RTG: Data-Intensive Research and Computing at the University of California, Merced

该计划的总体目标是解决国家培养下一代劳动力的需求，使其在计算和数据科学领域具有高技能。为了实现这一目标，我们建议建立数据密集型研究与计算（DIRAC）研究训练组（RTG）。DIRAC RTG利用加州大学默塞德分校应用数学学院的优势，为本科生、研究生和博士后研究人员提供培训经验，为他们在学术界、工业界和政府的职业生涯做好准备。一个关键的挑战是，计算科学和数据科学涉及数学、科学、技术和工程之间不可分割的联系。加州大学默塞德分校应用数学专业能够很好地应对这一挑战，因为它的三种主要的科学方法将成为RTG的核心：(1)物理和生物系统的建模，(2)科学计算，(3)数据分析。为了向受训者提供计算和数据科学方面的协作培训经验，DIRAC RTG将培育小型指导和研究培训（SMaRT）团队，这些团队是垂直整合的、以社区为基础的指导结构，每个团队都以四个研究主题之一为中心：(I)能源与环境，（II）传感和成像，（III）数学生物学和（IV）数值分析。这些SMaRT团队将为个人提供支持，指导他们的培训，并培养一支训练有素、灵活的劳动力队伍，为快节奏的现代计算研究做出贡献。此外，DIRAC RTG致力于为加州大学默塞德分校应用数学学院积极招收的本科和研究生课程中代表性不足的第一代学生提供服务。每个SMaRT团队都有积极的措施来招募包容性的学员团队，为学员提供持续的指导和支持，以留住这些学员，并培养学员在完成培训项目后取得成功所需的专业技能。计算科学和数据科学是科学探究和发现的新范式，它结合了数学、统计学、计算机科学和特定领域的知识。由于计算和数据驱动的科学相对较新，它们与现有数学培训计划的自然有效整合仍有待完善。这个RTG项目汇集了加州大学默塞德分校的整个应用数学学院，共同的目标是为本科生、研究生和博士后开发一个现代化的综合培训计划，以自然有效的方式整合这些学科，为受训者在学术界、政府和工业界的广泛领域的成功职业生涯做好准备。建议的RTG计划有三个主要组成部分：(1)一个与研究紧密结合的平衡课程，并使其现代化，以反映计算和数据科学的当前需求；(2)垂直整合的指导计划，包括本科生、研究生、博士后助理和教职员工；(3)发展广泛、充满活力和支持性的社区，重点关注教育、研究和专业发展。所考虑的专题研究领域侧重于及时和重要的问题，分为(I)能源与环境，（II）传感与成像，（III）数学生物学和（IV）数值分析。这个培训项目的重点是提高每个学员在研究过程中的技能和经验（而不仅仅是研究成果），并提供实用的教学培训、沟通技巧和专业发展。这个RTG中的活动对于系统地改进加州大学默塞德分校现有的培训项目至关重要，这可以作为其他项目的典范。这些制度上的变化将深刻地改变数学课程，并对培养下一代计算型和数据型科学家产生持久的影响。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。

The overarching objective of this program is to address the national need to train the next-generation workforce to be highly skilled in the field of computational and data-enabled sciences. To achieve this objective, we propose to establish the Data-Intensive Research And Computing (DIRAC) Research Training Group (RTG). The DIRAC RTG leverages strengths of the UC Merced Applied Mathematics faculty to provide undergraduate and graduate students, and postdoctoral researchers a training experience that prepares them for careers in academia, industry, and government. A key challenge is that computational and data-enabled sciences involve inextricable ties between mathematics, science, technology, and engineering. UC Merced Applied Mathematics is well positioned to address this challenge because of its three main approaches to science that will be at the core of this RTG: (1) modeling of physical and biological systems, (2) scientific computing, and (3) data analysis. To provide its trainees a collaborative training experience in computational and data-enabled sciences, the DIRAC RTG will foster Small Mentoring and Research Training (SMaRT) teams, which are vertically integrated, community-based mentoring structures, each centered on one of four research themes: (I) energy and the environment, (II) sensing and imaging, (III) mathematical biology, and (IV) numerical analysis. These SMaRT teams will provide support to individuals, guide their training, and produce a well-trained, nimble workforce that can contribute to the fast-paced modern computational research. Additionally, the DIRAC RTG is committed to serving the underrepresented and first-generation students that UC Merced Applied Mathematics actively recruits into its undergraduate and graduate programs. Built into each SMaRT Team are active measures for recruiting inclusive teams of trainees, providing continuous mentorship and support to retain these trainees, and developing the professional skills of trainees needed to succeed upon completion of this training program.Computational and data sciences are new paradigms for scientific inquiry and discovery that incorporate mathematics, statistics, computer science, and domain-specific knowledge. Since computational and data-enabled sciences are relatively new, their natural and effective integration into existing training programs in mathematics remains to be perfected. This RTG project brings together the entire Applied Mathematics faculty of UC Merced with the common goal of developing a modernized and comprehensive training program for undergraduate and graduate students, and postdoctoral associates that integrates these subjects in a natural and effective way and prepares the trainees for successful careers in academia, government, and industry in a broad range of fields. The proposed RTG project has three major components: (1) a balanced curriculum tightly integrated with research which is modernized to reflect the current needs in computational and data-enabled sciences; (2) a vertically integrated mentoring program that engages undergraduate, graduate, postdoctoral associates, and faculty participants; and (3) the development of extensive, dynamic, and supportive communities focused on education, research, and professional development. The thematic research areas considered focus on timely and important issues and are divided into (I) energy and the environment, (II) sensing and imaging, (III) mathematical biology, and (IV) numerical analysis. This training program focuses on enhancing each trainee's skills and experience in the process of research (as opposed to just the products of research) and provides practical teaching training, communication skills, and professional development. The activities in this RTG are crucial to making systematic improvements to the existing training program at UC Merced, which can then serve as a model for other programs. These institutional changes will profoundly transform mathematics programs and have long-lasting impact on training the future generations of computational and data-enabled scientists.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.