REU Site: Intelligent and Scalable Systems (Renewal)

REU 站点：智能和可扩展系统（续订）

• 批准号: 2051037
• 负责人: Michael Spear
• 金额: $ 40.23万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051037&HistoricalAwards=false
• 关键词: REU; Site; Intelligent; Scalable; Systems

The Intelligent and Scalable Systems Research Experiences for Undergraduates (REU) site provides a 10-week intensive research experience for 10 undergraduate students per year. Participants will conduct cutting-edge research with mentorship from faculty focusing on the intersection between two critical research areas: machine learning and scalable systems. During the course of their research projects, students will learn how to apply machine learning to create systems that learn from data and apply what they have learned to produce solutions to pressing societal and technical problems. Through a focus on scalability, the REU site will ensure that students are able to produce research artifacts that can make use of parallel computational resources, in order to process data quickly without sacrificing accuracy. The REU Site will recruit broadly, with an emphasis on encouraging women, underrepresented, and minority students to participate. The REU Site will also offer an extensive program of seminars and tutorials that ensure students have the necessary background in machine learning, concurrency, and research methods, not only for the sake of their summer projects, but also to prepare them for advanced career options and graduate education.The primary goal of this REU Site will be to mentor students and develop them into competent and successful researchers who are inspired to pursue graduate degrees in computer science. Participants will conduct research in fundamental topics in both machine learning and scalable computer systems. Examples include new algorithms for training of machine learning models, algorithms and techniques for increasing the speed of processing training data, acceleration of inference, techniques for enhancing the explainability of intelligent systems, and application of machine learning to hard scientific and societal problems. Students will participate in a broad series of seminars and workshops that will emphasize ethical use of machine learning; new techniques for efficient computation and software development; technical communication; novel applications of machine learning; the research process and methodology; preparation for graduate studies; and how to succeed in a career in Computer Science.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.

智能和可扩展的系统研究经验的本科生（REU）网站提供了一个为期10周的密集的研究经验，每年为10名本科生。 参与者将在教师的指导下进行前沿研究，重点关注两个关键研究领域之间的交叉：机器学习和可扩展系统。在他们的研究项目的过程中，学生将学习如何应用机器学习来创建从数据中学习的系统，并应用他们所学到的知识来解决紧迫的社会和技术问题。 通过对可扩展性的关注，REU网站将确保学生能够制作可以利用并行计算资源的研究工件，以便在不牺牲准确性的情况下快速处理数据。 REU网站将广泛招募，重点是鼓励女性，代表性不足和少数民族学生参与。 REU网站还将提供广泛的研讨会和教程计划，确保学生拥有机器学习，并发和研究方法的必要背景，不仅是为了他们的暑期项目，同时也为他们的高级职业选择和研究生教育做好准备。这个REU网站的主要目标是指导学生，并将他们培养成有能力和成功的研究人员，他们受到启发，攻读计算机科学研究生学位。 参与者将在机器学习和可扩展计算机系统的基础主题进行研究。例子包括用于训练机器学习模型的新算法，用于提高处理训练数据的速度的算法和技术，加速推理，用于增强智能系统的可解释性的技术，以及将机器学习应用于硬科学和社会问题。 学生将参加一系列广泛的研讨会和讲习班，强调机器学习的道德使用;有效计算和软件开发的新技术;技术交流;机器学习的新应用;研究过程和方法;研究生学习的准备;该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

项目成果

Efficient first-order predictor-corrector multiple objective optimization for fair misinformation detection

• DOI: 10.48550/arxiv.2209.07245
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Eric Enouen;Katja Mathesius;Sean Wang;Arielle K. Carr;Sihong Xie

Learning a More Efficient Backward-Chaining Reasoner

学习更高效的后向链接推理器

• 发表时间: 2022
• 期刊: The Tenth Annual Conference on Advances in Cognitive Systems (ACS-2022
• 作者: Arnold, Alex;Heflin, Jeff
• 通讯作者: Heflin, Jeff

PogoDrone: Design, Model, and Control of a Jumping Quadrotor

• DOI: 10.48550/arxiv.2204.00207
• 发表时间: 2022-04
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Brian Zhu;Jiawei Xu;A. Charway;David Saldaña

A Predictor-Corrector Method for Multi-objective Optimization in Fair Machine Learning

公平机器学习中多目标优化的预测校正方法

• DOI: 10.1109/bdcat56447.2022.00041
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Big Data (Big Data
• 作者: Wang, Sean;Carr, Arielle;Xie, Sihong
• 通讯作者: Xie, Sihong