Large scale matrix computations are at the heart of several modern technologies that are revolutionizing human life. These include, the now ubiquitous deep learning models, large language models, and scientific computing for supporting research in various domains. The sheer size of the data and models in these domains requires such computations to be performed in a distributed manner over large clusters, whereby an overall job is divided into smaller tasks. Unfortunately, these clusters often suffer from the problem of stragglers (slow or failed workers), especially when they are deployed within cloud computing platforms; this can cause an undesirable increase in the overall job execution time. The overall goal of this project is to research techniques for mitigating the effect of stragglers in the specific context of distributed matrix computations. This project will also provide training for students in the usage of cloud platforms. In addition, the project also involves outreach activities to local schools for mathematics tutoring and the creation of K-12 computer science modules. The field of coded computation uses ideas from coding theory to embed distributed matrix computation into the structure of an erasure code. Specifically, the idea is to create redundant tasks by linearly combining the input submatrices such that as long as a minimum number of workers complete their tasks, the overall job can be completed. The vast majority of prior coded matrix computation app