The field of complex systems is witnessing a significant shift towards the adoption of topological data analysis (TDA) techniques to uncover hidden patterns and relationships in complex networks and high-dimensional data. Researchers are leveraging TDA tools, such as persistent homology and simplicial complexes, to develop innovative methods for distinguishing between simple and complex contagions, clustering complex datasets, and analyzing geometric features of network contagion. These advances have far-reaching implications for various applications, including recommender systems, AIOps solutions, and semiconductor image analytics. Notably, the integration of TDA with deep learning and self-supervised learning techniques is enabling the development of more accurate and robust models for clustering and classification tasks. Furthermore, the use of kinetic data structures and bottleneck distance computations is facilitating the analysis of dynamic data and geometric matching problems. Overall, the field is moving towards a more nuanced understanding of complex systems, driven by the advent of TDA and its applications. Noteworthy papers in this regard include: Extended Persistent Homology Distinguishes Simple and Complex Contagions with High Accuracy, which demonstrates the effectiveness of EPH in detecting simple and complex contagion processes. Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques, which introduces a novel framework for unsupervised image clustering using TDA and deep learning techniques.