The field of linear algebra and matrix computation is experiencing significant advancements, driven by the development of randomized and hybrid methods. These innovative approaches are designed to address the challenges of large-scale matrix problems, which are ubiquitous in various scientific applications, including imaging, genomics, and time-varying systems. The use of randomized techniques, such as the randomized extended Kaczmarz method and randomized numerical linear algebra, has shown great promise in improving computational efficiency and accuracy. Furthermore, the integration of greedy strategies and semi-randomized methods has led to the creation of fast and flexible algorithms for solving large-scale linear systems and ill-posed problems. Notable papers include: The paper on two-dimensional greedy randomized extended Kaczmarz methods, which proposes a novel method for solving large linear least-squares problems with improved convergence. The paper on fast flexible LSQR with a hybrid variant, which introduces a novel fast flexible Golub-Kahan bidiagonalization method and develops the fast flexible LSQR algorithm, offering comparable computational cost to FCGLS while supporting hybrid regularization.