
Projects
Harnessing Mobility Big Data and Artificial Intelligence Through a Transdisciplinary Research Network in Food Production, Processing, and Consumption Systems

Active

Project Summary
The Coordinated Innovation Network (CIN) in big data and modern artificial intelligence (AI) will promote multidisciplinary research into food production, processing, and consumption systems in collaboration with several institutions, including academic, governmental, and international partners. The CIN will use big data to address food systems questions, exploit a unique mobility dataset of 200 million cell phone users, and complete three immediate research applications. Most agricultural AI innovations are limited to production data analysis, indicating a clear need for new research relying on big data beyond the farmgate and AI systems to answer research questions of national scope focusing on producer marketing, food retailing, and consumer choices. We will design a unique geospatial cyberinfrastructure that combines large-scale heterogeneous mobility data with multiple other sources. Spatial and temporal data filtering and indexing will facilitate frontier food systems research. We will construct location-based heterogeneous graphs to develop new insights regarding the producer, retailer, and consumer interactions and behaviors adopting modern AI systems. Trajectory mining will enable us to leverage data correlations and construct user visitation models. These novel data science applications will support multidisciplinary frontier research of CIN members. The CIN will be established and sustained through transdisciplinary training of undergraduate and graduate students, virtual networking, and an annual research symposium. Stakeholder engagement will be maintained in all project aspects. A user-friendly database that integrates visualization and AI tools will be made available through a dedicated web interface and GitHub.
Objectives
Design a novel information and integration system for mobility big data analysis.
Construct a location-based and dynamic heterogeneous network of mobile user interactions in food production, processing, and consumption systems.
Develop innovative dynamic heterogeneous neural network algorithms for classification, feature representation, and clustering.
Establish a sustainable, transdisciplinary research network for AI and big data in food systems research.
Develop a user-friendly web interface for transferability, visualization, stakeholder engagement, decision-making support tool, and model interpretation.
Funding Agency
National Institute of Food and Agriculture, AFRI Program
Project Members

Cristina Connolly (PI)
University of Connecticut

Dongjin Song
University of Connecticut

Sandro Steinbach
North Dakota State University

Tao Lu
University of Connecticut

Suining He
University of Connecticut

Debarchana Ghosh
University of Connecticut