RTG: Computational and Applied Mathematics in Statistical Science

这个研究培训小组(RTG)项目支持创建一个动态的、互动的、垂直整合的学生和研究人员社区，他们在计算、应用数学和统计学领域共同工作。该活动认识到应用数学和统计学正变得日益一体化的方式。例如，反映潜在物理规律的物理问题的机械模型正在与数据驱动的方法相结合，在这些方法中，统计推断和优化发挥着关键作用。这些发展正在改变整个统计学和应用数学的研究议程，数据分析中的基本问题导致了数学和统计研究的新领域。其结果是，越来越需要以新的方式培训下一代统计学家、计算数学家和应用数学家，以应对自然科学和社会科学中以数据为中心的问题。该项目的研究和教育活动涉及统计、计算和应用数学。这项研究包括化学和分子动力学、气候科学、计算神经科学、凸优化和非线性优化、机器学习和统计遗传学。研究团队由12名教职员工组成，其中包括芝加哥丰田理工学院和阿贡国家实验室的研究人员。RTG以学生垂直整合的研究体验为中心，包括本科生和研究生教育的创新。这些措施包括成立学生和博士后工作组，以提供一个互动的环境，让学生在广泛的学科中积极探索计算、数学和统计学方面的创新。博士后将在指导研究生和高级本科生方面发挥领导作用。RTG的参与者将获得教育经验，为他们在工业、政府和学术界的职位做好充分准备，并有能力采用来自计算、数学和统计科学的方法来解决问题。

This Research Training Group (RTG) project supports creation of a dynamic, interactive, and vertically integrated community of students and researchers working together in computational and applied mathematics and statistics. The activity recognizes the ways in which applied mathematics and statistics are becoming increasingly integrated. For example, mechanistic models for physical problems that reflect underlying physical laws are being combined with data-driven approaches in which statistical inference and optimization play key roles. These developments are transforming research agendas throughout statistics and applied mathematics, with fundamental problems in analyzing data leading to new areas of mathematical and statistical research. A result is a growing need to train the next generation of statisticians and computational and applied mathematicians in new ways, to confront data-centric problems in the natural and social sciences. The research and educational activities of the project lie at the interface of statistics, computation, and applied mathematics. The research includes investigations in chemistry and molecular dynamics, climate science, computational neuroscience, convex and nonlinear optimization, machine learning, and statistical genetics. The research team is made up of a diverse group of twelve faculty, including researchers at Toyota Technological Institute at Chicago and Argonne National Laboratory. The RTG is centered on vertically integrated research experiences for students, and includes innovations in both undergraduate and graduate education. These include the formation of working groups of students and postdocs to provide an interactive environment where students can actively explore innovations in computation, mathematics, and statistics in a broad range of disciplines. Post-docs will assume leadership roles in mentoring graduate students and advanced undergraduates. Participants in the RTG will receive an educational experience that provides them with strong preparation for positions in industry, government, and academics, with an ability to adopt approaches to problem solving that are drawn from across the computational, mathematical, and statistical sciences.