My research and academic interests are at the intersection of artificial intelligence (AI) and traditional scientific and engineering disciplines. Here are some key areas: - AI for Science and Engineering: Using AI techniques to solve complex problems in scientific and engineering domains, leveraging machine learning, deep learning, and optimization methods. - Data Assimilation: Integrating observational data with computational models to improve predictions and simulations, particularly in fields like climate science, meteorology, and engineering. - Reduced Order Modeling: Developing simplified models that capture the essential dynamics of complex systems, enabling faster and more efficient simulations while maintaining accuracy. - Nuclear Engineering: Applying advanced computational methods, including AI and reduced order modeling, to address challenges in nuclear energy, reactor design, safety analysis, and related areas. My work involves interdisciplinary collaboration, combining expertise in AI, computational science, and engineering to tackle cutting-edge problems in these fields.