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🌊 Model Flows with Physics & AI

Shuolin “Shawn” Li, Ph.D.
Postdoctoral Scientist Fellow
Data Science Institute, Columbia University, New York, USA
Email: shuolin.li@columbia.edu

🔬 About Me

I lead the Machine Learning for Data Assimilation project at LEAP — NSF’s Science and Technology Center focused on Learning the Earth with AI and Physics. At Columbia University, I am jointly mentored by Professors Pierre Gentine and Tian Zheng(Department of Statistics).

I develop physics-grounded, uncertainty-quantified models that fuse turbulence theory with generative machine learning to predict how Earth’s surface and climate extremes evolve under intensifying human pressure. Turbulence is the unifying driver—governing sediment transport, shaping river and coastal morphodynamics, and controlling boundary-layer processes that modulate heat-wave intensity and persistence. I seek to link small-scale turbulent energetics to basin-to-regional landscape evolution and risk, yielding tools that support nature-based solutions and real-time climate decision-making. My research is at the interface of (i) sediment geomorphology, (ii) turbulence and hydrodynamics, and (iii) generative machine learning.

🎓 Education

I earned my Ph.D. degree in Fluid Dynamics and Hydrology (on Applied Math and Statistics) from Duke University (USA) in April 2023, where I was fortunate to have been advised by Professor Gabriel “Gaby” Katul, a member of the National Academy of Engineering. Additionally, I was guided by Professor Vahid Tarokh, also an NAE member, and obtained a Master of Science degree in Computer Science concentrated on Artificial Intelligence.

I completed a Master’s degree in Mechanical Engineering (minor in Climate Science) at Cornell University (USA) in June 2019, following an earlier degree in Environmental Engineering Science from Northwestern University (USA), where I graduated in May 2018—marking the beginning of my academic journey in the United States.

🏅 Honors & Service

  • Convener, AGU 2025: Transport & Interactions in Aquatic Ecosystems Across Scales
  • Best Student Paper Award, CVPR EarthVision: ML for Data Assimilation, 2024
  • Associate Editor, Journal of Hydrology (Q1, 2023–2027)
  • Associate Editor, Journal of Geophysical Research: Machine Learning and Computation (Q1, 2025–Present)
  • Leadership Award, Duke Engineering Ph.D. Plus Program, 2023
  • Advisory Board Member, Ph.D. Plus, Duke Engineering, 2022–2023
  • Vice President, Duke Chinese Students & Scholars Association, 2021–2022
  • Award for Outstanding Self-Funded Students Abroad, China Ministry of Education, 2020

“Modeling nature demands both rigor and creativity — I try to bring both.”