RTG: Optimization and Inversion for the 21st Century Workforce

该研究培训小组（RTG）旨在培养新一代的应用数学家，他们是数学优化，反演和数据科学领域的专家。学员将获得可视化、分析和学习现实世界数据的前沿知识和经验。RTG将加强研究生和博士后项目，以吸引全国顶尖学生，并将他们安置在顶级职位上。研究人员将通过在高中和本科早期阶段的努力使招募多样化，面向代表性不足的群体，以扩大数学的参与。RTG将为学生引入新颖的转变体验，并强调关键的职业过渡点，以吸引和留住学生从事与数学相关的职业。RTG项目将鼓励处于学术生涯不同阶段的参与者之间的互动、协作和指导。核心例子包括与初中和高中教师合作，使数学对学生来说更容易理解，更令人兴奋，垂直整合的重点阅读/研究小组，一项科学研究计划，大大增加了参与研究的一年级本科生的数量，以及北极海冰的极地研究体验，为学生提供了一个独特的动手机会，收集，分析和建模他们自己的数据，关闭理论和实践之间的循环。数学优化、反演和数据科学在科学、工程和医学领域的应用中发挥着至关重要的作用。这个RTG利用数学教师在这些和相关领域的专业知识来培训和指导从高中到博士和博士后学者的各级学生。一些核心项目将包括超材料、多孔介质、光子学、气候建模、机器学习、遥感、极地生态、医学成像、地球物理勘探、药物输送和发现以及不确定性量化的优化设计。RTG将引入新的以优化为中心的研究生课程，为各级学员提供在垂直整合环境中解决重要跨学科问题的重要经验，并模仿成功的工程课程，举办数学设计竞赛来激励学生，并举办主题RTG会议，为RTG参与者和国际知名研究人员之间提供合作机制。这个RTG项目将使更广泛的数学界和其他领域受益，因为参与其中的学生和博士后在21世纪的劳动力中担任研究人员和教育工作者的主要角色。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) aims to develop a new generation of applied mathematicians who are experts in the areas of mathematical optimization, inversion, and data science. Trainees will gain cutting-edge knowledge and experience in visualizing, analyzing, and learning from real-world data. The RTG will strengthen the graduate and postdoctoral programs to attract top students in the nation and place them in top jobs. The investigators will diversify the recruitment with efforts at the high school and early undergraduate levels, geared towards underrepresented groups to broaden participation in mathematics. The RTG will introduce novel transformative experiences for students and emphasize on critical career transition points to attract and retain students into math-related careers. The RTG project will encourage interaction, collaboration, and mentorship between participants at different stages of their academic careers. Core examples include working with junior high and high school teachers to make math more accessible and exciting to students, vertically integrated Focused Reading/Research Groups, a Science Research Initiative to significantly increase the number of first-year undergraduates involved in research, and Polar Research Experiences on Arctic Sea ice that provide a unique hands-on opportunity for students to gather, analyze, and model their own data, closing the loop between theory and practice.Mathematical optimization, inversion, and data science play a crucial role in applications across the sciences, engineering, and medicine. This RTG leverages the expertise of mathematics faculty in these and related areas to train and mentor students across levels ranging from high school to doctoral and postdoctoral scholars. Some of the core projects will include optimal design of metamaterials, porous media, photonics, climate modeling, machine learning, remote sensing, polar ecology, medical imaging, geophysical exploration, drug delivery and discovery, and uncertainty quantification. The RTG will introduce a new optimization-centered graduate curriculum, offer trainees at all levels significant experience working on important interdisciplinary problems in vertically integrated settings, and, mimicking successful engineering classes, a mathematical design competition to motivate students, and a thematic RTG conference to provide collaborative mechanisms between RTG participants and internationally renowned researchers. This RTG project will benefit the broader math community and beyond as the involved students and postdocs assume leading roles as researchers and educators in the 21st-century workforce.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.