Food Computing aims to acquire and analyze food data from disparate sources for recommending and monitoring food consumption as well as addressing food-related issues in medicine, biology, gastronomy, and agronomy. The availability of large-scale food datasets and recent advances in Food Computing can transform the way that individuals consume food. In this work, we investigate computational models that enables learning nutrition facts from food descriptions. Our research targets diet monitoring applications and may generate results
with significant public health impact.

Learning Objective: (1) learninig to develop effective computational models that accurately estimate nutrition facts of any given food
(2) learning to investigate if computational modeling of food ingredients can help better estimation of nutrition facts


Hadi Amiri (Presenter)

Andrew Beam, Harvard
isaac kohane, Harvard

Presentation Materials: