Large matrices arise in many ML applications, including as representations of datasets, graphs, model weights, first and second-order derivatives, etc. Randomized Numerical Linear Algebra (RandNLA) is an area that uses randomness to develop improved algorithms for ubiquitous matrix problems. The area has reached a certain level of maturity, and current efforts of incorporating RandNLA algorithms into core numerical libraries, as well as recent advances in ML, Statistics, and Random Matrix Theory…