This talk will discuss how to bring together hierarchical Bayesian techniques in inverse problems with novel ideas leveraging data science techniques and state of the art methods to address large scale computing challenges arising in a number of important real world applications. In particular, we will focus on dictionary learning methods applied to inverse problems, design of informative dictionaries and sparsity promoting techniques.