Collaborative Research: SCIPE: Interdisciplinary Research Support Community for Artificial Intelligence and Data Sciences

Research in every field of Science and Engineering can be accelerated by leveraging modern advancements in Artificial Intelligence and Machine Learning (ML). Unfortunately, there is a lack of experts to help researchers understand and integrate the latest ML capabilities into their work. This proposed project aims to address this critical need for experts by creating and sustaining a cohort of cyberinfrastructure (CI) professionals (CIP) to empower researchers to effectively leverage ML techniques. By embedding the recruited personnel into large research projects while providing tailored mentoring and training programs, the project aims to enhance interdisciplinary collaboration and foster innovation. This initiative aligns with the national interest of promoting the progress of science and advancing the fields of CI and ML. The project's intellectual merit lies in its comprehensive approach to developing long-term career development paths for interdisciplinary CI professionals. Through personalized training, mentoring, and hands-on experience in large research projects, the cohort of Interdisciplinary Research Machine Learning Engineers and Interdisciplinary Research Machine Learning Facilitators will gain the necessary skills to excel in their roles. By creating a mechanism to charge new grants for employing these professionals, the project anticipates growth beyond the initial four positions, expanding the cohort's capacity to support diverse research domains. The project emphasizes the production of impactful research, technical papers, tutorials, and training materials, contributing to the broader ML and CI communities. Furthermore, by engaging with non R1 colleges/universities, R2 and minority-serving institutions, and two-year colleges, the project aims to foster diversity and inclusivity in the CIP workforce, ensuring that all institutions can benefit from AI capabilities and CI support. The training materials developed will be openly available to support interdisciplinary research across various domains and promote the use of CI resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.