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Research Project: Student Research Opportunities to Expand AI and Data Science Applications in ARS Research (UC-Davis)

Location: Office of Associate Administrator

Project Number: 0500-00110-001-003-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 30, 2022
End Date: Sep 29, 2024

Modern agricultural research is a highly multidisciplinary enterprise that increasingly requires sophisticated computational and statistical methods to analyze datasets that are rapidly expanding in size, scope, and complexity. As such, agricultural science often benefits from an interdisciplinary team approach that brings together deep domain expertise and strong data science and analytics skills. The overall goal of this partnership is to advance ARS research efforts and enhance the Cooperator’s student training programs by providing fellowships that allow students with advanced data analytics and/or machine learning skills to collaborate with ARS research teams working on data-intensive research problems. Our specific objectives are four-fold. First, we will advance ARS research efforts by allowing Cooperator-affiliated graduate students with strong, applied skills in data science and related fields (e.g., computer science, artificial intelligence, machine learning, statistics) to join and contribute these skills to ARS research teams. Second, we will enhance participating students’ educational experiences by providing paid, hands-on, real-world research opportunities, scientific domain expertise, and mentoring from ARS scientists. Third, we will boost the Cooperator’s affiliated departments and programs by increasing the breadth of training they are able to offer to their students. Finally, we will support future ARS workforce development efforts by increasing student awareness of ARS and agricultural research as a rewarding career path for data scientists.

ARS has deep scientific expertise across a wide spectrum of agricultural research domains, spanning biological disciplines from landscape ecology down to molecular biology, engineering, chemistry, and more. ARS also has extensive scientific computing infrastructure, called SCINet, that includes multiple high-performance computing (HPC) clusters, high-performance and archival storage systems, and a high-speed networking backbone. The Cooperator oversees an AI Institute focused on food systems that is a collaboration among six research entities, including four major research universities. The Cooperator and the other land grant universities in the collaborative AI institute all house strong academic programs in computer science, engineering, and a wide spectrum of agricultural science fields.