Martin Wiedmann

This project tackles four bottlenecks in digital food safety across the farm-to-table supply chain by developing algorithms, data standards, and frameworks for privacy and text analytics. Focus areas include: 1) Active learning for monitoring optimization, 2) Standardized data for sharing, 3) Privacy-guaranteed data sharing, and 4) Machine learning for text analysis.

Researchers investigates the potential of data-driven innovation in achieving digital "smart" food safety by interviewing industry leaders about sharing safety data. The team will then create curated datasets and develop digital twins for food systems which will integrate into a comprehensive retail food safety and spoilage model for dynamic risk assessment.

The proposed multidisciplinary project will develop a framework for an integrated smarter food safety that achieves a vertical integration of AI systems for food safety across the supply chain in order to predict and assess the food safety risk in the food product at consumption (at the end of the supply chain) based on a holistic view of the entire supply chain.