REU Site: Computing for Health and Well-being

本科生研究经验（REU）网站解决了美国医疗保健系统由于短期挑战（如COVID-19爆发带来的挑战）以及长期趋势（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，与计算机科学家和健康专业人员一起在跨学科，以问题为中心的团队中工作。 REU学生将（1）学习跨学科研究;（2）将计算思维应用于现实世界的问题;（3）熟悉不同学科的文化和研究方法;（4）参加研究方法，科学交流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和能力的自信。特别关注的是提供研究经验，从学校的学生不是研究机构，学校缺乏跨学科的研究机会所提供的综合性医疗中心的存在，和/或学生从代表性不足的群体，包括对第一代学生的特别重视。其目标是帮助培养一支有才华的年轻科学家队伍，他们将有助于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，以提高健康和福祉。解决与社会相关的问题和参与专业发展将为学生提供智力挑战和引人入胜的体验，同时培养对计算机科学和科学研究的热情。每个学生都被纳入一个既定的研究小组，在导师的直接监督下工作，致力于健康和福祉领域的前沿跨学科挑战。 使用最新的技术和计算工具，以及建模，仿真，人工智能，机器学习和人机交互的先进算法方法，学生将研究传染病建模，疾病风险评估，先进的助听器技术，评估情绪或情绪的筛选工具等领域的问题，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。接触他们的队列中的各种研究项目的集合将进一步参与者对自己的项目的理解，同时扩大他们对不同研究方法的欣赏：同时，本发明还公开了一种用于该方法的装置，导师发展活动将有助于早期-职业教师和高级研究生学习如何有效地与年轻科学家合作。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。

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.