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

加速和实时分析是智能数据革命的前沿，一方面是互联网连接传感器硬件的进步，另一方面是加速的AI/ML分析。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，结合新型传感器和可编程硬件组件、主动存储、闪存、内存云系统、嵌入式传感器等，可满足国防和网络安全、天气和野火预测、健康和疾病监测、工业物联网等诸多需求。我们将开发新的加速和实时方法，以实现与我们的工业和机构合作伙伴相关的这些技术。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，开展合作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AI/ML，用于加速动态系统的数字双胞胎，以及基于物理的AI/ML建模，以提高理解，可编程耦合软件和硬件加速分析。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在多个行业领域进行修改，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/无监督学习、联邦学习、可编程架构、语言/视觉模型、实时仿真和区块链。我们的项目将通过提供更大的高技能劳动力库而使企业受益。我们将通过在适当的期刊和会议上发表与工业界广泛传播这项合作研究的结果。我们还建议与其他希望复制这种方法的非CARTA机构分享我们的研究结果。我们将利用我们的计划，为代表性不足的本科生/研究生招募他们在我们的行业合作伙伴和研究在CARTA项目的暑期实习。该奖项反映了NSF的法定使命，并被认为是值得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的支持。

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.