Modeling and simulations are of tremendous value in quantitative science as they allow an understanding of observations when the ground truth is known. In this context, they provide the means to untangle methodological confounds from experimental observations, and allow a quick and efficient understanding of the underlying phenomena of interest under various scenarios with high statistical accuracy. I have extensively used simulations to study spatiotemporal dynamics in EEG and MEG as well as connectivity and graph theory measures. More recently, I joined a multidisciplinary collaboration dedicated to modeling the human brain by utilizing electrodynamical models to simulate brain activity using structural connectivity data as input. Experimental observations as measured by EEG, MEG and fMRI are derived from these simulations. I am interested in pursuing a wide range of applications related to this utility by varying the connectivity inputs to better understand brain function as well as neural disorders.