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Research Project: Student Research Opportunities to Expand AI and Data Science Applications in ARS Research (New College FL)

Location: Office of Associate Administrator

Project Number: 0500-00110-001-002-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 a 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 by providing summer and spring fellowships that allow students with advanced data analytics 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 graduate students in the Cooperator’s Applied Data Science Program to join and contribute their 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 Applied Data Science Program 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 has strong academic programs in data science, including a 2-year, 36-credit Masters in Applied Data Science program. This program, offered by the Cooperator since 2015, includes various practical components such as a summer internship program and a full-semester spring Practicum program that allows graduate students to participate in real-word research and business projects that require use of data science techniques with various (big) data sets. This collaboration will seek to recruit students from this MS program for summer internship as well as spring practicum opportunities with ARS researchers and research projects. Each component is typically administered under the supervision of a faculty advisor in collaboration with a participating organization, ARS in this case. While students support scientific research at ARS with their data science knowledge and skills, the faculty member will work in an advising capacity to help ensure that the goals of each project are met. We will develop a process to match graduate students who have strong skills in data science and related fields with ARS research units and ARS researchers who will serve as mentors for the students during paid, 10-week summer fellowships or 12-week spring fellowships. Students will work with their ARS mentor (or mentors) to contribute their expertise to active ARS research efforts while also learning new research skills and domain knowledge. Students will also be co-mentored by faculty at their home institution during their fellowships. Student participants will have full access to scientific computing infrastructure and training resources provided by SCINet and the ARS AI Center of Excellence (AI-COE). We anticipate that most fellowships will follow a hybrid work model in which students receive 2 weeks of travel support to visit their assigned ARS unit and mentor(s) and spend the remainder of their fellowship working remotely from their home or home institution.