RII Track-1: Data Analytics that are Robust and Trusted (DART): From Smart Curation to Socially Aware Decision Making

RII Track-1：稳健且值得信赖的数据分析 (DART)：从智能管理到社会意识决策

• 批准号: 1946391
• 负责人: Jennifer Fowler
• 金额: $ 2000万
• 依托单位: Arkansas Science & Technology Authority
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1946391&HistoricalAwards=false
• 关键词: RII; Track; Data; Analytics; Robust

The DART research program will create a consortium of Arkansas researchers with a synergistic, integrated focus on excellence in data analytics research. The vision of the education and workforce development program is to create a statewide Data Science and Analytics educational ecosystem, where learners receive a designed, consistent, sequenced, and modular education in data science with job or further educational opportunities available at appropriate points in their academic path. These efforts, combined with intensive industry collaboration, will provide the pillars of support needed to improve research capability and competitiveness in Arkansas. DART will develop: 1) the means to increase the speed and efficiency of data curation and labeling; 2) techniques to protect privacy and identify impartial content; 3) methods for harnessing the predictive power of machine learning while increasing the interpretability of the processes behind the predictions; and 4) data science curricula that are more inclusive and better prepare students for a data-centric future. These advances will be made possible by bringing together in one research project a large group of talented scientists from diverse, but complementary, research areas. The project will support basic research in math, statistics, data science, and computer science that will enable data-driven discovery through visualization, better data mining, privacy and security protections, machine learning and more. The project will build an open computational infrastructure for researchers and students and develop innovative educational pathways to train the next generation of data scientists. DART will include a data science summer institute for undergraduates and extensive curriculum support for middle-school teachers. A key opportunity in the design and development of the Data Science and Analytics degree program will be to leverage DART research areas and topics as real-life examples for the courses and to integrate these into the curriculum. DART will bring together data science researchers with diverse, but complementary, research interests, backgrounds, and skills to stimulate innovation. DART scientific objectives contribute to the National Science Foundation's (NSF) Harnessing the Data Revolution (HDR) Big Idea in foundations, algorithms, and systems in data science and further develop a coordinated state-wide data cyberinfrastructure. The project will study key barriers to better big data analytics and develop improved algorithms and methods to provide: 1) the means to more automatically curate heterogeneous, unstructured, and poorly-structured data; 2) faster and more robust model training by augmenting manual methods; 3) more secure data by protecting the privacy of contributors; 4) improvements in metrics of data quality; 5) novel unbiased model predictions and decision support systems; and 6) a better balance between the predictive power of complex machine learning models and the interpretability provided by statistical models. Each of these research outcomes will create a better framework for balancing the risks and benefits of new data analytics technologies. As the state better aligns its investments with industry strengths, more opportunities to improve the quality of life in Arkansas and to steadily increase educational attainment and wages will develop. DART will include a data science summer institute for undergraduates, summer internships and research experiences, increased data science educational opportunities, integrated support for middle school teachers across the state, and revamped curricula to include relevant data science topics and capstone projects. Developments in data cyberinfrastructure will increase sharing of information among educational institutions, research institutions, and industry.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.

DART研究计划将创建一个由阿肯色州研究人员组成的联盟，该联盟将协同、综合地专注于卓越的数据分析研究。教育和劳动力发展计划的愿景是创建一个全州范围的数据科学和分析教育生态系统，学习者在数据科学方面接受设计好的，一致的，有序的和模块化的教育，并在其学术道路的适当点提供工作或进一步的教育机会。这些努力，结合密集的行业合作，将为提高阿肯色州的研究能力和竞争力提供必要的支持。DART将开发：1）提高数据管理和标签的速度和效率的方法; 2）保护隐私和识别公正内容的技术; 3）利用机器学习的预测能力，同时提高预测背后过程的可解释性的方法;以及4）更具包容性的数据科学课程，更好地为以数据为中心的未来做好准备。这些进展将通过在一个研究项目中汇集来自不同但互补的研究领域的一大群有才华的科学家来实现。该项目将支持数学，统计学，数据科学和计算机科学的基础研究，通过可视化，更好的数据挖掘，隐私和安全保护，机器学习等实现数据驱动的发现。该项目将为研究人员和学生建立一个开放的计算基础设施，并开发创新的教育途径，以培养下一代数据科学家。DART将包括一个面向本科生的数据科学暑期学院，以及为中学教师提供广泛的课程支持。设计和开发数据科学和分析学位课程的一个关键机会将是利用DART研究领域和主题作为课程的现实例子，并将其整合到课程中。DART将汇集具有不同但互补的研究兴趣、背景和技能的数据科学研究人员，以刺激创新。DART的科学目标有助于美国国家科学基金会（NSF）在数据科学的基础，算法和系统中利用数据革命（HDR）大创意，并进一步发展协调的全州数据网络基础设施。该项目将研究更好的大数据分析的关键障碍，并开发改进的算法和方法，以提供：1）更自动地管理异构，非结构化和结构不良数据的方法; 2）通过增强手动方法更快，更强大的模型训练; 3）通过保护贡献者的隐私更安全的数据; 4）改进数据质量指标; 5）新颖的无偏模型预测和决策支持系统; 6）复杂机器学习模型的预测能力与统计模型提供的可解释性之间的更好平衡。这些研究成果中的每一项都将为平衡新数据分析技术的风险和收益创建一个更好的框架。随着阿肯色州更好地将其投资与行业优势相结合，将有更多机会提高阿肯色州的生活质量，并稳步提高教育水平和工资。DART将包括一个面向本科生的数据科学暑期学院，暑期实习和研究经验，增加数据科学教育机会，为全州的中学教师提供综合支持，并修改课程，以包括相关的数据科学主题和顶点项目。数据网络基础设施的发展将增加教育机构、研究机构和行业之间的信息共享。该奖项反映了NSF的法定使命，并通过使用基金会的知识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

A Robust Classifier under Missing-Not-at-Random Sample Selection Bias

缺失非随机样本选择偏差下的鲁棒分类器

• DOI: 10.1109/bigdata59044.2023.10386877
• 发表时间: 2023
• 期刊: IEEE
• 作者: Mai, Huy;Huang, Wen;Du, Wei;Wu, Xintao
• 通讯作者: Wu, Xintao

A Deep Learning-Based Model for Gene Regulatory Network Inference

基于深度学习的基因调控网络推理模型

• 发表时间: 2023
• 期刊: IEEE CPS conference proceeding
• 作者: Mary Yang, Jialu Ma

Fair Collective Classification in Networked Data

• DOI: 10.1109/bigdata55660.2022.10020610
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Karuna Bhaila;Yongkai Wu;Xintao Wu

AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation

• DOI: 10.1007/s11263-022-01702-9
• 发表时间: 2022-10-28
• 期刊: INTERNATIONAL JOURNAL OF COMPUTER VISION
• 影响因子: 19.5
• 作者: Khoa Vo;Truong, Sang;Ngan Le
• 通讯作者: Ngan Le

Multiagent-based Youtube Content Discovery Bot

基于多代理的 Youtube 内容发现机器人

• 发表时间: 2023
• 期刊: In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• 作者: Nitin Agarwal