REU Site: Computing for Health and Well-being
REU 网站:计算促进健康和福祉
基本信息
- 批准号:2349121
- 负责人:
- 金额:$ 46.27万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-04-01 至 2027-03-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This Research Experiences for Undergraduates (REU) site addresses the ongoing strains faced by the U.S. healthcare system due to short-term challenges, such as those posed by the COVID-19 outbreak, as well as long-term trends, such as aging population. To tackle these and other problems, the next generation of computer scientists should be trained to work with both computer scientists and health professionals on interdisciplinary, problem-focused teams. REU students will (1) learn about interdisciplinary research;(2) apply computational thinking to real-world problems; (3) become familiar with the culture and research methods of different disciplines; (4) participate in workshops on research methods, scientific communication, and career development; and (5) develop their first professional network while boosting self-confidence in their own skills and abilities. The special focus is on providing research experiences to students from schools that are not research institutions, schools that lack the interdisciplinary research opportunities afforded by the presence of a comprehensive medical center, and/or students from underrepresented groups, including a special emphasis on first-generation students. The goal is to help prepare a workforce of talented young scientists who will help maintain the nation's competitive advantage in science and technology. The REU participants will learn and apply algorithmic methods from computing and data science to enhance health and well-being. Tackling socially relevant problems and participating in professional development will provide students with an intellectually challenging and engaging experience, while fostering a passion for computer science and scientific research. Each student is integrated into an established research group working under a mentor's direct supervision on cutting-edge interdisciplinary challenges in health and well-being. Using the latest technology and computational tools, as well as advanced algorithmic methods from modeling, simulation, artificial intelligence, machine learning, and human-computer interaction, students will work on problems in areas such as infectious disease modeling, disease risk assessment, advanced hearing aid technology, screening tools to assess mood or emotion, and immersive technologies used to engage neurodiverse children through gameplay. Students will also learn through their interactions with other REU participants, graduate students, domain experts, and mentors. Exposure to a collection of diverse research projects within their cohort will further participants’ understanding of their own projects while broadening their appreciation of different research methods: at the same time, mentor development activities will help early-career faculty and advanced graduate students learn how to work effectively with young scientists.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.
该本科生研究经验 (REU) 网站旨在解决美国医疗保健系统因短期挑战(例如 COVID-19 爆发带来的挑战)以及长期趋势(例如人口老龄化)而面临的持续压力。为了解决这些问题和其他问题,下一代计算机科学家应该接受培训,以便与计算机科学家和卫生专业人员在跨学科、以问题为中心的团队中合作,REU 学生将 (1) 学习跨学科研究;(2) 应用计算思维。到现实世界(3) 熟悉不同学科的文化和研究方法;(4) 参加有关研究方法、科学传播和职业发展的研讨会;(5) 发展他们的第一个专业网络,同时增强对自己技能的自信;特别注重为来自非研究机构的学校、缺乏综合医疗中心提供的跨学科研究机会的学校和/或来自代表性不足群体的学生提供研究经验,包括特别强调。关于第一代学生。 REU 的目标是帮助培养一批有才华的年轻科学家,帮助保持国家在科学和技术方面的竞争优势。REU 参与者将学习和应用计算和数据科学的算法方法,以增强健康和福祉。参与专业发展将为学生提供智力上具有挑战性和引人入胜的体验,同时培养对计算机科学和科学研究的热情。每个学生都会融入一个既定的研究小组,在导师的直接监督下研究健康领域的前沿跨学科挑战。和福祉。学生将利用最新的技术和计算工具,以及来自建模、仿真、人工智能、机器学习和人机交互的先进算法方法,研究传染病建模、疾病风险评估、先进助听技术等领域的问题、评估情绪或情绪的筛选工具,以及用于通过游戏吸引神经多样性儿童的沉浸式技术。学生还将通过与其他 REU 参与者、研究生、领域专家和导师的互动来学习。他们的团队将进一步加深参与者的理解自己的项目,同时拓宽他们对不同研究方法的认识:同时,导师发展活动将帮助早期职业教师和高级研究生学习如何与年轻科学家有效合作。该奖项是 NSF 的法定使命,并被认为是值得的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来获得支持。
项目成果
期刊论文数量(0)
专著数量(0)
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Elizabeth Kleiman其他文献
Ieee Transactions on Information Theory Large Deviation Bounds for Functionals of Viterbi Paths
IEEE Transactions on Information Theory 维特比路径泛函的大偏差界
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- 影响因子:0
- 作者:
Arka P. Ghosh;Elizabeth Kleiman;A. Roitershtein - 通讯作者:
A. Roitershtein
Elizabeth Kleiman的其他文献
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{{ truncateString('Elizabeth Kleiman', 18)}}的其他基金
REU Site: Computing for Health and Well-being
REU 网站:计算促进健康和福祉
- 批准号:
2050526 - 财政年份:2021
- 资助金额:
$ 46.27万 - 项目类别:
Standard Grant
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相似海外基金
REU Site: Intelligent Edge Computing Systems
REU 站点:智能边缘计算系统
- 批准号:
2348711 - 财政年份:2024
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2348782 - 财政年份:2024
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2348884 - 财政年份:2024
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2348926 - 财政年份:2024
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- 资助金额:
$ 46.27万 - 项目类别:
Standard Grant