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

合作研究：SCIPE：人工智能和数据科学跨学科研究支持社区

• 批准号: 2320954
• 负责人: Karen Tomko
• 金额: $ 149.96万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320954&HistoricalAwards=false
• 关键词: Collaborative; Research; SCIPE; Interdisciplinary; Support

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.

通过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的每个领域的研究。不幸的是，缺乏专家来帮助研究人员理解最新的ML功能并将其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批网络基础设施(CI)专业人员(CIP)来满足这一对专家的迫切需求，以使研究人员能够有效地利用ML技术。通过将招聘人员嵌入大型研究项目，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和促进创新。这一倡议符合促进科学进步和推进传播与信息领域的国家利益。该项目的智力价值在于其全面的方法，为跨学科的CI专业人员发展长期职业发展道路。通过在大型研究项目中的个性化培训、指导和实践经验，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队伍将获得在其角色中脱颖而出所需的技能。通过建立一种机制，为雇用这些专业人员收取新的赠款，该项目预计将增长超过最初的四个职位，扩大队列的能力，以支持不同的研究领域。该项目强调制作有影响力的研究、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和为少数群体服务的机构以及两年制学院接触，该项目旨在促进CIP劳动力的多样性和包容性，确保所有机构都能受益于人工智能能力和CI支持。开发的培训材料将公开提供，以支持跨不同领域的跨学科研究，并促进CI资源的使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。