IRES Track 1 Graduate Research In Industrial Projects for Students - Berlin

该项目将支持由纯粹与应用数学研究所（IPAM）领导的“研究生水平的工业项目研究-柏林”（grip）。该计划将为数学及相关学科的研究生提供机会，通过与德国柏林的研究校园MODAL合作，在柏林从事行业赞助的研究问题。莫代尔现有的工业合作伙伴隶属于他们的实验室，为他们的研究小组提供有趣和具有挑战性的研究问题。该项目将持续八周；每年夏天将有8名美国研究生参加。每年有一半的美国学生将是女性，其中一名或多名学生将是代表性不足的种族群体的成员。MODAL将招募8名欧洲学生组成4个研究小组，每个小组2名美国学生和2名欧洲学生，并提供学术导师。该项目不仅将在数学应用于现实问题方面进行有趣的研究，还将为不同群体的美国学生提供对工业研究的见解，并为他们提供解决工业问题的实践经验。该计划的目标之一是让学生意识到多种职业道路向他们开放，并强调他们的数学技能在应用于有趣的现实生活问题时的价值和适应性。学生还将获得与外国同事和公司合作的宝贵经验。这些项目将是MODAL现有行业研究伙伴关系的自然产物，并将与IPAM合作选择。参加者将提交一份关于其工作的最终报告，并鼓励提交其工作，以便在会议上发表和/或在技术期刊上发表。预计许多参与者将继续他们在项目中开始的研究和合作。应用数学研究已经变得越来越重要，与工程问题和日常生活息息相关。越来越多的核心应用数学主题，从应用概率到偏微分方程，再到优化，在材料科学、数学生物学、运筹学以及最近的机器学习等领域找到了重要的应用。将数学技术应用于现实生活中的问题，既需要对数学理论有深刻的理解，也需要对问题的物理和技术方面有敏锐的认识，以及任何数值算法或模拟的实现细节。由于这个原因，这种成功的应用程序通常是个人和团队努力的结合，主要问题被划分为更专门的部分，每个团队成员的特定专业知识被用于每个这样的组件。研究项目的确切主题每年都会发生变化，并取决于所涉及的行业和学术研究人员的兴趣。一般来说，这些项目将应用数学技术来解决优化（如铁路网络吞吐量的优化）、高效模拟（如现代纳米光子器件的模拟）和机器学习（如医疗海量数据集的分析）等问题。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 project will support the "Graduate-level Research in Industrial Projects for Students-Berlin" (GRIPS) led by the Institute for Pure and Applied Mathematics (IPAM). The program will offer graduate students in mathematics and related disciplines the opportunity to work on industry-sponsored research problems in Berlin through a collaboration with Research Campus MODAL in Berlin, Germany. MODAL has existing industrial partners affiliated with their laboratories that provide their research groups with interesting and challenging research problems. The program will be eight weeks in length; eight US graduate students will participate each summer. Half of the US students each year will be women, and one or more will be a member of an underrepresented ethnic group. MODAL will recruit 8 European students to form 4 research groups of two US and two European students each, and provide academic mentors. The program will not only produce interesting research in applications of mathematics to real-life problems, but will give a diverse group of US students an insight into industrial research and provide them with hands-on experience of working on industrial problems. One of the goals of the program is to make the students aware of the multiple career pathways open to them and highlight the value and adaptability of their mathematics skills as applied to interesting real-life problems. The students will also receive invaluable experience collaborating with foreign colleagues and companies. The projects will be the natural outgrowth of MODAL's existing industry research partnerships, and will be selected in collaboration with IPAM. Participants will produce a final report on their work, and will be encouraged to submit their work for conference presentations and/or publication in technical journals. It is expected that many of the participants will continue the research and collaborations they started in the program.Applied mathematics research has become increasingly important and relevant to engineering problems and, indeed, to everyday life. Increasingly, core applied mathematics topics, ranging from applied probability, to partial differential equation, to optimization, find important applications in areas such as materials science, mathematical biology, operations research, and, more recently, machine learning. Applications of mathematical techniques to real-life problems require both a deep understanding of mathematical theory, as well as acute awareness of both the physical and technical aspects of the problem, as well as implementation details of any numerical algorithms or simulations. For this reason, such successful applications are often a combination of individual and team efforts, with the main problem being divided into more specialized parts, and specific expertise of each team member brought to bear on each such component.The exact topics of the research projects will change from year to year and will depend on the interest of industry and academic researchers involved. Generally speaking, the projects will apply mathematical techniques to questions of optimization (such as optimization of rail network throughput), efficient simulation (such as simulation of modern nanophotonic devices) and machine learning (such as analysis of medical mass data sets).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.