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

利用人工智能和机器学习（ML）的现代进步，可以加速科学和工程各个领域的研究。不幸的是，缺乏专家来帮助研究人员理解并将最新的ML功能集成到他们的工作中。这个拟议的项目旨在通过创建和维持一个网络基础设施（CI）专业人员（CIP）队列来解决对专家的这一关键需求，从而使研究人员能够有效地利用机器学习技术。通过将招募的人员纳入大型研究项目，同时提供量身定制的指导和培训计划，该项目旨在加强跨学科合作，促进创新。该项目符合国家促进科学进步和推进CI和ML领域发展的利益。该项目的智力优势在于其全面的方法为跨学科CI专业人员制定了长期的职业发展路径。通过个性化培训、指导和大型研究项目的实践经验，跨学科研究机器学习工程师和跨学科研究机器学习促进者将获得在他们的角色中脱颖而出的必要技能。通过建立一种机制，为雇用这些专业人员收取新的资助，该项目预计将在最初的四个职位之外增长，扩大队列的能力，以支持不同的研究领域。该项目强调生产有影响力的研究、技术论文、教程和培训材料，为更广泛的ML和CI社区做出贡献。此外，通过与非R1学院/大学、R2和少数民族服务机构以及两年制学院合作，该项目旨在促进CIP劳动力的多样性和包容性，确保所有机构都能从人工智能能力和CI支持中受益。开发的培训材料将公开提供，以支持跨各个领域的跨学科研究，并促进CI资源的使用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.