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.

本科生（REU）网站的智能和可扩展系统研究经验为每年10个本科生提供了为期10周的强化研究经验。 参与者将通过专注于两个关键研究领域的交集的指导进行尖端研究：机器学习和可扩展系统。在他们的研究项目过程中，学生将学习如何应用机器学习来创建从数据学习的系统，并运用他们所学的知识来生产解决方案来压迫社会和技术问题。 通过关注可扩展性，REU站点将确保学生能够生产可以利用并行计算资源的研究工具，以便快速处理数据而不牺牲准确性。 REU网站将广泛招募，重点是鼓励妇女，人数不足和少数族裔学生参加。 REU网站还将提供广泛的研讨会和教程计划，以确保学生在机器学习，并发和研究方法中具有必要的背景，不仅是为了夏季项目，而且还为他们准备高级职业教育和研究生教育的准备。 参与者将在机器学习和可扩展计算机系统中进行基本主题的研究。示例包括用于培训机器学习模型的新算法，提高处理训练数据速度，推理加速度的算法和技术，提高智能系统的解释性以及机器学习在硬科学和社会问题上的应用。 学生将参加一系列广泛的研讨会和研讨会，这些研讨会将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；机器学习的新颖应用；研究过程和方法论；准备研究生；以及如何在计算机科学领域取得成功。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。

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