On August 14, 2023, as part of the conference Numerical Analysis in the 21st Century, Dr. James Baglama gave a talk titled “Golub-Kahan-Lanczos Bidiagonalization (GKLB) Methods for Computing Singular Triplets for Very Large Sparse Matrices and Applications.” The conference was held at the University of Oxford, one of the most prestigious research and educational institutions in the world, more specifically, at the Blavatnik School of Government and Balliol College, Oxford, UK. The talk was an accumulation of research over the past two decades starting with a method called irlba originally developed in 2005. The irlba method is used for computing partial singular value decompositions of very large sparse matrices and is at the core of the proprietary MATLAB’s command svds.m. More recently, the irlba method was implemented in the R programming language and has quickly gathered more than three million downloads. Such wide acceptance of the R-implementation of irlba can be primarily attributed due to its connection to the principal component analysis (PCA) which often plays a key role in the analysis of large data sets arising from applications such as genomics. This year, Dr. Perovic and Dr. Baglama have extended the functionally of the irlba routine to include the ability to deflate the previously computed largest singular triplets in order to get the next set of the largest triplets – this has an immediate impact on the problem of singular value thresholding and applications such as matrix completion, solution of large scale linear discrete ill-posed problems, and analysis of directed networks.