Real-time inventory logging from food donors. Automated data collection via mobile interface with timestamp and geolocation metadata.
Geospatial matching algorithms optimize donor-recipient pairs based on proximity, capacity, and food type compatibility. Machine learning models predict optimal routing.
Automated dispatch notifications to volunteer drivers. Real-time tracking and confirmation system ensures food reaches shelters efficiently.
Meal Lift was developed as a capstone project combining applied mathematics and mechanical engineering principles to address food insecurity. The creator is a student at the University of Utah, specializing in data-driven solutions for social impact.
With a background in Applied Math and Mechanical Engineering, this project represents a commitment to humanitarian engineering—using technical expertise to create systems that serve communities while demonstrating computational rigor.