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世纪最具颠覆性的技术之一，它将彻底改变我们彼此之间、与环境以及与设备和系统的交互方式。VR使用先进的显示和身临其境的音频技术来创建互动的三维(3D)环境。AR使用数字技术将虚拟对象覆盖到物理世界上，以提供信息并美化我们的体验。当前和预期的应用领域包括教育、医疗保健、专业培训、建筑和产品设计、远程交互和娱乐。AR/VR这一新兴领域的持续发展要求研究人员具有博士培训和研究经验，涉及多个学科，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、声学和音频以及人机界面。为了满足这一需求并实现AR/VR技术的变革潜力，授予罗切斯特大学的国家科学基金会研究培训(NRT)奖将促进AR/VR方面的结构化、多学科博士培训计划的发展。该项目预计将培训62名博士生，其中包括12名受资助的学员，他们来自电气和计算机工程、光学、生物医学工程、脑和认知科学、计算机科学和神经科学。此外，该项目还将使其他约300名STEM研究生受益，他们将参与培训和专业发展。学员将获得推动AR/VR技术发展的视野和技能，以及对这些技术更广泛的文化和社会影响的理解。该项目将培养一批兼容并蓄的科学家和工程师，让他们成为工业、学术界和政府的技术领袖，为社会做出贡献。该项目将培养一批在AR/VR领域具有独特能力的博士生。它将有助于塑造未来的科学家和工程师将如何接受培训，不仅是在AR/VR方面，而且在更广泛的人-数据-系统接口方面。该项目将以创新的主题推进跨学科研究：将人类感知-认知过程的量化模型整合到跨层设计方法中，以创建和量化评估新的AR/VR技术和应用。以交叉方式将研究推进与培训计划相结合，将推进AR/VR系统的科学基础，并影响下一代AR/VR系统的设计。这些研究方向对应于AR/VR问题领域的四个层次：(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；(4)AR/VR接口和应用。该培训计划包含三个新的创新课程，针对即将到来的受训人员的不同背景，让他们面临AR/VR挑战，并提供在多学科团队中处理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军企业的身临其境的专业发展会面。研究生培训模式及其成果将通过有组织的活动和网上活动向更广泛的学术界广泛传播。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革意义的STEM研究生教育培训模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.

When Counterpoint Meets Chinese Folk Melodies

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

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

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

Regularization by Adversarial Learning for Ultrasound Elasticity Imaging

超声弹性成像的对抗性学习正则化

• 发表时间: 2021
• 期刊: European Signal Processing Conference
• 作者: Mohammadi, Narges;Doyley, Marvin M.;Cetin, Mujdat.
• 通讯作者: Cetin, Mujdat.