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layout: about title: About permalink: / subtitle:
profile: align: right image: shawn1.jpg image_circular: false more_info: > <p>Data Science Institute, Columbia University</p> <p>New York, NY, 10027, USA</p>
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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
I am a Full Professor at Wuhan University, working at the intersection of Artificial Intelligence, Hydrology, and Fluid Dynamics. Previously, I was a Research Scientist at the Data Science Institute at Columbia University, where I led the Machine Learning for Data Assimilation project within LEAP — an NSF Science and Technology Center for Learning the Earth with AI and Physics. Prior to joining Columbia, I earned a Ph.D. in Fluid Dynamics and an M.S. in Computer Science from Duke University in 2023. I also completed an M.S. in Mechanical Engineering at Cornell University in 2019, following my first U.S. degree in Environmental Engineering at Northwestern University in 2018.
555 your office number
123 your address street
Your City, State 12345
layout: about title: About permalink: / subtitle:
profile: align: right image: shawn1.jpg image_circular: false more_info: > <p>Data Science Institute, Columbia University</p> <p>New York, NY, 10027, USA</p>
selected_papers: true social: true
announcements: enabled: true scrollable: true limit:
latest_posts: enabled: true scrollable: true limit: 3 —
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
I am a Full Professor at Wuhan University, working at the intersection of Artificial Intelligence, Hydrology, and Fluid Dynamics. Previously, I was a Research Scientist at the Data Science Institute at Columbia University, where I led the Machine Learning for Data Assimilation project within LEAP — an NSF Science and Technology Center for Learning the Earth with AI and Physics. Prior to joining Columbia, I earned a Ph.D. in Fluid Dynamics and an M.S. in Computer Science from Duke University in 2023. I also completed an M.S. in Mechanical Engineering at Cornell University in 2019, following my first U.S. degree in Environmental Engineering at Northwestern University in 2018.