Data-driven tools in gravitational wave parameter estimation and population analysis

As gravitational wave (GW) detectors sensitivity improves, the rate we observe GWs increases geometrically. This demands innovations in our data analysis tools that make them capable of handling both the size and complexity of the incoming catalog of GW signals. In this talk, we present a number of new tools for different GW data analysis tasks, including a parameter estimation pipeline that is capable to converge within minutes, a flexible normalizing flow-based population analysis framework, and a code to distill information from flexible population models using symbolic regression.