The field is witnessing a significant shift towards leveraging geometric and representation learning techniques to tackle complex problems. Researchers are exploring innovative methods to improve image retargeting, surface parameterization, and optimal transport in various domains, including computer vision and graphics. Notably, the use of self-supervised learning, mesh deformation, and quasiconformal maps is becoming increasingly popular. Additionally, there is a growing interest in developing efficient algorithms for high-dimensional diffeomorphic mapping, tensor decomposition, and MRI representation learning. These advances have the potential to revolutionize various applications, including image processing, computer-aided design, and medical imaging. Noteworthy papers include: Object-IR, which achieves state-of-the-art performance in image retargeting by leveraging object consistency and mesh deformation. Hyperbolic Optimal Transport, which proposes a novel algorithm for computing optimal transport maps in hyperbolic space. Metadata-Aligned 3D MRI Representations, which introduces a metadata-guided framework for learning MRI contrast representations. No-rank Tensor Decomposition Using Metric Learning, which presents a framework for tensor decomposition grounded in metric learning. Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks, which proposes a data-informed approach for compressing neural networks.