A distributed computing scenario is considered, where the computational power of a set of
worker nodes is used to perform a certain computation task over a dataset that is dispersed …

Distributed matrix computations over large clusters can suffer from the problem of slow or
failed worker nodes (called stragglers) which can dominate the overall job execution time …

One of the major challenges in using distributed learning to train complicated models with
large data sets is to deal with stragglers effect. As a solution, coded computation has been …

We consider the problem of coded computing, where a computational task is performed in a
distributed fashion in the presence of adversarial workers. We propose techniques to break …

The current big data era routinely requires the processing of large-scale data on massive
distributed computing clusters. In these applications, data sets are often so large that they …

Over-complete systems of vectors, or in short, frames, play the role of analog codes in many
areas of communication and signal processing. To name a few, spreading sequences for …

We consider the critical problems of distributed computing and learning over data while
keeping it private from the computational servers. The state-of-the-art approaches to this …

We study coded distributed matrix multiplication from an approximate recovery viewpoint.
We consider a system of computation nodes where each node stores of each multiplicand …

We consider the critical problem of distributed learning over data while keeping it private
from the computational servers. The state-of-the-art approaches to this problem rely on …

Distributed matrix computations-matrix-matrix or matrix-vector multiplications-are well-
recognized to suffer from the problem of stragglers (slow or failed worker nodes). Much of …