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REU Site: Secure, Robust, and Resilient AI-enabled System Engineering

REU Site: Secure, Robust, and Resilient AI-enabled System Engineering
REU 站点:安全、稳健且有弹性的人工智能系统工程
批准号:
2050972
负责人:
Ming Shao
金额:
$40.46万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-04-01 至 2024-03-31

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中文摘要
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英文摘要
Artificial intelligence (AI) has gained significant attention in recent years as an enabler of technologies such as autonomous cyber-physical systems and cybersecurity. This funding establishes a new Research Experiences for Undergraduates (REU) Site at the University of Massachusetts Dartmouth with a focus on secure, robust, and resilient AI-enabled system engineering. In each summer, ten undergraduate students will participate in research for ten weeks. During the research, participants will learn engineering principles to support the rigorous design and quantitative test of systems and incorporate AI components to ensure properties such as security, robustness, and resilience. This program will provide a research experience with a balance of theory, hands-on practical skills, and applications. The project is led by a team of experienced faculty who will mentor the participating researchers through a number of professional development activities, which prepare them to take a systems perspective while maintaining technical excellence to enhance the robustness of AI-enabled systems in commercial and societal contexts. This project will provide unique opportunities for undergraduate students, especially for those from underrepresented and minority groups and institutions with limited research resources, to participate in real-world research.Artificial intelligence has gained significant attention in recent years as an enabler of technologies such as autonomous cyber physical systems and cyber security. However, engineering principles to support the rigorous design and quantitative test of systems incorporating AI components are needed to ensure properties such as security, robustness, and resilience. AI system engineering research will provide the foundation required to establish user trust and promote regulatory activities such as safety assurance. To enable to the promise of AI systems, this project proposes a unique Research Experiences for Undergraduates (REU) Site focused on secure, robust, and resilient AI-enabled system engineering. This REU Site will intertwine AI research with cybersecurity and systems engineering in a portfolio of synergistic projects. Ten undergraduate students will be engaged in a ten-week summer research program to conduct research at the University of Massachusetts Dartmouth (UMD). The themes of the proposed REU Site are AI, cybersecurity, and system engineering research and their combined implications. Specifically, the following research topics will be explored: (1) cyber attacks that degrade the performance of AI algorithms; (2) AI-enabled cybersecurity against malicious actors; (3) resilient AI-enabled system engineering methods. These research themes will create opportunities for multidisciplinary knowledge sharing between AI and cybersecurity from software, hardware and system engineering perspectives, including topics in biometrics, UAVs, brain computer interaction, and signal processing. The REU will prepare talented young researchers to take a systems perspective, while maintaining technical excellence to enhance the robustness of AI-enabled systems in commercial and societal contexts. The site will provide unique opportunities for undergraduate students, especially for those from underrepresented and minority groups and institutions with limited research resources, to participate in real-world research.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.
期刊论文(6)
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科研奖励(0)
会议论文
DOI: 10.1109/icra46639.2022.9811868
发表时间: 2022-05
期刊: 2022 International Conference on Robotics and Automation (ICRA)
影响因子: --
作者: [Marc Tunnell;Huijin Chung;Yuchou Chang]
通讯作者: Marc Tunnell;Huijin Chung;Yuchou Chang
DOI: 10.1109/milcom52596.2021.9653036
发表时间: 2021-11
期刊: MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)
影响因子: --
作者: [Julio Galvan;A. Raja;Yanyan Li;Jiawei Yuan]
通讯作者: Julio Galvan;A. Raja;Yanyan Li;Jiawei Yuan
Machine-Learning PUF-based Detection of RF Anomalies in a Cluttered RF Environment
杂乱 RF 环境中基于机器学习 PUF 的 RF 异常检测
DOI: 10.1109/hst53381.2021.9619834
发表时间: 2021
期刊: 2021 IEEE International Symposium on Technologies for Homeland Security (HST
影响因子: --
作者: [Lu, James, Morehouse, Todd, Yuan, Jiawei, Zhou, Ruolin]
通讯作者: Zhou, Ruolin
Adversary on Multimodal BCI-based Classification
基于多模式 BCI 分类的对手
DOI: --
发表时间: 2023
期刊: 11th International IEEE EMBS Conference on Neural Engineering
影响因子: --
作者: [Kumar, Chetan, Donohue, James P., Gonjari, Rohan, Rahimi, Neela, McLinden, John, Shahriari, Yalda, Shao, Ming]
通讯作者: Shao, Ming
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    • 批准号:
      82103981
    • 项目类别:
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    • 资助金额:
      30.0万元
    • 批准年份:
      2021
    • 负责人:
      陈维琳
    • 依托单位:
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    • 批准号:
      41340011
    • 项目类别:
      专项基金项目
    • 资助金额:
      20.0万元
    • 批准年份:
      2013
    • 负责人:
      钱凤魁
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