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Data and analytics

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Agri-food and health informatics is essential for understanding the complexity of the data that technology is delivering. The ability to utilize that data for design, and for identifying risks, resilience, and potential failures is a rich opportunity for design.

With the capacity for evaluating data, decision processes and creating strategic efficiencies in labor, production, processing, distribution become more rapid and better grounded. Artificial Intelligence and Machine Learning are the basis of such innovations. The increased expectation for sharing data sets from federally funded projects means larger and more data resources are available, reducing the requirement for original field work for every research question, and leading to more applied research studies on evaluation and decisions and rapid guidance for stakeholders.

Meet the platform leader

Song li

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Dr. Li is an associate professor in the school of plant and environmental sciences. His research group develops machine learning methods for high throughput phenotyping and genomic data analysis. His research interest lies in using artificial intelligence to translate large datasets from applied and basic research projects into actionable predictions in agriculture production. 

“I am interested in identifying innovative solutions using artificial intelligence in agriculture. I will help CAIA members to connect with experts from other research domains to facilitate interdisciplinary collaboration.”