PARTNER: Innovating AI for efficient and insightful data transformation

该项目是波多黎各马亚圭兹大学(UPRM)和人工智能与基础交互人工智能研究所(IAIFI)之间的扩展人工智能合作伙伴关系。在该项目中，一家少数群体服务机构领导与一家人工智能研究所的新合作，专注于扩大波多黎各马亚圭斯大学已经建立的研究和教育项目，并围绕发展人工智能为社会服务以及培养下一代人工智能教育和劳动力人才追求共同的、互补的目标。合作研究的重点是人工智能的发展，为高能物理实验的突破做出贡献。该项目还将在以前没有很好开展此类活动的地方建立社区和新的人工智能卓越中心。当今时代是科学发现的见证，人工智能(AI)在洞察数据方面发挥着决定性作用。最近深度学习技术的爆炸式增长与不断发展的计算体系结构相匹配，使人工智能工具能够高效地处理比以前更高维度和更复杂的问题。像大型强子对撞机这样的高能物理实验收集的复杂、高维数据为物理学和人工智能的融合研究提供了条件。这项研究围绕以下人工智能主题构建：深度学习中的最优传输、物理信息不确定性量化、稀疏词典学习和深度学习的计算方面。其目的是通过将低级别、高维空间中的信息转换为更高级别、更低维的表示来增强数据约简。它还将通过增强稀疏学习、分类、图像超分辨率、光谱分解和分割而有利于成像。该项目强调包容性参与式设计，以促进人工智能创新的积极社会成果。UPRM和IAIFI之间研究伙伴关系的一个关键目标是显著增加UPRM在人工智能研究、教育和劳动力发展方面的参与。它利用了UPRM正在进行的关于大型强子对撞机上的紧凑型Muon螺线管实验的研究，人工智能成像小组在高光谱和生物医学成像中的人工智能研究和应用工作，以及IAIFI关于通过新方法推进基础人工智能的专业知识。该项目的部分资金来自NSF在STEM卓越公平部门内的Louis Stokes少数群体参与联盟(LSAMP)计划。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。

This project is an ExpandAI Partnership between the University of Puerto Rico Mayaguez (UPRM) and the AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) In this project, a minority-serving institution leads a new collaboration with an AI Institute focused on scaling up already-established research and education programs at UPRM and to pursue shared, complementary goals around developing AI with use for society in mind and for developing the next generation of AI education and workforce talent. The collaborative research focuses on the development of AI for contributing to breakthroughs in high-energy physics experiments. The project will also build community and new centers of excellence in AI where such activities were not previously well developed. The present era is witnessing scientific discoveries where Artificial Intelligence (AI) plays a decisive role in giving insights into data. The recent explosion of deep learning techniques matched by continuously evolving computing architectures has enabled AI tools to handle higher-dimensional and more complex problems than previously achievable efficiently. The complex, high dimensional data collected by high-energy physics experiments like those at the Large Hadron Collider provide for convergent research in physics and AI. This research is structured around the following AI themes: Optimal Transport in Deep Learning, Physics Informed Uncertainty Quantization, Sparse Dictionary Learning, and Computational aspects of Deep Learning. The intent is to enhance data reduction by transforming the information in the low-level, high-dimensional space into a higher-level and lower-dimensional representation. It will also benefit imaging by enhancing sparse learning, classification, image super-resolution, spectral unmixing, and segmentation. The project emphasizes inclusive participatory design to promote positive societal outcomes of AI innovation. A key goal of the research partnership between UPRM and IAIFI is to significantly increase participation of UPRM in AI research, education, and workforce development. It leverages ongoing research at UPRM on the Compact Muon Solenoid Experiment at the LHC, the Artificial Intelligence Imaging Group’s work on AI research and applications in hyperspectral and biomedical imaging, and IAIFI expertise on advancing foundational AI through novel approaches. The project is partially funded by NSF’s Louis Stokes Alliances for Minority Participation (LSAMP) program within the Division of Equity for Excellence in STEM.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.