Block Low-Rank Matrices for Modern Scientific Computing
Block low-rank matrices provide framework for compressing and accelerating large-scale computations. In this talk, I will introduce the basic principles behind these matrix formats, highlight notable algorithms that exploit their structure, and discuss their growing role in modern computational mathematics. Applications ranging from seismic imaging and computational biology to artificial intelligence will be used to illustrate the broad impact of block low-rank methods on science and engineering.
Overview
Presenters
Brief Biography
Dr. Daria Sushnikova is a Postdoctoral Research Fellow at KAUST specializing in fast algorithms for large-scale scientific computing. She earned her Ph.D. in Mathematical Modeling and Numerical Methods at the Institute of Numerical Mathematics, Russian Academy of Sciences, under Prof. Ivan Oseledets. Her research spans numerical linear algebra, hierarchical matrices, and high-performance computing, with contributions such as the FMM-LU solver, Compress-and-Eliminate factorization, and H2-MG algorithm. She received the Rising Stars in Computational & Data Sciences Award (2020).
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Daria Sushnikova
- Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering