REU Site: Intelligent and Scalable Systems

本科生智能和可扩展系统研究经验（REU）网站为利哈伊大学的10名本科生提供了为期10周的鼓舞人心和成功的研究经验。整个夏天，参与者将学习如何利用机器学习的巨大潜力，以及现代并行计算资源的前所未有的力量，来发明新的系统和算法，这些系统和算法将注入智能，从而能够解决紧迫的社会问题。拟议的REU网站采用了多方面的招聘策略，重点是鼓励妇女，代表性不足，和少数民族学生参与;一个特别的重点将放在从缺乏本科研究机会的机构招聘。学生还将受益于研讨会和教程计划，旨在确保他们准备好合作，了解现代智能和可扩展系统中的关键概念和技术，并鼓励在计算机科学领域继续深造和终身职业。这十年的目标-一周的夏季研究经验是增加追求计算机科学研究生学位的学生的数量和多样性，围绕智能和可扩展系统的及时主题。 这个REU网站将促进对机器学习和可扩展计算机系统的基本主题的研究，例如机器学习的新算法，隐私保护的新方法以及提高并行程序性能的新技术。它还将支持以应用为重点的研究，重点是为研究和社会中的难题创造解决方案。通过研讨会和教师监督的合作项目，参与者将了解研究过程，磨练他们的软件开发技能，提高他们的舒适度与技术交流，成为了解研究生院及其应用程序，并最终对他们在科学/技术/工程/数学事业。该奖项反映了NSF的法定使命，并被认为是值得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的支持。

The Intelligent and Scalable Systems Research Experiences for Undergraduates (REU) site provides an inspirational and successful research experience for 10 undergraduate students over a 10 week period at Lehigh University. Over the summer, participants learn how to harness the incredible potential of machine learning, and the unprecedented power of modern parallel compute resources, to invent new systems and algorithms that are infused with intelligence and hence capable of solving pressing societal problems. The proposed REU Site employs a multifaceted recruiting strategy, with a focus on encouraging women, underrepresented, and minority students to participate; a special focus will be placed on recruiting from institutions that lack undergraduate research opportunities. Students will also benefit from a program of seminars and tutorials designed to ensure that they are ready to collaborate, knowledgeable about the key concepts and technologies in modern intelligent and scalable systems, and inspired to pursue both further education and life-long careers in Computer Science.The objective of this ten-week summer research experience is to increase the number and diversity of students pursuing graduate degrees in computer science, around the timely theme of intelligent and scalable systems. This REU Site will facilitate research into fundamental topics in both machine learning and scalable computer systems, such as new algorithms for machine learning, new approaches to privacy preservation, and new techniques for increasing the performance of parallel programs. It will also support application-focused research, with an emphasis on creating solutions to hard problems in both research and society. Through seminars and faculty-supervised collaborative projects, participants will learn about the research process, hone their software development skills, improve their comfort with technical communication, become knowledgeable about graduate school and its application process, and ultimately develop greater confidence in their ability to succeed in a Science/Technology/Engineering/Math career.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.