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HDR DSC: Collaborative Research: Creating and Integrating Data Science Corps to Improve the Quality of Life in Urban Areas

HDR DSC: Collaborative Research: Creating and Integrating Data Science Corps to Improve the Quality of Life in Urban Areas
HDR DSC:协作研究:创建和整合数据科学团队以提高城市地区的生活质量
批准号:
1923982
负责人:
Aryya Gangopadhyay
金额:
$61.5万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-10-01 至 2024-09-30

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中文摘要
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英文摘要
The goal of this project is to develop a team-based data science corps program for undergraduate students from Computer Science, Information Systems, and Business integrating both academic training as well as hands-on experience through real-world data science projects. This project is a collaborative effort with the University of Maryland Baltimore County as the coordinating as well as an implementing organization, and the University of Baltimore, Towson University, and Bowie State University as implementing organizations. This project focuses on the city of Baltimore as an exemplar for other cities in the US and across the globe. The project team will collaborate with a number of communities in the city of Baltimore to integrate real-world data science projects into classroom instruction in data science. The specific objectives of this project are as follows: (i) Develop the technical, analytical, modeling, and critical thinking skills that are key to success as a data science professional; (ii) Connect a cohort of students to communities, organizations, and projects that can benefit from the power of data science; (iii) Nurture and support innovative thinking in solving some of the key challenges facing the real world; (iv) Promote a better understanding of the power and pitfalls of data-driven discoveries to improve the quality of life in urban communities; (v) Increase the data science workforce capacity to support this critical area that is of growing importance in society; and finally, (vi) Evaluate the effect of the proposed data science corps on student learning. This project will create a core set of knowledge that will be valuable in developing solutions for real-world urban settings with the understanding that not all projects will require the application or use of every topic covered in the data science corps program. The core set of knowledge includes data collection and cleaning, data analysis using machine learning and deep learning techniques, data visualization including geospatial data and virtual reality, data privacy and security, and infrastructure for smart cities including IoT-based sensor networks. The proposed data science corps program will have two main phases: instructional phase (10 modules in total) and real-world team projects (5 modules in total). The project teams consist of students who have taken a course in at least one of the following areas: data collection and analysis, big data, machine learning including deep learning, smart cities, cybersecurity, geospatial data analysis and visualization, and virtual reality. Examples of team projects include: (i) developing community-based indicators that are compiled from open data portals and parametric and non-parametric statistical techniques to understand the relationship between urban sustainability and a range of factors including cleanliness and environment, crime and safety, business and economics, social and political, housing, health, and education; (ii) combining deep learning models such as convolutional neural networks (CNN) and long term short term memory recurrent neural networks (LSTM-RNN) to develop prediction models for derelict buildings that are likely to become vacant; (iii) combining sensor data and social media for automated information extraction, validation, and quality checks that can be beneficial to both citizens and emergency managers in crisis situations such as flash floods; (iv) developing smart streetlights that are networked LED systems that can be adjusted based on time of day and motion and can report outages back to central operations; and (v) developing augmented reality-based systems that leverage systems such as Microsoft HoloLens and mobile devices for building evacuation.NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.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.
期刊论文(5)
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会议论文
Multi-modal Deep Learning Based Fusion Approach to Detect Illicit Retail Networks from Social Media
基于多模态深度学习的融合方法从社交媒体检测非法零售网络
DOI: 10.1109/csci51800.2020.00047
发表时间: 2020
期刊: 2020 International Conference on Computational Science and Computational Intelligence (CSCI
影响因子: --
作者: [Paul Rupa, Anamika, Gangopadhyay, Aryya]
通讯作者: Gangopadhyay, Aryya
DOI: 10.1109/csr51186.2021.9527966
发表时间: 2021-07
期刊: 2021 IEEE International Conference on Cyber Security and Resilience (CSR)
影响因子: --
作者: [Oluwagbemiga Ajayi;A. Gangopadhyay]
通讯作者: Oluwagbemiga Ajayi;A. Gangopadhyay
DOI: 10.1109/smartcomp52413.2021.00027
发表时间: 2021-08
期刊: 2021 IEEE International Conference on Smart Computing (SMARTCOMP)
影响因子: --
作者: [Pretom Roy Ovi;E. Dey;Nirmalya Roy;A. Gangopadhyay]
通讯作者: Pretom Roy Ovi;E. Dey;Nirmalya Roy;A. Gangopadhyay
DOI: 10.1109/icmla55696.2022.00183
发表时间: 2022-12
期刊: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
影响因子: --
作者: [Pretom Roy Ovi;A. Gangopadhyay;R. Erbacher;Carl E. Busart]
通讯作者: Pretom Roy Ovi;A. Gangopadhyay;R. Erbacher;Carl E. Busart
RAPID: Deep Learning Models for Early Screening of COVID-19 using CT Images
Integrating Cybersecurity with Undergraduate IT Programs
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