DSFAS-CIN: HARNESSING MOBILITY BIG DATA AND ARTIFICIAL INTELLIGENCE THROUGH A TRANSDISCIPLINARY RESEARCH NETWORK IN FOOD PRODUCTION, PROCESSING, AND CONSUMPTION SYSTEMS

The goals of this project fall under four main categories. The first involves network building. We aim to establish a Coordinated Innovation Network (CIN) that promotes big data and Artificial Intelligence (AI) systems to conduct food systems research. This CIN will build a network of collaborators interested in using big data to research questions related to food production, processing, and consumption systems, exploiting a unique dataset of 200 million cell phone users with the help of AI systems, and completing three immediate research applications. We will bring together experts from multiple disciplines, including agricultural economics, geography, and computer science, to identify a synergistic solution to siloing in the agricultural and food sciences. The network of collaborators will include internal researchers from UConn's College of Agriculture, Health and Natural Resources (CAHNR), the School of Engineering, the College of Liberal Arts and Sciences, and the School of Business, as well as external collaborators from academic institutions and governmental agencies in the U.S. and abroad.The second goal involves research. We will strategically build on the unique data science collaborations at the University of Connecticut (UConn) to create transformative research that improves academic and policymaker understanding of the U.S. agriculture and food system. Specifically, we will use mobility big data to conduct food systems research, combining multiple datasets to answer questions related to producer marketing decisions, retailer networks, and consumer behaviors. This innovative research will be conducted through the CIN by designing, integrating, analyzing, and interpreting AI and data mining applications to the food system. We will leverage AI to address critical questions in food and agriculture by automating processes that create scalable insights from big geospatial and mobility data. This includes addressing topics spanning from agriculture and food retailing to consumer choices and health.The third goal involves data science. We will design multiple novel AI systems to acquire knowledge, iteratively extract information and learn from the mesmerizing patterns in our high-dimensional input data. We will develop new deep learning approaches, formulating a flexible and powerful way to learn from the training data as a nested hierarchy of concepts, where more complicated and high-level concepts will build on simpler ones. This includes designing data analysis techniques that lead to methodological advances that will enable a more efficient and scalable platform for researchers and stakeholders to discover new producer, processor, and consumer patterns from large mobility datasets, such as identifying frequent visitations from large-scale datasets for food market decision support.The fourth goal involves stakeholders and students. We will combine transdisciplinary network building and large-scale cyberinfrastructure development for novel insights in both data and agricultural sciences, improve food system collaborations, and provide policy-relevant research findings. We will also incorporate a training program that engages graduate and undergraduate students in multiple disciplines, ensuring graduates with workforce-ready skills. Finally, we will ensure strong stakeholder involvement in all project elements through collaborations with UConn's Department of Extension.