REU Site: Cross-disciplinary Research Experience for Undergraduates on Big Data Analytics in Smart Cities

REU 网站：智慧城市大数据分析本科生跨学科研究经验

• 批准号: 1950485
• 负责人: Lei Yang
• 金额: $ 32.4万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950485&HistoricalAwards=false
• 关键词: REU; Site; Cross; disciplinary; Research

This Research Experiences for Undergraduates (REU) Site aims to deliver a unique and holistic cross-disciplinary research experience on the cutting edge topics of big data for future smart cities development. As the fields of big data and smart cities are reshaping our societies, changing our everyday life, and providing new opportunities for economic growth, the overarching goal is to inspire and prepare undergraduate researchers to pursue academic studies and careers in STEM via cross-disciplinary research, education, and training on big data analytics in smart cities. The project will provide 10 undergraduates per summer the opportunity to work with faculty mentors on important research projects. The participants will also be involved in various professional development activities that will help them to build not only important research skills but also self-confidence to pursue graduate education in big data for smart cities.This REU Site leverages the unique infrastructures at University of Nevada-Reno (e.g., AlertTahoe Fire Camera Network and the intelligent mobility facilities) that provide real data and experimental environments to enhance undergraduates' cross-disciplinary research experience. Undergraduates will work with faculty mentors to address the multitude of open challenges in big data theory and big data computing, and provide big data analytics based solutions to enhance the situational awareness and decision making in smart city applications. The cross-disciplinary research work will explore novel framings and solutions for research problems with the focus on big data analytics in smart cities, by fostering the collaborations among researchers with diverse expertise. The research projects will integrate big data analytics into various smart city applications, advance the state-of-the-art in each domain, and ultimately form novel data analytics frameworks for addressing scientific and societal challenges and opportunities. In order to generate a true data revolution, the REU Site will make significant efforts on the cross-disciplinary education and training for undergraduate students, with a particular focus on underrepresented students. The research and training activities will prepare these students for graduate programs in big data and smart cities, and thereby meet the national need for trained workforce in cross-disciplinary fields of big data and smart cities.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.

本科生研究经验（REU）网站旨在为未来智慧城市发展的大数据前沿课题提供独特而全面的跨学科研究经验。随着大数据和智慧城市领域正在重塑我们的社会，改变我们的日常生活，并为经济增长提供新的机会，总体目标是激励和准备本科研究人员通过跨学科研究，教育和培训在智慧城市中进行大数据分析，从事学术研究和STEM职业。该项目将提供10个本科生每年夏天的机会，与教师导师的重要研究项目。参与者还将参与各种专业发展活动，这将帮助他们不仅建立重要的研究技能，而且建立自信，以追求智能城市大数据的研究生教育。这个REU网站利用内华达大学里诺分校独特的基础设施（例如，AlertTahoe消防摄像机网络和智能移动设施），提供真实的数据和实验环境，以提高本科生的跨学科研究经验。本科生将与教师导师合作，解决大数据理论和大数据计算中的众多开放性挑战，并提供基于大数据分析的解决方案，以增强智能城市应用中的态势感知和决策。跨学科研究工作将通过促进具有不同专业知识的研究人员之间的合作，探索研究问题的新框架和解决方案，重点是智能城市中的大数据分析。这些研究项目将把大数据分析整合到各种智慧城市应用中，推进每个领域的最新技术，并最终形成新的数据分析框架，以应对科学和社会的挑战和机遇。为了产生真正的数据革命，REU网站将在本科生的跨学科教育和培训方面做出重大努力，特别关注代表性不足的学生。该研究和培训活动将帮助这些学生为大数据和智慧城市的研究生课程做好准备，从而满足国家对大数据和智慧城市跨学科领域训练有素的劳动力的需求。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。

项目成果

TopoCommit: A Topological Commit Protocol for Cross-Ledger Transactions in Scientific Computing

• DOI: 10.1109/cluster52292.2023.00038
• 发表时间: 2023-10
• 期刊: 2023 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: Olamide Timothy Tawose;Lei Yang;Dongfang Zhao

Drifting Streaming Peaks-over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast

用于短期风电场发电预测的漂移流峰值超阈值增强型自进化神经网络

• DOI: 10.3390/fi15010017
• 发表时间: 2023
• 期刊: Future Internet
• 影响因子: 3.4
• 作者: Liu, Yunchuan;Ghasemkhani, Amir;Yang, Lei
• 通讯作者: Yang, Lei

A holistic approach for single-cell data trajectory inference using chromosome physical location and ensemble random walk

使用染色体物理位置和集合随机游走进行单细胞数据轨迹推断的整体方法

• 发表时间: 2024
• 期刊: 21st International Conference on Information Technology : New Generations
• 作者: Cardoza-Aguilar, J.;Milbourn, C.;Zhang, Y.;Yang, L.;Dascalu, S.;Harris, F.
• 通讯作者: Harris, F.

Nemo: An Open-Source Transformer-Supercharged Benchmark for Fine-Grained Wildfire Smoke Detection

• DOI: 10.3390/rs14163979
• 发表时间: 2022-08
• 期刊: Remote. Sens.
• 作者: Amirhessam Yazdi;Heyang Qin;Connor B. Jordan;Lei Yang;Feng Yan

Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data

具有大量缺失数据的多元时间序列传感器数据的数据插补

• DOI: 10.1109/jsen.2022.3166643
• 发表时间: 2022
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Wu, Rui;Hamshaw, Scott D.;Yang, Lei;Kincaid, Dustin W.;Etheridge, Randall;Ghasemkhani, Amir
• 通讯作者: Ghasemkhani, Amir