The session welcomes all papers that are related to learning methods to electromagnetic quantitative imaging problems. The session aims at presenting frontier research in the theories, computations, and applications of machine learning for solving electromagnetic quantitative imaging problems. Traditional electromagnetic quantitative imaging approaches include FFT-based, linear equation-based, and full-wave solvers. Different combinations of machine learning techniques and traditional quantitative imaging approaches are welcomed. Applications include but not limited to microwave imaging, through-wall-imaging, subsurface detection, electric impedance or capacitance tomography, biomedical imaging, non-destructive testing/evaluation, remote sensing, phaseless imaging, and millimetre-wave short-range imaging.