include Student RAs, data purchase or collection, specialized software or modest equipment (<$5,000), participant incentives (following WSPH procedures), travel for collaboration or data collection, and dissemination activities. Funds will not support the following: Faculty salaries, indirect costs, institutional overhead, open access publication fees.  


Eligibility and Project Scope

Fulfill the following to be eligible: 

  • Lead PI must be full-time Tulane faculty
  • At least one investigator with a primary appointment in WSPH
  • One submission per lead PI per cycle
  • Interdisciplinary teams strongly encouraged (e.g., public health + CS/engineering/medicine/social science)

Additionally, projects should leverage AI/ML methods to advance population health, health-systems performance, or health equity. Priority areas include (but are not limited to):

  • Predictive modeling for infectious-disease surveillance or chronic disease risk
  • Climate change or environmental exposure analytics
  • Precision public health interventions and decision support
  • Ethical AI, bias mitigation, or data privacy in health contexts
  • AI-enabled optimization of healthcare access or supply chains 


Application and Submission Process

Submit a single PDF that contains the following:

  • Cover Page: Project title, PI/co-PI names, departments/units, contact info
  • Abstract (≤ 250 words)
  • Research Plan (3 pages max): objectives, methods, expected outcomes, external funding trajectory
  • Budget + Justification: Itemized expenses with rationale. Clearly separate (a) WSPH Seed-Grant costs and (b) CAIDS RA costs. Please also specify any other current funding sources, including applications under review (which are viewed favorably)
  • Collaboration & Student Engagement Plan (≤ 1 page): roles of each investigator and approach for recruiting, training, mentoring and supervising student RAs
  • Abbreviated Biosketches/CVs (≤ 2 pages each)  
  • Optional: Letters of support  

Email the proposal in PDF format titled as “WSPH-AI_LastName_Title.pdf ” to phutchin@tulane.edu with the subject line “AI/ML Seed Grant – [PI Last Name]”. 


Key Dates

CFP Release Date: September 1, 2025  
Submission Deadline: Friday, October 17, 2025 (11:59 PM CDT) 
Award Notification: early November 2025
Earliest Project Start:  November 15, 2025 


For questions, contact:   

  • Paul Hutchinson, Professor, Celia Scott Weatherhead School of Public Health & Tropical Medicine (WSPH)
  • Joey Couvillon, Senior Program Manager, Connolly Alexander Institute for Data Science (CAIDS),