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

该项目在华盛顿州立大学温哥华分校和休斯顿大学清湖分校建立了本科生研究经验（REU）站点。REU网站每年接待来自全国各地的10名本科生，为期三年，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，癌细胞检测。REU网站为本科生提供了一个很好的平台，可以获得对高性能计算（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与科学数据分析、机械工程大规模模拟和高性能计算系统优化相关的各个方面的信息。REU网站为来自社区学院、主要本科机构和西班牙裔服务机构的代表性不足的学生提供机会，从代表不同种族、性别和文化的一组研究人员那里获得跨学科研究的经验。数据密集型工作流（即科学工作流）在当今大多数数据驱动的研究学科中被广泛使用，通常利用丰富多样的数据资源和并行分布式计算平台。在提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性科学工作流的各个方面，从它们作为分析和处理来自HPC应用程序的大规模真实数据的技术，到调查底层计算机软件和硬件支持，以加速这些工作流的执行。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建模，液体活检中的微流控癌细胞检测。现场活动包括培训学生使用数据驱动的方法（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中的大数据问题。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。

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.