Shawn (Shuolin) Li

I develop physics-grounded, uncertainty-quantified models that integrate turbulence theory with generative machine learning to understand and predict the dynamics of environmental and engineered systems. Turbulence serves as the unifying framework of my research: it governs particle transport, shapes riverine and landscape morphodynamics, regulates heat and mass transfer, and influences atmospheric boundary-layer processes and climate extremes.

My research sits at the intersection of:

• Turbulence and fluid dynamics
• Generative machine learning and data science
• Hydrology, sediment transport, and geomorphology
• Heat and mass transfer and data-center cooling