An NSF REU Site Based on Trust and Reproducibility of Intelligent Computation: Experience Report
An NSF REU Site Based on Trust and Reproducibility of Intelligent Computation: Experience Report
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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
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Yadrov, Artem
中科院分区：
文献类型：
--
作者：
Hall, Mary;Gopalakrishnan, Ganesh;Eide, Eric;Cohoon, Johanna;Phillips, Jeff;Zhang, Mu;Elhabian, Shireen;Bhaskara, Aditya;Dam, Harvey;Yadrov, Artem
本文介绍了一个概述的NSF研究经验的本科生（REU）网站的信任和再现的智能计算，在犹他州大学计算学院的教师和研究生。选定的主题汇集了未来在产生可信任的计算结果方面的几个问题：安全，可再现，基于良好的算法基础，并在道德考虑的背景下开发。学生项目所代表的研究领域包括机器学习，高性能计算，算法和应用，计算机安全，数据科学和以人为本的计算。在项目的前四周，整个学生群体每天上午都在学习这些交叉主题的专家课程，并使用犹他州大学运营的独一无二的研究平台，即NSF资助的CloudLab和POWDER设施;阅读作业，测验和动手练习加强了课程。在接下来的五周里，讲座不那么频繁了，因为学生们分成小组来发展他们的研究项目。最后一周的重点是海报展示和最终报告。通过描述我们的经验，该计划可以作为一个模型，为未来的劳动力准备将机器学习集成到值得信赖和可重复的应用程序。
This paper presents an overview of an NSF Research Experience for Undergraduate (REU) Site on Trust and Reproducibility of Intelligent Computation, delivered by faculty and graduate students in the Kahlert School of Computing at University of Utah. The chosen themes bring together several concerns for the future in producing computational results that can be trusted: secure, reproducible, based on sound algorithmic foundations, and developed in the context of ethical considerations. The research areas represented by student projects include machine learning, high-performance computing, algorithms and applications, computer security, data science, and human-centered computing. In the first four weeks of the program, the entire student cohort spent their mornings in lessons from experts in these crosscutting topics, and used one-of-a-kind research platforms operated by the University of Utah, namely NSF-funded CloudLab and POWDER facilities; reading assignments, quizzes, and hands-on exercises reinforced the lessons. In the subsequent five weeks, lectures were less frequent, as students branched into small groups to develop their research projects. The final week focused on a poster presentation and final report. Through describing our experiences, this program can serve as a model for preparing a future workforce to integrate machine learning into trustworthy and reproducible applications.
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