Food Safety and Processing Data Aggregation for AI Development
Description
Data in food processing, and particularly food safety, while abundant, is siloed in individual firms, plants, and labs, and not well integrated for big data approaches like AI development. This project aims to develop deeper collaboration within companies and the research community to build out AI infrastructure in the following areas: food safety, Federated Learning for collaborative and privacy-preserving learning, and inventory strategies to improve supply chain resilience. The project’s broader impact will be the food safety testing database for public-domain food safety data. This will meet an unmet need for data availability, and could become a widely used data exploration tool.
Team
Chenhui Shao
Principal Investigator
Matt Stasiewicz
Co Principal Investigator
Publications
A Perspective on Data Sharing in Digital Food Safety Systems
Nationwide Genomic Atlas of Soil-dwelling Listeria Reveals Effects of Selection and Population Ecology on Pangenome Evolution
Federated Learning-based Semantic Segmentation for Pixel-wise Defect Detection in Additive Manufacturing
A Greedy Agglomerative Framework for Clustered Federated Learning
Statistical Analysis of the Long-Term Influence of COVID-19 on Waste Generation-A Case Study of Castellón in Spain
Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production
Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water
