The field of geometric analysis and machine learning is rapidly evolving, with a focus on developing innovative methods for predicting geometric deviations, mitigating biases in surgical operating rooms, and creating emergent morphogenesis via planar fabrication. Researchers are exploring new approaches to decouple geometry from optimization in 2D irregular cutting and packing problems, and investigating the use of breath as a biomarker for health monitoring. Additionally, there is a growing interest in developing robust methods for physical layer signal authentication, asymmetric stereo matching, and geometric shape assembly. Noteworthy papers include: Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology, which achieved a prediction accuracy of 0.012 mm at a 95% confidence level. Mitigating Biases in Surgical Operating Rooms with Geometry, which demonstrated that geometric representations capture more meaningful biometric features than RGB models. Emergent morphogenesis via planar fabrication enabled by a reduced model of composites, which enabled efficient computational design and scalable manufacturing of 3D forms.