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 (USC) 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, graduate, and postdoctorate students 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 USC 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 USC. 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.