FoodAtlas is an expanding knowledge graph that aims to capture comprehensive relationships between foods and other entities, including chemicals and diseases. Utilizing powerful large language models, FoodAtlas can parse millions of scientific articles to efficiently extract relationships pertaining to foods, while simultaneously assigning quality scores to each relationship based on the source’s trustworthiness.

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AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pre-trained models, as well the ability to generate synthetic data and annotations.

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Agricultural and food processing byproducts are an untapped resource for supporting circular systems. The byproduct database (BPDB) maps and quantifies these byproducts, unlocking their potential for reuse in industries like nutraceuticals, cosmetics, and more.

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Postdoc