The recent developments in the research area of data analysis and signal processing have shown a significant shift towards more sophisticated and scalable methods for handling complex, multi-modal, and non-stationary data. There is a growing emphasis on integrating Bayesian approaches with tensor decompositions to enhance the interpretability and adaptability of models, particularly in scenarios involving large-scale and heterogeneous datasets. Innovations in tensor factorization techniques, such as the introduction of personalized and scalable Bayesian methods, are addressing the need for more flexible and efficient data fusion strategies. Additionally, advancements in dynamic mode decomposition (DMD) are being driven by the incorporation of physics-informed constraints, which not only improve computational efficiency but also ensure adherence to physical laws, thereby enhancing the accuracy and reliability of predictions in dynamical systems. Furthermore, there is a notable trend towards the development of novel algorithms for signal decomposition that can operate at sub-Nyquist sampling rates, offering potential improvements in accuracy, efficiency, and noise robustness. These developments collectively indicate a move towards more integrated, scalable, and physically grounded approaches in data analysis and signal processing, with a focus on real-time applications and the handling of increasingly complex data types.