Center: IUCRC Phase II Rutgers University: Center for Accelerated and Real Time Analytics (CARTA)

中心：IUCRC 第二阶段 罗格斯大学：加速和实时分析中心 (CARTA)

• 批准号: 2310966
• 负责人: Dimitris Metaxas
• 金额: $ 50万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing 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=2310966&HistoricalAwards=false
• 关键词: Center; IUCRC; Phase; II; Rutgers

Accelerated and real-time analytics is a leading edge of the smart data revolution, pushed by advances in internet-connected sensor hardware on one side and accelerated AI/ML analytics on the other. These technologies include AI/ML, cognitive, quantum, neuromorphic, and edge computing combined with new classes of sensory and programmable hardware components, active storage, flash storage, in-memory cloud systems, embedded sensors, etc. They address many pursuits of national defense and cybersecurity, weather and wildfire prediction, health- and disease-monitoring, Industrial IoT, etc. In IUCRC CARTA Phase II, we will develop new accelerated and real-time approaches to these technologies relevant to our industrial and agency partners. We will leverage our cutting-edge research in AI/ML and programmable cyber-systems to conduct collaborative CARTA research. Our research areas include accelerated medical image analytics, explainable/interpretable AI/ML for medical and vision/language models, digital twins for dynamical systems with acceleration, and physics-based modeling with AI/ML for improved understanding, programmable coupled software and hardware for accelerated analytics. Our research will be primarily “application neutral.” Therefore, the techniques we will develop can be applied with modifications across multiple industry sectors, including cyber security, healthcare and medical, dynamic systems, environment, and business intelligence. The technologies we will explore include semi/un-supervised learning, federated learning, programmable architectures, language/vision models, real-time simulations, and blockchains. Our project will benefit businesses by providing a larger pool of highly skilled workforce. We will disseminate the results of this collaborative research with industry widely by publishing them in appropriate journals and conferences. We also propose to share the results of our research with other non-CARTA institutions that want to duplicate this approach. We will leverage our programs for underrepresented undergraduate/graduate students to recruit them for summer internships at our industry partners and research in CARTA projects. The MS in Data-Science Program will be used for students to do CAPSTONE projects.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.

加速和实时分析是智能数据革命的前沿，一方面是Internet连接的传感器硬件的进步，另一方面加速了AI/ML分析。 These technologies include AI/ML, cognitive, quantum, neuromorphic, and edge computing combined with new classes of sensory and programmable hardware components, active storage, flash storage, in-memory cloud systems, embedded sensors, etc. They address many pursuits of national defense and cybersecurity, weather and wildfire prediction, health- and disease-monitoring, industrial IoT, etc. In IUCRC CARTA Phase II, we将开发与我们的工业和代理合作伙伴相关的这些技术的新加速和实时方法。我们将利用AI/ML和可编程网络系统的尖端研究来进行协作CARTA研究。我们的研究领域包括加速的医学图像分析，用于医学和视觉/语言模型的可解释/可解释的AI/ML，用于加速的动态系统的数字双胞胎以及具有AI/ML的基于物理的建模，可改善理解，可编程耦合软件和硬件，用于加速分析。我们的研究将是主要的“应用程序中性”。因此，我们将开发的技术可以通过多个行业的修改，包括网络安全，医疗保健和医疗，动态系统，环境和商业智能。我们将探索的技术包括半/无监督的学习，联合学习，可编程架构，语言/视觉模型，实时模拟和区块链。我们的项目将通过提供更多高技能的劳动力来使企业受益。我们将通过在适当的期刊和会议上发布这项与行业的合作研究结果传播。我们还建议与想要复制这种方法的其他非贸易机构分享我们的研究结果。我们将利用我们的计划来使代表性不足的本科/研究生在我们的行业合作伙伴和CARTA项目研究中招募暑期实习。数据科学课程将用于学生进行顶峰项目。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。