My research focuses on improving the scalability of ground motion simulations using machine learning. While physics-based simulations are important for complementing seismic recordings, they can be computationally intensive, which limits their scalability across large areas or multiple events. To address these limitations, I am developing machine learning algorithms to efficiently predict strong ground motion patterns across wide geographic regions and for a large number of earthquake events. The aim is to make high-resolution seismic simulations more accessible and practical, which could enhance risk assessment and emergency preparedness.
