Bio
My research interests lie at the intersection of deep learning and scientific computing, with a focus on developing advanced techniques for modeling and simulation. In the past, I have worked extensively on physics-informed neural networks (PINNs), contributing to their improved training methodologies and performance. More recently, my projects have shifted towards building better operator learning methods, aiming to bridge the gap between traditional scientific computing and modern machine learning approaches.