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)

基本信息

  • 批准号:
    2310966
  • 负责人:
  • 金额:
    $ 50万
  • 依托单位:
  • 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

项目摘要

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。

项目成果

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Dimitris Metaxas其他文献

A frame-based model for large manufacturing databases
Algorithmic issues in modeling motion
运动建模中的算法问题
  • DOI:
    10.1145/592642.592647
  • 发表时间:
    2002
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Pankaj K. Agarwal;Leonidas J. Guibas;H. Edelsbrunner;Jeff Erickson;M. Isard;Sariel Har;J. Hershberger;Christian Jensen;L. Kavraki;Patrice Koehl;Ming Lin;Dinesh Manocha;Dimitris Metaxas;Brian Mirtich;David Mount;S. Muthukrishnan;Dinesh Pai;E. Sacks;J. Snoeyink;Subhash Suri;Ouri E. Wolfson;Merl Mirtich@merl Com
  • 通讯作者:
    Merl Mirtich@merl Com
A combustion-based technique for fire animation and visualization
  • DOI:
    10.1007/s00371-007-0162-3
  • 发表时间:
    2007-06-28
  • 期刊:
  • 影响因子:
    2.900
  • 作者:
    Kyungha Min;Dimitris Metaxas
  • 通讯作者:
    Dimitris Metaxas
Multi-Stage Feature Fusion Network for Video Super-Resolution
用于视频超分辨率的多级特征融合网络
  • DOI:
    10.1109/tip.2021.3056868
  • 发表时间:
    2021-02
  • 期刊:
  • 影响因子:
    10.6
  • 作者:
    Huihui Song;Wenjie Xu;Dong Liu;Bo Liu;Qingshan Liu;Dimitris Metaxas
  • 通讯作者:
    Dimitris Metaxas
The Traffic Calming Effect of Delineated Bicycle Lanes
划定自行车道的交通平静效果
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hannah Younes;Clinton Andrews;Robert B. Noland;Jiahao Xia;Song Wen;Wenwen Zhang;Dimitris Metaxas;Leigh Ann Von Hagen;Jie Gong
  • 通讯作者:
    Jie Gong

Dimitris Metaxas的其他文献

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{{ truncateString('Dimitris Metaxas', 18)}}的其他基金

Collaborative Research: HCC: Medium: Linguistically-Driven Sign Recognition from Continuous Signing for American Sign Language (ASL)
合作研究:HCC:媒介:美国手语 (ASL) 连续手语中语言驱动的手语识别
  • 批准号:
    2212301
  • 财政年份:
    2022
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
NSF Convergence Accelerator Track H: AI-based Tools to Enhance Access and Opportunities for the Deaf
NSF 融合加速器轨道 H:基于人工智能的工具,增强聋人的获取和机会
  • 批准号:
    2235405
  • 财政年份:
    2022
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
NSF Convergence Accelerator Track D: Data & AI Methods for Modeling Facial Expressions in Language with Applications to Privacy for the Deaf, ASL Education & Linguistic Res
NSF 融合加速器轨道 D:数据
  • 批准号:
    2040638
  • 财政年份:
    2020
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
CHS: Medium: Collaborative Research: Scalable Integration of Data-Driven and Model-Based Methods for Large Vocabulary Sign Recognition and Search
CHS:中:协作研究:用于大词汇量符号识别和搜索的数据驱动和基于模型的方法的可扩展集成
  • 批准号:
    1763523
  • 财政年份:
    2018
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Phase 1 IUCRC Rutgers-New Brunswick: Center for Accelerated Real Time Analytics (CARTA)
第一阶段 IUCRC 罗格斯-新不伦瑞克:加速实时分析中心 (CARTA)
  • 批准号:
    1747778
  • 财政年份:
    2018
  • 资助金额:
    $ 50万
  • 项目类别:
    Continuing Grant
AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures
AitF:协作研究:3D/4D 心脏图像的拓扑算法:理解复杂和动态结构
  • 批准号:
    1733843
  • 财政年份:
    2017
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
CHS: Medium: Data Driven Biomechanically Accurate Modeling of Human Gait on Unconstrained Terrain
CHS:中:数据驱动的无约束地形上人类步态的生物力学精确建模
  • 批准号:
    1703883
  • 财政年份:
    2017
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
EAGER: Collaborative Research: Data Visualizations for Linguistically Annotated, Publicly Shared, Video Corpora for American Sign Language (ASL)
EAGER:协作研究:美国手语 (ASL) 语言注释、公开共享视频语料库的数据可视化
  • 批准号:
    1748022
  • 财政年份:
    2017
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
CIF: Medium: Collaborative Research: Quickest Change Detection Techniques with Signal Processing Applications
CIF:媒介:协作研究:信号处理应用的最快变化检测技术
  • 批准号:
    1513373
  • 财政年份:
    2015
  • 资助金额:
    $ 50万
  • 项目类别:
    Continuing Grant
EAGER: Multi-modal human gait experimentation and analysis on unconstrained terrains
EAGER:无约束地形上的多模式人类步态实验和分析
  • 批准号:
    1451292
  • 财政年份:
    2014
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant

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