REU Site: Trust and Reproducibility of Intelligent Computation

这个面向本科生的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(值得信赖)工作并产生一致的结果(可重现)。可信性和可重复性决定了对软件产生的结果的采用和接受，这些软件涵盖了一系列应用，包括医疗诊断、面部识别、流量分析、网络安全和化学反应控制。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可靠性、可信性和重复性的原则性方法的理解。一个典型的本科生没有充分接触到这些担忧，也没有足够的上述支持方法，但他们将是构建未来智能系统的人。因此，这个REU站点将解决这些问题，以及新出现的危险，例如在基于人工智能的应用程序中引入偏见或在软件系统的道德考虑方面泄露个人数据需求说明，这是可信度的另一个方面。学生将学习在可靠和可重复使用的科学和工程中通常使用的最先进的方法。每周的培训和活动课程将把整个队列聚集在一起，进行预先打包的练习，例如使用Jupyter笔记本电脑、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。这些活动被部署在由犹他大学运营的独一无二的转化性研究平台上，即NSF资助的CloudLab和PASH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的其他活动，研究人员可以提供关于研究最佳实践、伦理和包容性的研讨会。将重点放在结合机器学习、实现高效率的应用程序以及支持这些应用程序的系统上，每个本科生参与者将与一名教师导师和他们的研究小组合作完成一个研究项目，产生一份书面研究报告和一个包装良好的人工制品，共同使另一个人能够理解研究、重复实验和复制结果。学生被选入该项目的双重目标是扩大对计算机的参与，并为在本国机构机会严重有限的学生提供研究经验。该项目在开始和结束时都会进行评估，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调查。该项目计划与其他机构共享这些评估和课程材料，以宣传值得信赖和可重复使用的软件的教育，以激励下一代科学家和工程师。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.