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Regularization plays a vital role in achieving complete answers of complex inverse problems and in resolving ill conditioning associated with factors such as limited data and uncertainties of the experimental environment. Recent advances have been made on developing different types of regularizers based on prior knowledge of the unknown parameters. Alternatively, Observations obtained from multiple modalities provide another form of regularizer for improved solution and incorporation of available knowledge, whether as a result of complementary information or as a means to lower measurement noise. This minisymposium brings together experts from the areas of optimization, numerical methods, and a wide range of imaging applications to discuss new regularizing approaches and identify promising future research directions.