Modern gravitational-wave observatories such as LIGO, Virgo, and KAGRA, along with future projects like LISA, are essential tools for understanding the physical processes occurring throughout our Universe. However, the complexity and sheer volume of their data make analysis computationally demanding. My PhD research aims to streamline the analysis of data from these observatories by leveraging modern machine learning techniques, including Simulation-Based Inference (SBI).

Photoactive molecules with precisely tunable absorption and emission properties play a key role in fields such as photocatalysis and green synthesis. While this tuning is conventionally achieved by altering the length of the π-conjugated chromophore, we exploit donor-acceptor substitution in compact aromatic building blocks to achieve this tuning and to understand how these molecules drive specific photochemical processes.