Feb. 24, 2026
Be part of a nationwide effort to advance food security innovation! The National Food Security Innovation Hub (NAFSI) will host its inaugural Student Data Challenge April 10-13, 2026.
This competition will select up to 50 student teams to tackle real-world food security issues using a wide set of digital assets on the National Research Platform. These assets were carefully curated and assembled by the NAFSI Hub to address major data gaps in food security research, support predictive modeling, and advance community-focused solutions.
Challenge tracks
- Track 1: Predictive Modeling for Agricultural Resilience
- Track 2: Building Data Tools for Targeted Nutrition Interventions
Why participate?
- Compete for a cash prize of $2,000
- Network with industry partners
- Collaborate in teams of 3-5 to solve real-world issues
- Access dedicated workspace at participating campuses
Get started
Students interested in competing should register for the virtual informational session at 4 p.m. March 9.
About the National Food Security and Innovation Hub
The National Food Security & Innovation (NAFSI) Hub advances food security by unifying the nation’s most impactful food systems data and solutions into one powerful, accessible hub. Our mission is to accelerate innovation, foster cross-sector collaboration, and empower researchers, industry, policymakers, and communities with cutting-edge data, tools, and insights to create resilient and sustainable food systems. NAFSI is the central data hub serving Track J: Food & Nutrition Security Phase 2 projects of the NSF Convergence Accelerator. We aggregate and steward diverse, high-impact datasets — including real-time sensor outputs, AI/ML tools, digital twins, and geospatial dashboards with varied levels of security and privacy to serve industry needs — produced by seven groundbreaking, cross-disciplinary teams. By standardizing and centralizing these assets, NAFSI enables seamless access to data-driven solutions that accelerate discovery and impact across the food security landscape.