ABSTRACT

This Research Training Group (RTG) project is a joint effort of Mathematics, Statistics, Computer Science and Engineering. It aims to develop a multi-tier Research Training Program at the University of South Carolina (UofSC) designed to prepare the future workforce in a multidisciplinary paradigm of modern data science. The education and training models will leverage knowledge and experience already existing among the faculty and bring in new talent to foster mathematical data science expertise and research portfolios through a vertical integration of post-doctoral research associates, graduate students, undergraduate students, and advanced high school students. A primary focus of this project is to recruit and train U.S. Citizens, females, and underrepresented minority (URM) among undergraduate and graduate students, and postdocs through research led training in Data Science. The research and training infrastructure implemented through this RTG program will not only support the planned majors and master?s degrees, but also provide systemic educational curricula for students and researchers from other areas whose research would benefit from Data Science within UofSC and in the vicinity. The training materials created by this RTG program will also be widely available to other institutions across the country. The RTG project will help build a highly educated workforce for academia, government and industry, in the area of data science, artificial intelligence, and machine learning.

This project is a response to emerging demands of modern technology-oriented societies for an innovative workforce with expertise in all areas related to Data Science. Based on a comprehensive view of Data Science, the program aims at providing students and postdocs with the necessary concepts that enable them to form their own research agenda. Our program covers, on the one hand, emerging developments in network science, artificial intelligence, machine learning, and optimization methodologies from computer science and statistical perspectives primarily for the Big-Data regime with applications such as autonomous systems. In addition, problems typically posed in a Small-Data regime can relate these concepts to relevant methodologies, such as Physics Informed Learning, needed to understand mathematical models, usually formulated in terms of Partial Differential Equations (PDEs), so as to understand key techniques for synthesizing models and data in the context of Uncertainty Quantification. Properly interrelating these activities in the broader Data Science landscape, will enable students to successfully tackle new problem areas at later stages of their career and address important challenges in sciences and engineering. The corresponding theoretical training is reinforced by accompanying practical training modules that are able to engage students across all levels as well as young researchers in synergistic activities, even reaching out to local industries. It is a feedback-loop between research and education that distinguishes the project. The educational component is designed with an ultimate goal of developing an innovative research training program to educate future workforce in a structured curriculum that offers a major, a master?s degree and a 4+1 dual degree in Data Science at UofSC. The project facilitates team-teaching by relevant experts and uses direct links to research projects that students will participated in. The built-in vertical and horizontal pedagogical synergies as well as the hierarchical mentoring scheme expose participating students to extensive educational and research experience offered by the program. This project is jointly funded by Computational and Data-enabled Science and Engineering in Mathematical and Statistical Sciences (CDS&E-MSS), the Established Program to Stimulate Competitive Research (EPSCoR), and the Workforce Program in the Mathematical Sciences, among others.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.