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layout: about title: About permalink: / subtitle:
profile: align: right image: shawn.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 predict how Earth’s surface and climate extremes evolve under intensifying human pressures. Turbulence serves as the unifying framework of my research: it governs particle transport, shapes landscape morphodynamics, and modulates boundary-layer dynamics that influence the onset, intensity, and persistence of heat waves.
My research sits at the intersection of:
- Turbulence and hydrodynamics
- Heat transfer and data-center cooling
- Sediment transport and geomorphology
- Generative machine learning for probability distributions
I am a research scientist in the Data Science Institute at Columbia University, where I lead the Machine Learning for Data Assimilation project within LEAP — an NSF Science and Technology Center of 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: shawn.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 predict how Earth’s surface and climate extremes evolve under intensifying human pressures. Turbulence serves as the unifying framework of my research: it governs particle transport, shapes landscape morphodynamics, and modulates boundary-layer dynamics that influence the onset, intensity, and persistence of heat waves.
My research sits at the intersection of:
- Turbulence and hydrodynamics
- Heat transfer and data-center cooling
- Sediment transport and geomorphology
- Generative machine learning for probability distributions
I am a research scientist in the Data Science Institute at Columbia University, where I lead the Machine Learning for Data Assimilation project within LEAP — an NSF Science and Technology Center of 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.