REU Site: Cyberinfrastructure (CI) Research 4 Social Change

世界越来越依赖于计算能力，越来越大的传感器和仪器网络增强了计算能力。为了推动跨多个学科的创新，需要先进的计算（数据、高性能计算、分析）跟上并加快科学发现的速度。响应美国国家科学基金会推动科学进步的使命；促进国民健康、繁荣和福利；为了确保国防安全，网络基础设施（CI）研究4社会变革REU项目每年夏天积极邀请10名本科生参加为期9周的解决与国家相关的现实问题的活动，教他们不仅成为批判性思想家，而且成为创造性和反思者。REU项目正在为未来的科学工作者准备使用先进的CI资源，从而在研究领域建立能力，支持在理解广泛的社会挑战方面取得重大进展。学生有强烈的愿望在他们的社区产生影响，有强烈的愿望有归属感，并强烈希望成为比自己更大的东西的物质部分。将社会变革原则与解决现实问题联系起来，是在计算领域招募和留住代表性不足的社区，以及让学生准备好利用国家CI的长期成功的关键。REU旨在实现三个目标：(1)通过整合计算科学，数据科学和多学科科学的学习，培养学生使用国家CI，为研究生课程和劳动力做准备；(2)培养学生运用先进的计算技能、批判性思维和创造力来解决与社会相关的问题；(3)增加STEM项目中具有计算能力的多样化学生的数量。在世界级超级计算中心的丰富和变革体验包括：高性能计算（HPC）、可视化和数据密集型计算方面的培训；由德克萨斯大学奥斯汀分校的研究人员指导；德州大学奥斯汀校区的社交和团队建设活动；专业发展和研究生院的准备；领导能力的发展以及发展和提高沟通技巧的机会。研究项目强调将先进的计算技术作为一种推动发现的工具，这些发现将影响未来几代人的社会变革。利用CI，学生们在工程、科学和计算医学方面进行前沿研究：帮助实现流程自动化，以支持生产生物燃料的清洁能源；增加石油、土木与环境工程、地质等领域多孔材料性质建模与预测的研究资源；利用数据科学和可视化来说明太平洋地区科学、传统文化和紧迫社会问题之间的联系；开发和分析风暴潮、潮汐和海岸环流中多物理场、多尺度流动和输运问题的数值算法；开发和分析个体化医疗的机器学习算法。REU招收了至少50%的弱势学生。定向招聘还包括女性、第一代大学生，以及来自研究机会有限的机构（包括少数族裔服务机构）的多数学生。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The world is increasingly more dependent on computational power, augmented by larger and larger networks of sensors and instruments. To fuel innovation across multiple disciplines, advanced computing (data, high performance computing, analytics) is required to keep pace with and accelerate the rate of scientific discoveries. In response to the National Science Foundation's mission to promote the progress of science; advance the national health, prosperity and welfare; and secure the national defense, the Cyberinfrastructure (CI) Research 4 Social Change REU project is actively engaging ten undergraduate students each summer for nine-weeks in solving real-world problems of national relevance, teaching them to not only be critical thinkers, but to be creative and reflective as well. The REU project is preparing the future scientific workforce to use advanced CI resources, thus building capacity in research areas that support major advances in understanding across a broad range of societal challenges. Students have a strong desire to have an impact in their communities, a strong desire to have a sense of belonging, and a strong desire to be materially part of something larger than themselves. Connecting social change principals to real-world problem solving is key to long-term success for recruiting and retaining underrepresented communities to computational fields and for preparing students to leverage the national CI. The REU aims to meet three objectives: (1) train students to use the national CI by integrating learning of computational science, data-enabled science and multi-disciplinary science in preparation for graduate programs and the workforce; (2) train students to apply advanced computational skills, critical thinking, and creativity to address problems relevant to society; and (3) increase the number of diverse and computationally competent students in the STEM pipeline. The enriching and transformative experience at a world-class supercomputing center includes: training in High Performance Computing (HPC), visualization, and data intensive computing; mentoring by The University of Texas (UT) at Austin researchers; social and team-building activities on the UT Austin campus; professional development and graduate school preparation; and leadership development and opportunities to develop and enhance communication skills. Research projects emphasize advanced computing as a tool to power discoveries that will impact social change for future generations. Using the CI, students conduct cutting-edge research in engineering, science, and computational medicine: helping automate processes in support of cleaner energy sources for production of biofuel; enhancing research resources for modeling/prediction of porous material properties in fields of petroleum, civil and environmental engineering as well as geology; using data science and visualization to illustrate interfaces between science, traditional culture, and pressing societal problems in the Pacific region; developing and analyzing numerical algorithms for multiphysics, multiscale flow and transport problems in storm surge, tides, and coastal circulation; and developing and analyzing machine learning algorithms for individualized medicine. The REU recruits at least 50% underrepresented students. Targeted recruitment also includes women, first-generation college students, and majority students from institutions with limited research opportunities, including Minority-Serving Institutions.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.