REU Site: Trust and Reproducibility of Intelligent Computation

REU 站点：智能计算的信任和可重复性

• 批准号: 2244492
• 负责人: Ganesh Gopalakrishnan
• 金额: $ 40.5万
• 依托单位: University of Utah
• 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: 2023-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244492&HistoricalAwards=false
• 关键词: REU; Site; Trust; Reproducibility; Intelligent

This Research Experience for Undergraduates Site addresses the growing reality that modern society is increasingly dependent on complex software components to work as expected (trustworthy) and produce consistent results (reproducible). Trustworthiness and reproducibility govern adoption and acceptance of the results produced by software across a range of applications including medical diagnostics, facial recognition, traffic analysis, network security, and chemical reaction control. Computing educators are obliged to instill in the next generation of scientists and engineers -- today’s undergraduates -- an understanding of principled methods that enhance software system reliability, trustworthiness, and reproducibility. A typical undergraduate student is not sufficiently exposed to these concerns nor the aforesaid supportive methods, and yet they will be the ones building future intelligent systems. Thus, this REU site will address these issues, as well as emerging dangers such as introducing bias into AI-based applications or leaking personal data demand instruction in ethical considerations of software systems, another aspect of trustworthiness. Student participants will learn the state-of-the-art methods typically used in trustworthy and reproducible science and engineering. Weekly training and activity sessions will bring the entire cohort together for pre-packaged exercises, e.g., using Jupyter notebooks, software version control, automated defect detection, automatic performance analysis and optimization, and data analysis. These activities are deployed on one-of-a-kind, translational research platforms operated by the University of Utah, namely the NSF-funded Cloudlab and the POWDER project. Additional activities through the university’s Office of Undergraduate Research and the Utah Center for Inclusive Computing allow the researchers to offer workshops on research best practices, ethics, and inclusion. With a focus on applications that incorporate machine learning, achieve high efficiency, and the systems that support the applications, each undergraduate participant will work with a faculty mentor and their research group to complete a research project, producing both a written research report and a well-packaged artifact that, together, enable another person to understand the research, repeat the experiments, and reproduce the results. Students are selected to the program with the dual goals of broadening participation in computing and offering research experiences to students with severely limited opportunities at their home institutions. The program is assessed at the beginning and end, with an evaluation of how students applied their new understandings to their research projects, with follow-ups conducted as students apply to graduate school. The project plans to share these assessments and the curricular material with other institutions to propagate education in trustworthy and reproducible software to galvanize the next generation of scientists and engineers.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.

该本科生研究体验网站解决了日益增长的现实，即现代社会越来越依赖复杂的软件组件来按预期工作（可信）并产生一致的结果（可重复）。可信度和可重复性决定了整个社会对软件产生的结果的采用和接受。计算机教育工作者有义务向下一代科学家和工程师（今天的本科生）灌输对增强软件系统的原则方法的理解。典型的本科生没有充分了解这些问题，也没有充分了解上述支持方法，但他们将是构建未来智能系统的人，因此，该 REU 网站将解决这些问题以及新兴问题。诸如在基于人工智能的应用程序中引入偏见或泄露个人数据等危险需要对软件系统的道德考虑进行指导，这是可信度的另一个方面，学生参与者将学习通常在可信赖和可重复的科学和工程中使用的最先进的方法。 。每周的培训和活动课程将把整个团队聚集在一起进行预先打包的练习，例如使用 Jupyter 笔记本、软件版本控制、自动缺陷检测、自动性能分析和优化以及数据分析。这些活动部署在其中之一上。犹他大学运营的一种转化研究平台，即 NSF 资助的 Cloudlab 和 POWDER 项目，通过大学本科生研究办公室和犹他州包容性计算中心开展的其他活动使研究人员能够举办研究研讨会。重点关注结合机器学习、实现高效率的应用程序以及支持应用程序的系统，每个本科生参与者将与导师及其研究小组合作完成一个研究项目，并产生成果。书面研究报告和精心包装的工件一起，使另一个人能够理解研究、重复实验并重现结果，学生被选入该项目的目的是扩大计算参与和提供研究。对在其所在机构机会严重有限的学生的经历进行评估。在开始和结束时，评估学生如何将他们的新理解应用到他们的研究项目中，并在学生申请研究生院时进行后续行动，该项目计划与其他机构分享这些评估和课程材料以宣传教育。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An NSF REU Site Based on Trust and Reproducibility of Intelligent Computation: Experience Report

基于智能计算的信任和可重复性的 NSF REU 站点：经验报告

• DOI: 10.1145/3624062.3624100
• 发表时间: 2023-11
• 期刊: and Analysis
• 作者: Hall, Mary;Gopalakrishnan, Ganesh;Eide, Eric;Cohoon, Johanna;Phillips, Jeff;Zhang, Mu;Elhabian, Shireen;Bhaskara, Aditya;Dam, Harvey;Yadrov, Artem;et al
• 通讯作者: et al