We describe ongoing work on BiG-AMP, a novel approximate-message-passing based solution to bilinear matrix recovery, with applications to dictionary learning, NMF, matrix completion, and robust PCA.  BiG-AMP tackles inference problems where the matrix-valued observation Y has elements conditionally independent given Z=A*X, where A and X (or sometimes Z) are the unknown matrices of interest. In this talk, we provide a brief overview of the approach and demonstrate it performs favorably with respect to state-of-the-art algorithms for dictionary recovery and non-negative matrix factorization on both synthetic and real-world datasets.