Collaborative Research: REU Site: Advancing Data-Driven Deep Coupling of Computational Simulations and Experiments

合作研究：REU 站点：推进数据驱动的计算模拟和实验的深度耦合

• 批准号: 2243981
• 负责人: Xiaokun Yang
• 金额: $ 4.67万
• 依托单位: University of Houston - Clear Lake
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243981&HistoricalAwards=false
• 关键词: Collaborative; Research; REU; Site; Advancing

This project establishes a Research Experiences for Undergraduate (REU) site at Washington State University Vancouver and University of Houston Clear Lake. The REU site hosts a diverse cohort of ten undergraduate students across the nation per year for three years to participate in various research projects on developing and optimizing data-driven and data-intensive scientific workflows used for arrhythmia detection, intelligent monitoring of health behavior, cancer cell detection. The REU site provides an excellent platform for undergraduate students to gain new insights into and potential interests in high-performance computing (HPC) and data-intensive workflows. The project informs a generation of teaching modules on various aspects related to scientific data analysis, large-scale simulation in mechanical engineering, and optimization of HPC systems. The REU site provides underrepresented students, from community colleges, primarily undergraduate institutions, and Hispanic-serving institutions, opportunities to gain experience in interdisciplinary research from a group of researchers representing different races, genders, and cultures.Data-intensive workflows (i.e., scientific workflows) are widely used in most data-driven research disciplines today, often exploiting rich and diverse data resources and parallel and distributed computing platforms. In the proposed summer research program, undergraduate students participate a variety of projects focusing on various aspects of developing data-intensive and data-dependent scientific workflows, from their use as a technique to analyze and process large-scale, real-world data from HPC applications to investigating the underlying computer software and hardware support for accelerating the execution of those workflows. The REU site focuses on real-world scientific problems, including flow simulations for managing large wind farms, droplet ejection modeling for advanced manufacture, microfluidic cancer cell detection in liquid biopsy. The site activities include students training on using data-driven approaches (e.g., machine learning, advanced system software, and emerging hardware) to address the big data issues in the study of these scientific research problems.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.

该项目在华盛顿州立大学温哥华分校和休斯顿大学克利尔湖分校建立了本科生研究经验 (REU) 站点。 REU 网站在三年内每年接待全国十名本科生，参与各种研究项目，开发和优化数据驱动和数据密集型科学工作流程，用于心律失常检测、健康行为智能监测、癌症细胞检测。 REU 网站为本科生提供了一个绝佳的平台，让他们获得对高性能计算 (HPC) 和数据密集型工作流程的新见解和潜在兴趣。该项目为一代教学模块提供了有关科学数据分析、机械工程大规模仿真和 HPC 系统优化等各个方面的信息。 REU 网站为来自社区学院、主要是本科院校和西班牙裔服务机构的代表性不足的学生提供了从代表不同种族、性别和文化的研究人员群体中获得跨学科研究经验的机会。工作流）广泛应用于当今大多数数据驱动的研究学科，通常利用丰富多样的数据资源以及并行和分布式计算平台。在拟议的夏季研究计划中，本科生参与各种项目，重点关注开发数据密集型和数据依赖型科学工作流程的各个方面，从使用它们作为分析和处理来自 HPC 的大规模真实世界数据的技术应用程序来调查底层计算机软件和硬件支持，以加速这些工作流程的执行。 REU 网站专注于现实世界的科学问题，包括管理大型风电场的流动模拟、先进制造的液滴喷射模型、液体活检中的微流体癌细胞检测。该网站的活动包括培训学生使用数据驱动的方法（例如机器学习、先进的系统软件和新兴硬件）来解决这些科学研究问题研究中的大数据问题。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。