Spring Lecture Series in Computational and Applied Mathematics

计算与应用数学春季讲座系列

• 批准号: 2204490
• 负责人: Tulin Kaman
• 金额: $ 2.54万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204490&HistoricalAwards=false
• 关键词: Spring; Lecture; Series; Computational; Applied

This award provides participant support for the Spring Lecture Series (SLS) on `Numerical linear algebra: from scientific computing to data science applications' at the University of Arkansas in Fayetteville, Arkansas, on May 4-6, 2022. Numerical linear algebra is at the core of many fields of science and engineering, whether in solving linear systems that arise from simulations of physical phenomena, or in obtaining various solutions of optimization problems in data related applications. As the world around us is progressively being analyzed or modeled with the help of available data, the types of computational problems encountered are changing, and as a result the field is currently undergoing a deep transformation. The principal speaker at the lecture series, Yousef Saad, will present an overview of methodologies that are used in both the scientific computing and data science disciplines in a series of five lectures. To build a bridge between Scientific Computing and Data Science, one of the goals of the lectures is to introduce data science techniques to non-specialists in scientific computing. Additionally, there will be ten one-hour lectures by invited speakers as well as poster and oral presentation sessions by young researchers which are intended to provide an opportunity for the exchange of ideas and expertise within and between the various computational science domains. Furthermore, a public lecture by Professor Sabine Van Huffel will be accessible to the community at large, and will focus on the power of matrix/tensor algebra and data-driven artificial intelligence (AI) in digital healthcare. A Women in STEM panel in intended to provide an environment for sharing of ideas and experiences with the goal of fostering diversity in research and promoting the participation of underrepresented groups in STEM.The lectures will present fundamental ideas of numerical linear algebra with an emphasis on those methods that have the potential to play an important role in data science, including eigenvalue and singular value problems, sparse matrix techniques, graph-based methods, Krylov subspaces methods, and preconditioning. Part of the lectures will be devoted to specific problems of in data science, covering applications of graph-based methods, randomization methods, network analysis, dimension reduction, and neural networks, among other themes. This lecture series will provide unique opportunities for early career researchers and graduate students to interact with prominent experts in their research areas. More information is available at https://fulbright.uark.edu/departments/math/research/spring-lecture-series/index.phpThis 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.

该奖项为春季讲座系列（SLS）提供参与者支持“数值线性代数：从科学计算到数据科学应用”在阿肯色州费耶特维尔，阿肯色州，2022年5月4日至6日的大学。 数值线性代数是许多科学和工程领域的核心，无论是在解决物理现象模拟中产生的线性系统，还是在数据相关应用中获得优化问题的各种解决方案。随着我们周围的世界在现有数据的帮助下逐渐被分析或建模，遇到的计算问题的类型正在发生变化，因此该领域目前正在经历深刻的变革。该系列讲座的主要演讲者Yousef Saad将在五个系列讲座中概述科学计算和数据科学学科中使用的方法。为了在科学计算和数据科学之间建立桥梁，讲座的目标之一是向科学计算的非专家介绍数据科学技术。此外，将有十个一小时的讲座，由特邀演讲者以及海报和口头介绍会议，由年轻的研究人员，旨在提供一个机会，交流思想和专业知识内和之间的各种计算科学领域。此外，Sabine货车Huelman教授的公开讲座将面向整个社区，并将重点关注矩阵/张量代数和数据驱动的人工智能（AI）在数字医疗保健中的作用。一个妇女在干小组旨在提供一个环境，分享思想和经验，以促进研究的多样性和促进在干的代表性不足的群体的参与的目标。讲座将提出数值线性代数的基本思想，重点放在那些有潜力发挥重要作用的方法，在数据科学，包括特征值和奇异值问题，稀疏矩阵技术，基于图的方法，Krylov子空间方法和预处理。部分讲座将致力于数据科学中的特定问题，涵盖基于图形的方法，随机化方法，网络分析，降维和神经网络等主题的应用。本系列讲座将为早期职业研究人员和研究生提供独特的机会，与他们研究领域的杰出专家进行互动。更多信息可在www.example.com上获得https://fulbright.uark.edu/departments/math/research/spring-lecture-series/index.phpThis奖项反映了NSF的法定使命，并被认为值得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来支持。