Spring Lecture Series in Computational and Applied Mathematics

University Of Arkansas, Fayetteville AR

Investigators

Abstract

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.php 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.