Food Nutrition & Innovation Council

Systemic change requires partnerships to foster innovative collaborations.

About the Council

The Innovation Institute brings together stakeholders from across the food value chain who have a common interest: to create a healthy, equitable, and sustainable food system.

Convening is a powerful tool. By making the effort to be together, we share ideas, teach each other new things, and inspire each other to see the world in new ways. At these convenings, we connect and value each other, and we find ways to collaborate, in ways both big and small.

Our Donors- thank you!

Platinum

3

Gold

3

Silver

3

Agrosuper
Ayana Bio
Azuluna Farms
Barilla, USA
Bay State Milling Co.
Branch Venture Group
Campbell’s
Clif Bar & Co.
Conti Ventures
Danone North America
Eat Well Global, Inc.
EatWell Meal Kits

Farmers Truck
FoodMinds
FoodShot Global
Food at Google
Fork Farms
General Mills
Good Measures
Hain Celestial Group
Haleon
Kenvue
Keurig Dr. Pepper
KIND Snacks

Kroger
Lipton Teas & Infusions
McCormick Science Institute
Notemeal
Nourished Rx
Olipop
PepsiCo
Pharmavite
Sifter Solutions
SOSV
The Well

Non-Profits

3

AARP Foundation
About Fresh
American Cancer Society
American Heart Association
American Society for Nutrition
The Angiogenesis Foundation
Center for Science in the Public Interest

CommonWealth Kitchen
Community Servings
The diaTribe Foundation
Dr. Yum Project
Eat REAL
FoodTank
Food Allergy Science Initiative
Global Liver Research Institute

Hunger to Health Collaboratory
Legacy Response
Milken Institute
Plant Based Foods Initiative
Seeding the Future
Teens for Food Justice
WANDA
Wello

Course

NUTR 390: Introduction to AI-Based Applications for Nutrition and Health Research (AIRNH) offers an overview of AI-based applications beneficial for health and nutrition research. It emphasizes the use of knowledge graphs, conceptual maps, and causal diagrams to enhance understanding and practical skills. The course also focuses on ethical considerations in using AI-powered tools, addressing the impacts of structural missingness on algorithmic bias, and exploring data representation and model transparency within the context of nutrition science and policy.