REU Site: Trust and Reproducibility of Intelligent Computation

这个本科生网站的研究经验解决了日益增长的现实，即现代社会越来越依赖于复杂的软件组件，以按预期（值得信赖）工作，并产生一致的结果（可重复）。可信度和可再现性决定了软件在一系列应用中产生的结果的采用和接受，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、可信度和可再现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问题，也没有上述的支持方法，但他们将是构建未来智能系统的人。因此，这个REU网站将解决这些问题，以及新出现的危险，例如在基于AI的应用程序中引入偏见或泄露个人数据，这需要在软件系统的道德考虑方面进行指导，这是可信度的另一个方面。 学生参与者将学习最先进的方法通常用于值得信赖的和可重复的科学和工程。 每周的培训和活动课程将使整个队列聚集在一起进行预先包装的练习，例如，自动化的缺陷检测、自动化的性能分析和优化以及数据分析。 这些活动部署在犹他州大学运营的独一无二的转化研究平台上，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的其他活动，研究人员可以提供关于研究最佳实践、道德和包容性的研讨会。 重点关注结合机器学习的应用程序，实现高效率，以及支持应用程序的系统，每个本科生参与者将与教师导师和他们的研究小组一起完成一个研究项目，制作书面研究报告和包装良好的工件，使另一个人能够理解研究，重复实验，并重现结果。 学生被选为该计划的双重目标是扩大参与计算和提供研究经验的学生在他们的家乡机构的机会严重有限。 该计划在开始和结束时进行评估，评估学生如何将他们的新理解应用于他们的研究项目，并在学生申请研究生院时进行后续工作。该项目计划与其他机构分享这些评估和课程材料，以推广值得信赖和可复制软件的教育，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 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.