CC* CIRA: Mid-TN AI for Interdisciplinary Imaging Interpretation Alliance

该项目促进田纳西州中部以成像人工智能 (AI) 为中心的新区域合作。这些规划和协调项目的主要目标是通过为学生举办社区活动、培训和辅导研讨会以及共享教学和研究活动的计算资源，促进以研究为重点的机构和少数族裔服务机构之间的联系。通过 Mid-TN AI 跨学科成像解释联盟 (AI4A)，该项目识别、描述和支持成像 AI 科学的驱动因素以及整个 AI4A 中此类科学的能力。该项目的一个关键组成部分是人类基础设施和互连的发展。 AI4A 举办暑期教师奖学金以促进合作，并为暑期学生提供与博士前学员合作的机会，与 Fisk Bridge NSF REU 等现有 REU 项目合作。 AI4A 开发人工智能成像所需技术技能的培训材料，包括 GPU 计算、深度学习概念和实践以及高性能计算的当前计算环境。 AI4A 还提供对这些设备和环境的访问，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、神经学和神经科学、细胞显微镜、生态学和遥感、交通、材料工程、考古学、数字人类学和深层网络中的成像问题。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和更广泛的影响审查标准进行评估，被认为值得支持。

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.