Sibo Cheng
About me
I am a Tenure-Track Junior Professor (Chaire de Professeur Junior) at ENPC, Institut Polytechnique de Paris and an Honorary Research Fellow at Imperial College London.
My research combines data assimilation, machine learning and uncertainty quantification for high-dimensional dynamical systems. I develop methods that connect numerical simulations with sparse observations, with applications to wildfire forecasting, air quality and computational physics.
Before joining ENPC in 2024, I was a Research Associate at the Data Science Institute, Imperial College London, working with Rossella Arcucci, from 2020 to 2024. I obtained my PhD from Université Paris-Saclay in 2020 under the supervision of Didier Lucor, working on error covariance specification in data assimilation in collaboration with EDF R&D.
Research interests
- Data assimilation and inverse problems: reconstructing states and estimating physical parameters from sparse observations.
- Reduced-order and latent-space modelling: learning efficient representations of high-dimensional physical systems for prediction and assimilation.
- Scientific machine learning: combining physical models with neural networks, differentiable simulation and neural operators.
- Generative modelling: probabilistic forecasting and field reconstruction using conditional generative models.
- Environmental applications: wildfire spread, air pollution and geophysical dynamical systems.
Selected award and research projects
- Hi!Paris AI Research Fellowship (2026–2030).
- OFML — ANR JCJC (2025–2029): machine learning for wildfire modelling and firefighting strategies.
- MILADY — ANR PRC (2025–2029): machine learning for computational fluid dynamics.
- QWERTY — PEPR FORESTT (2025–2030): wildfire and biodiversity.
Contact
Email: sibo.cheng@enpc.fr