Bio
My research interests lie at the intersection of scientific computing and machine learning. My previous research focused on developing boosting methods, such as adaptive sampling and ensemble learning, for machine-learning-based PDE solvers. Currently, I am exploring broader topics in physics-informed learning and operator learning, aiming to bridge numerical methods and data-driven approaches to address challenges in this area.