NRT-HDR: Interdisciplinary Graduate Training in the Science, Technology, and Applications of Augmented and Virtual Reality

NRT-HDR：增强和虚拟现实科学、技术和应用的跨学科研究生培训

• 批准号: 1922591
• 负责人: Mujdat Cetin
• 金额: $ 156万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922591&HistoricalAwards=false
• 关键词: NRT; HDR; Interdisciplinary; Graduate; Training

Augmented and virtual reality (AR/VR) promises to become one of the most disruptive technologies of the 21st century, revolutionizing how we interact with each other, with our environment, and with devices and systems. VR uses advanced display and immersive audio technologies to create an interactive, three-dimensional (3D) environment. AR uses digital technology to overlay virtual objects onto the physical world to provide information and embellish our experiences. Current and envisioned application areas include education, healthcare, professional training, architectural and product design, remote interaction, and entertainment. Continued progress in the burgeoning field of AR/VR requires researchers with Ph.D. training and research experience spanning multiple disciplines including electronic and computing systems, perceptual and cognitive neuroscience, optics and imaging, computer vision, acoustics and audio, and human-computer interfaces. To address this need and realize the transformative potential of AR/VR technologies, this National Science Foundation Research Traineeship (NRT) Award to the University of Rochester will facilitate the development of a structured, multi-disciplinary Ph.D. training program on AR/VR. The project anticipates training 62 PhD students, including 12 funded trainees, from Electrical and Computer Engineering, Optics, Biomedical Engineering, Brain and Cognitive Sciences, Computer Science, and Neuroscience. In addition, the project will benefit approximately 300 other STEM graduate students who will participate in aspects of the training and professional development. Trainees will gain the vision and skills to advance AR/VR technologies as well as an appreciation for the broader cultural and societal implications of these technologies. The project will train inclusive cohorts of scientists and engineers to contribute to society as technical leaders in industry, academia, and government.The project will train a new cohort of Ph.D. students with a unique set of competencies in the AR/VR domain. It will help shape how future scientists and engineers will be trained not only in AR/VR but more broadly in human-data-system interfaces. The project will advance interdisciplinary research with an innovative theme: integration of quantitative models of human perceptual-cognitive processes into cross-layer design approaches to create and quantitatively evaluate new AR/VR technologies and applications. Research thrusts integrated with the training program in a cross-cutting manner will advance the scientific foundations of AR/VR systems and impact the design of next-generation AR/VR systems. These research thrusts, corresponding to four layers of the AR/VR problem domain, are: (1) AR/VR platforms and computation, (2) perceptual-cognitive aspects of AR/VR design, (3) machine intelligence for AR/VR systems, and (4) AR/VR interfaces and applications. The training program contains three new innovative courses addressing the diverse backgrounds of incoming trainees, exposing them to AR/VR challenges and providing competency to work on AR/VR projects within multi-disciplinary teams as well as a variety of structured professional development activities. In addition, the training will include industry internships and immersive professional development encounters with industry leaders. Both the graduate training model and its outcomes will be widely disseminated to the broader academic community through organized events and a web presence. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

增强和虚拟现实（AR/VR）有望成为21世纪最具破坏性的技术之一，彻底改变了我们如何与环境以及设备和系统相互互动。 VR使用高级显示和沉浸音频技术来创建交互式的三维（3D）环境。 AR使用数字技术将虚拟对象叠加到物理世界中，以提供信息并修饰我们的经验。当前和设想的应用领域包括教育，医疗保健，专业培训，建筑和产品设计，远程互动和娱乐。 AR/VR迅速发展领域的持续进展需要博士学位的研究人员。培训和研究经验涵盖了多个学科，包括电子和计算系统，感知和认知神经科学，光学和成像，计算机视觉，声学和音频以及人类计算机接口。为了满足这一需求并实现AR/VR技术的变革潜力，这项国家科学基金会研究培训（NRT）授予罗切斯特大学将有助于发展结构化的多学科博士学位。 AR/VR的培训计划。该项目预计将培训62名博士生，包括来自电气和计算机工程，光学，生物医学工程，大脑和认知科学，计算机科学和神经科学的12名资助学员。此外，该项目将使大约300名其他STEM研究生受益，他们将参与培训和专业发展方面。学员将获得远见和技能，以推进AR/VR技术，并对这些技术的更广泛的文化和社会影响表示赞赏。该项目将培训包容性的科学家和工程师，以作为工业，学术界和政府的技术领导者为社会做出贡献。该项目将培训新的博士学位。在AR/VR域中具有独特能力的学生。它将有助于塑造未来的科学家和工程师将不仅在AR/VR中进行培训，而且在人数据系统界面中进行更广泛的培训。该项目将以创新的主题推进跨学科研究：将人类感知认知过程的定量模型整合到跨层设计方法中，以创建和定量评估新的AR/VR技术和应用。与培训计划相结合的研究刺激以跨切割方式促进了AR/VR系统的科学基础，并影响下一代AR/VR系统的设计。这些研究推力对应于AR/VR问题域的四层，是：（1）AR/VR平台和计算，（2）AR/VR设计的感知认知方面，（3）AR/VR系统的机器智能以及（4）AR/VR接口和应用。该培训计划包含了三个新的创新课程，这些课程针对传入的受训者的不同背景，使他们面临AR/VR挑战，并提供了在多学科团队中从事AR/VR项目的能力，以及各种结构化的专业发展活动。此外，培训将包括行业实习和与行业领导者的身临其境的专业发展相遇。研究生培训模型及其成果将通过有组织的活动和网络形象广泛地传播到更广泛的学术界。 NSF研究训练（NRT）计划旨在鼓励开发和实施用于STEM研究生教育培训的大胆，新的潜在变革模型。该计划致力于通过全面的跨学科或收敛性研究领域的STEM研究生进行有效培训，通过全面的培训模型，这些模型具有创新性，基于循证的，并且与不断变化的劳动力和研究需求保持一致。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务，并被视为值得的支持。

项目成果

Method for minimizing lens breathing with one moving group

用一个移动组最小化镜片呼吸的方法

• DOI: 10.1364/oe.457420
• 发表时间: 2022
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Goodsell, Jeremy;Blahnik, Vladan;Rolland, Jannick P.
• 通讯作者: Rolland, Jannick P.

EEG Emotion Recognition via Graph-based Spatio-Temporal Attention Neural Networks

• DOI: 10.1109/embc46164.2021.9629628
• 发表时间: 2021-11
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
• 作者: Shadi Sartipi;Mastaneh Torkamani-Azar;Müjdat Çetin

Ultrasound Elasticity Imaging Using Physics-Based Models and Learning-Based Plug-and-Play Priors

• DOI: 10.1109/icassp39728.2021.9413652
• 发表时间: 2021-03
• 期刊: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: N. Mohammadi;M. Doyley;M. Çetin

When Counterpoint Meets Chinese Folk Melodies

• 发表时间: 2020
• 作者: Nan Jiang;Sheng Jin;Z. Duan;Changshui Zhang

Noise-Resilient Training Method for Face Landmark Generation From Speech

• DOI: 10.1109/taslp.2019.2947741
• 发表时间: 2020-05
• 期刊: IEEE/ACM Transactions on Audio, Speech, and Language Processing
• 作者: S. Eskimez;R. Maddox;Chenliang Xu;Z. Duan