Neutron stars: These serve as natural laboratories for studying fundamental matter under extreme conditions. Understanding their interiors requires careful propagation of uncertainties from both raw observations and theoretical models. We develop fast forward-modeling and inference techniques that meaningfully quantify and propagate these uncertainties.
Statistical inference: We design algorithms capable of performing rapid and accurate parameter inference directly from raw, high-dimensional telescope data (e.g., see here).
Astrophysical simulations: Accurate forward modeling is essential for understanding astrophysical phenomena and analyzing observational data. We design such models by leveraging machine learning and high-performance computing.
