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.

加速实时分析是智能数据革命的前沿，一方面受到互联网连接传感器硬件进步的推动，另一方面加速人工智能/机器学习分析的推动。这些技术包括人工智能/机器学习、认知、量子、神经形态和边缘计算，与新型传感和可编程硬件组件、主动存储、闪存、内存云系统、嵌入式传感器等相结合。它们解决了国防和网络安全、天气和野火预测、健康和疾病监测、工业物联网等方面的许多追求。在IUCRC CARTA第二阶段，我们 将针对与我们的工业和机构合作伙伴相关的这些技术开发新的加速和实时方法。我们将利用我们在人工智能/机器学习和可编程网络系统方面的前沿研究来开展协作 CARTA 研究。我们的研究领域包括加速医学图像分析、用于医学和视觉/语言模型的可解释/可解释的人工智能/机器学习、用于加速动态系统的数字孪生、利用人工智能/机器学习进行基于物理的建模以提高理解、用于加速分析的可编程耦合软件和硬件。我们的研究将主要是“应用中立”。因此，我们将开发的技术可以通过修改应用于多个行业领域，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监督/无监督学习、联邦学习、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表来广泛传播与业界合作研究的结果。我们还建议与其他想要复制这种方法的非 CARTA 机构分享我们的研究结果。我们将利用针对代表性不足的本科生/研究生的计划，招募他们到我们的行业合作伙伴进行暑期实习并参与 CARTA 项目的研究。数据科学硕士项目将供学生进行 CAPSTONE 项目。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。