Flatiron Seminar Series: Manas Rachh

Title: The interplay of data, analysis, and fast algorithms

Abstract: Structured matrices are ubiquitous in many areas of science and engineering such as molecular dynamics, antenna and speaker modeling, medical imaging, microfluidic device design, computational statistics, and data visualization to name a few. Traditionally, these matrices tend to be dense and the cost of applying N x N matrices scales like O(N^2). However, large sub-blocks of these matrices tend to be data-sparse. This structure can be exploited to apply matvecs in O(N) CPU time — thus enabling the simulation of much larger computational problems with the same computational budget. This is true, not only in first principle simulations, but also as core components of data analysis pipelines.