The field of system identification and dynamical systems is moving towards the development of more efficient and stable algorithms for modeling complex systems. Researchers are exploring new methods for estimating model equations from data, including sparse and nonparametric techniques, which can capture nonlinearities in complex systems without requiring a priori information about their functional form. Another area of focus is the development of numerically efficient and stable algorithms for kernel-based regularized system identification, which can be used to identify models of dynamical systems from data. Additionally, there is a growing interest in applying data-driven system identification techniques to specific domains, such as biology and photosynthesis regulation. Notable papers in this area include: The paper on sparse and nonparametric estimation of equations governing dynamical systems, which introduces a novel framework that integrates sparse parametric estimation with nonparametric techniques. The paper on numerically efficient and stable algorithms for kernel-based regularized system identification, which derives and exploits an alternative Givens-vector representation of kernel matrices to yield more accurate results than existing algorithms.