UMD Researchers Receive USDA Funding to Use Big Data Analytics and Machine Learning
The University of Maryland (UMD) recently received a grant from the United States Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA) to develop a next-generation food safety risk assessment model by combining emerging techniques in both food safety and machine learning. With vast genomic data on foodborne pathogens such as Salmonella now available through whole genome sequencing, there exists the new potential to substantially improve public health through more specific food safety risk assessments that can better predict the risk of outbreaks and guide strong risk management decisions at the policy level. However, big data analytics approaches such as artificial intelligence (AI) and machine learning have yet to be leveraged in the field of food safety to integrate this genomic data with pathogen characteristics of interest to risk assessors. With this new funding, UMD is paving the way to a more robust food safety risk assessment model that combines computational techniques, genomic and microbial data, and machine learning to improve the management of foodborne illness and better protect public health.
“I am so excited about this grant award, as it gives us the resources to conduct cutting-edge research on food safety and risk assessment, which is very important in controlling foodborne diseases and protecting public health,” says Abani Pradhan, associate professor in Nutrition and Food Science at UMD and lead investigator of this work. “AI as an emerging technology can take advantage of big data available in the agriculture and food sectors and has the potential to integrate food production, processing, food safety risk factors, and genomic data that can transform public health strategies to prevent foodborne diseases and rapidly respond to outbreaks.”