Bio
My research interests span a range of topics at the interface of computational science and machine learning. Current efforts are focused on the development of foundation models for accelerating physical simulations, physics-informed machine learning, neural operators, and uncertainty quantification. By bridging the gap between data-driven approaches and scientific knowledge, my group’s work is paving the way for more accurate, efficient, and interpretable simulations across fields like Earth system modeling, fluid dynamics, and materials science. Our methods not only aim to accelerate scientific discovery but also to address real-world challenges in engineering design and the optimization of complex physical systems.