Random matrices, originally invented by E. Wigner in the 1950’s to model energy level statistics of heavy nuclei, play a fundamental role in a broad range of modelling quantum mechanical processes, as well as in statistics, evolutionary biology and neuroscience. Random matrices are known to exhibit spectral universality, meaning that their eigenvalue statistics are largely independent of the details of the ensemble. Our project extends this vision of beyond the traditional eigenvalue gap statistics to cover eigenvectors and expectation values of observables measured in experiments. This requires developing new tools, most importantly the theory of multi-resolvent
local laws which serve as a work-horse for many of our results. The second main focus is to extend the well established hermitian theory to the more general and complicated non-hermitian situations, especially because several applications in neuroscience naturally go beyond the hermitian world. As a theoretical research in mathematics, its direct impact on society is not realistically expected, but a deeper understanding of non-hermitian random matrices plays an important role how and to what extent random matrix models can be used in applied sciences.