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

点击翻译按钮获取中文摘要

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.