In his talk, J. LeBeau presented his work improving both the accuracy and precision of STEM imaging. While electron microscopy has been revolutionized by the aberration corrector, which dramatically improved spatial resolution, real-space distance measurements have remained semi-quantitative. In particular, accuracy and precision for scanning transmission electron microscopy (STEM) was significantly hampered by the presence of sample drift and scan distortion. Until recently, this limitation has obscured the capabilities to characterize minute changes to the atomic structure that can ultimately define material properties. He discussed his approach to resolve this problem, called revolving scanning transmission electron microscopy (RevSTEM). The method uses a series of fast-acquisition STEM images, but with the scan coordinates rotated between successive frames, which encodes drift rate and direction to the resulting image distortion. Multiple case studies were presented to highlight the power of this new technique to characterize materials. For example, picometer precise measurements were shown to enable the direct quantification of static atomic displacements within a complex oxide solid solution due to local chemistry. He also discussed recent work implementing deep convolutional neural networks to autonomously quantify electron diffraction data. The networks were shown to first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data for sample thickness and tilt measurements. The performance of the network was explored as a function of a variety of variables including thickness, tilt, and dose. The processing speed was also shown to far outpace a least squares approach by orders of magnitude. He also discussed the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis.