NRT: Improving strategies for hunger relief and food security using computational data science

North Carolina Agricultural & Technical State University, Greensboro NC

Investigators

Abstract

Food insecurity occurs when individuals have limited access to safe and nutritious food for an active, healthy life. It is a global issue that affects a significant number of individuals domestically. To address it, humanitarian organizations involved in hunger relief work collaboratively with the government and private sector. Humanitarian organizations rely on uncertain sources of supply, respond to uneven and variable needs, and make careful decisions regarding how to use scarce resources. These organizations generate data on a massive scale about food supply, food distribution, and food need, which helps them to perform their function. Challenges arise from not only the uncertainty of supply and demand but also from variations in information collected by different organizations. This National Science Foundation Research Traineeship (NRT) award to North Carolina Agricultural and Technical State University will develop an innovative, interdisciplinary training model in data science designed to grow the workforce that will help these organizations analyze their efforts and improve the provision of food aid at the local, state, and federal level. This traineeship seeks to provide a unique and comprehensive training experience for a total of 50 masters and doctoral students, including forty-five (45) funded trainees, by combining disciplines in industrial and systems engineering, computer science, mathematics, agricultural economics, sociology, and public policy. At present, no formal training mechanism exists by which students can acquire the interdisciplinary knowledge needed to derive insight from heterogeneous big data generated by the food aid supply chain because (a) traditional STEM student training in this area does not include the public policy perspective and (b) working with big data is limited to a subset of the STEM population. To answer this need, this project's research and education efforts will use existing data from the domestic humanitarian hunger relief supply chain as the basis for an innovative, evidence-based, scalable approach to training its future workforce. Our program will provide a model for preparing the next generation of data scientists employed in the humanitarian sector. The overarching goal is two-fold: (i) to create a sustainable training model that improves the preparation of students in STEM fields to pursue careers in data science and (ii) to provide training experiences that will orient students on the use of big data to inform and potentially transform the delivery of services that have societal impact. We propose to leverage big data to reduce uncertainty in this supply chain and drive more effective modes of food distribution by addressing information inequality, visualization, and information-driven decision modeling. The program will feature a summer training institute, industry/academia research clusters, professional development seminars, and ongoing evaluation of the training model. Students who complete all requirements will obtain new knowledge and skills with respect to big data collection, interpretation, and its use in decision-making; obtain new knowledge about using big data to address multidisciplinary problems in hunger relief and food security; and obtain the training necessary to pass an industry-level certification exam. At North Carolina Agricultural and Technical State University, the award will help to establish a new certificate-based interdisciplinary graduate training program in data science. In the larger humanitarian sector, the research generated by this work will improve access to food by reducing information inequality, enhance operational decision-making by providing real-time adaptable visualization of information, and create new information-driven decision models that can positively impact food aid policy and operations. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This project is co-funded by the Alliances for Graduate Education and the Professoriate (AGEP) program. AGEP funds research and the development, implementation, and investigation of models to transform the dissertation phase of doctoral education, postdoctoral training and/or faculty advancement of historically underrepresented minorities (URMs) in Science, Technology, Engineering and Mathematics (STEM) and/or STEM education research.