Ethan Ligon

Our goal is to deliver healthy foods at low cost to improve nutrition for all Americans. Advances in chemistry reveal thousands of unique food molecules. This project extends ML models to include the gut microbiome and explores cost-health trade-offs in dietary choices.

Utilizing AI, researchers aim to enhance real-time dietary assessment using photo diaries. The project's long-term goal is to develop modules linking food photos to ingredients and ingredients to health outcomes, incorporating novel data types like the glycome of food and hierarchical dietary representations for improved predictions.

The project aims to develop digital twin and machine learning algorithms for optimizing LED-driven pod-based hydroponic farming, with a focus on addressing plant pathogen challenges, offering potential impacts on predicting pathogen spread, decontamination, simulating hydroponic systems, and revolutionizing the food processing industry if successful.