Her areas of expertise include data/physics-driven machine learning, multiscale and multiphysics simulations, as well as numerical methods such as operator splitting, spectral methods, and inverse problems. In addition, she is active in other machine learning topics, including adversarial training and hybrid ML models. During her research career, she has published several papers in peer-reviewed journals and conferences, and collaborated on interdisciplinary projects involving physics and data science. She also has experience working with high-performance computing clusters and implementing efficient algorithms using MATLAB and Python.