Understanding how the Sun operates is the cornerstone of our ability to predict solar activity on long and short time scales. The project aims to study the solar activity phenomena from the interior to the corona through a comprehensive analysis of observational data from space and ground-based observatories, such as magnetograms, subsurface flows, solar oscillations, and others. This research will enable the development of a new generation of data assimilation models to predict solar activity. The project goal will be achieved through state-of-the-art data analysis and modeling techniques, machine-learning approaches, principal component analysis, and others. The research plan includes collecting and analyzing observational data in the form of a data processing pipeline, including data from Solar Dynamics Observatory and other space missions, with possible extension to historical (e.g., Solar and Heliospheric Observatory) and ground-based observations. In addition, advanced mathematical methods of data analysis, theoretical models, and simulations will be employed to cross-analyze the diverse data sets and models and investigate various aspects of solar activity. Therefore, this opportunity encourages collaborations of researchers from heliophysics, magnetohydrodynamics, computational physics, and data and computer sciences.