Often in applications ranging from medical imaging and sensor networks to error correction
and data science (and beyond), one needs to solve large-scale linear systems in which a …

We consider linear systems where consists of normalized rows,, and where up to entries of
have been corrupted (possibly by arbitrarily large numbers). Haddock, Needell, Rebrova & …

Complex structured data-driven applications frequently encompass a higher-order
connectivity or interaction among data samples and can be represented by a multilayer …

Develo** large-scale distributed methods that are robust to the presence of adversarial or
corrupted workers is an important part of making such methods practical for real-world …

The use of all samples in the optimization process does not produce robust results in
datasets with label noise. Because the gradients calculated according to the losses of the …

Regularization is essential for avoiding over-fitting to training data in network optimization,
leading to better generalization of the trained networks. The label noise provides a strong …

In this thesis we study efficient algorithms for solving very large linear algebra problems. We
first consider the Kaczmarz method for solving linear systems, and develop a variant that is …

The COVID-19 epidemic has had a major global impact on humanity and the economy.
Analyzing the effect of the COVID-19 pandemic can provide guidance for future pandemics …