RTG: Mathematical Foundation of Data Science at University of South Carolina

这个研究训练组（RTG）项目是数学、统计学、计算机科学和工程的共同努力。它的目标是在南卡罗来纳大学（UofSC）开发一个多层次的研究培训计划，旨在为现代数据科学的多学科范式的未来劳动力做好准备。教育和培训模式将利用教师中已有的知识和经验，并通过博士后研究助理、研究生、本科生和高级高中生的垂直整合，引入新的人才，培养数学数据科学专业知识和研究组合。该项目的一个主要重点是通过以研究为主导的数据科学培训，招募和培训美国公民、女性、本科生和研究生中的未被充分代表的少数族裔（URM）以及博士后。通过该RTG项目实施的研究和培训基础设施不仅将支持计划中的专业和硕士学位，还将为来自其他领域的学生和研究人员提供系统的教育课程，这些学生和研究人员的研究将受益于UofSC及附近地区的数据科学。RTG项目编写的培训材料也将广泛提供给全国其他机构。RTG项目将帮助在数据科学、人工智能和机器学习领域为学术界、政府和工业界建立一支受过高等教育的劳动力队伍。该项目是对现代技术导向社会对具有与数据科学相关的所有领域专业知识的创新劳动力的新兴需求的回应。基于数据科学的综合观点，该计划旨在为学生和博士后提供必要的概念，使他们能够形成自己的研究议程。一方面，我们的课程涵盖了网络科学，人工智能，机器学习和优化方法的新兴发展，从计算机科学和统计的角度主要针对自主系统等应用的大数据制度。此外，通常在小数据体制中提出的问题可以将这些概念与相关方法联系起来，例如物理知情学习，这些方法需要理解通常用偏微分方程（PDEs）表示的数学模型，从而理解在不确定性量化背景下综合模型和数据的关键技术。在更广泛的数据科学领域中适当地将这些活动相互关联，将使学生在职业生涯的后期阶段成功地解决新问题领域，并解决科学和工程领域的重要挑战。相应的理论培训通过附带的实践培训模块得到加强，这些模块能够让各个层次的学生以及年轻的研究人员参与协同活动，甚至接触到当地的行业。这是一个研究和教育之间的反馈循环，这是这个项目的特点。教育部分的最终目标是开发一个创新的研究培训计划，以结构化的课程培养未来的劳动力，提供UofSC的数据科学专业，硕士学位和4+1双学位。该项目促进了相关专家的团队教学，并直接链接到学生将参与的研究项目。内置的纵向和横向教学协同作用以及分层指导计划使参与的学生能够获得该计划提供的广泛的教育和研究经验。该项目由数学和统计科学计算与数据驱动科学与工程（CDS&E-MSS）、刺激竞争研究的既定计划（EPSCoR）和数学科学劳动力计划等共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

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.