CC* CIRA: Mid-TN AI for Interdisciplinary Imaging Interpretation Alliance
CC* CIRA: Mid-TN AI for Interdisciplinary Imaging Interpretation Alliance
批准号:
2321684
负责人:
Daniel Moyer
金额:
$20.0万
依托单位:
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
项目状态:
未结题
起止时间:
2023-08-01 至 2025-07-31
中文摘要
该项目促进了田纳西州中部以人工智能(AI)成像为中心的新区域合作。这些规划和协调项目的一个主要目标是,通过举办社区活动、为学生举办培训和辅导讲习班,以及分享教学和研究活动的计算资源,促进以研究为重点的机构与为少数群体服务的机构之间的联系。通过Mid-TN AI for Interdisciplinary Imaging Interpretation Alliance(AI4A),该项目识别、表征并实现了成像AI科学的驱动因素以及AI4A中此类科学的能力。AI4A主办夏季教师奖学金,种子合作,和夏季学生的机会,与现有的REU计划,如菲斯克桥NSF REU合作博士前学员参与。AI4A为AI成像所需的技术技能开发培训材料,包括GPU计算,深度学习概念和实践,以及当前高性能计算的计算环境。AI4A还提供了访问这些设备和环境,以支持研究合作,并为学生提供大型模型开发和验证的实践经验。 该项目促进了从头研究方向的出现,但基于参与者当前的研究重点,并基于放射学,神经病学和神经科学,细胞显微镜,生态学和遥感,运输,材料工程,考古学,数字人类学,该奖项反映了NSF的法定使命,并通过使用基金会的智力价值进行评估,更广泛的影响审查标准。
英文摘要
This project facilitates new regional collaborations in middle Tennessee centered on artificial intelligence (AI) for imaging. A primary goal of these planning and coordination project is to facilitate connections between research-focused institutions and minority serving institutions by hosting community events, training and tutorial workshops for students, and sharing computational resources for both pedagogy and research activities. Through the Mid-TN AI for Interdisciplinary Imaging Interpretation Alliance (AI4A), the project identifies, characterizes, and enables drivers of imaging AI science and the capacity for such science across the AI4A.A critical component of this project is the development of human infrastructure and interconnections. AI4A hosts summer faculty fellowships to seed collaborations, and summer student opportunities to engage with pre-doctoral trainees in collaboration with existing REU programs such as the Fisk Bridge NSF REU. AI4A develops training materials for the technical skills required in AI-imaging, including GPU computing, deep learning concepts and practice, and current computing environments for high performance computing. AI4A also provides access to these devices and environments to support research collaborations and to provide practical experience for students in the development and validation of large models. The project facilitates the emergence of de novo research directions, but based on the current research foci of participants, and based around imaging problems in radiology, neurology and neuroscience, cell microscopy, ecology and remote sensing, transportation, materials engineering, archeology, digital anthropology, and deep networks.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.
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