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: 2320952
• 负责人: Vipin Chaudhary
• 金额: $ 249.64万
• 依托单位: Case Western Reserve 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=2320952&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，可以加快科学和工程领域的研究。 不幸的是，缺乏专家来帮助研究人员理解并将最新的ML功能整合到他们的工作中。 该提议的项目旨在通过创建和维持一系列网络基础设施（CI）专业人员（CIP）来满足对专家的关键需求，以使研究人员有效利用ML技术。 通过在提供量身定制的指导和培训计划的同时，将招聘人员嵌入大型研究项目中，该项目旨在增强跨学科的合作和促进创新。该计划与促进科学进步并推进CI和ML领域的国家利益相吻合。该项目的智力优点在于其全面的方法是为跨学科的CI专业人员开发长期职业发展道路。通过在大型研究项目中的个性化培训，指导和动手经验，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的队列将获得必要的技能，使其扮演其角色。通过创建一种机制来为雇用这些专业人员收取新的赠款，该项目预计增长超出了最初的四个职位，从而扩大了同伙支持各种研究领域的能力。该项目强调了有影响力的研究，技术论文，教程和培训材料的生产，这为更广泛的ML和CI社区做出了贡献。此外，通过与非R1学院/大学，R2和少数族裔服务机构以及两年学院互动，该项目旨在促进CIP劳动力中的多样性和包容性，以确保所有机构都可以从AI能力和CI支持中受益。开发的培训材料将公开可用，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NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。