Long Li

Background

After a bachelor degree (2012-2015) of Mathematics at University of Jean Monnet France, I obtained the Master degree (2015-2017) in Applied Mathematics (MSIAM) at University of Grenoble Alpes France. During my masters, I specialised in probability and statistics, for issues such as global sensitivity analysis for high-dimensional problems, Bayesian parametric and nonparametric method in statistical learning. From October 2017 to March 2021, I did a PhD within the Fluminance team of INRIA Rennes, on stochastic modelling of oceanic dynamics for ensemble forecasting, uncertainty quantification and data assimilation. I am currently on a Starting Research Position (SRP) at Inria within the ERC STUOD  

Research

My current research is focused on a stochastic parametrization of oceanic mesoscale eddies, particularly in the quasi-geostrophic regime. The aim is to better understand their physical effects on large-scale circulation, to identify their contributions on energy transfer and to characterize the low-frequency variability in realistic ocean models. In general, I also have strong interests in the applications of uncertainty quantification and ensemble data assimilation. Rencently, I participated in the French national LEFE MANU project – Mutiple Scale Ocean Model (MSOM) and in the International ERC program – Stochastic Transport in Upper Ocean Dynamics (STUOD).

Keywords: stochastic modelling, oceanic dynamics, uncertainty quantification, data assimilation

Publications

• Werner Bauer, Pranav Chandramouli, Long Li, Etienne Mémin – Stochastic representation of mesoscale eddy effects in coarse-resolution barotropic models – In press, Ocean Modelling, Elsevier, 2020.
• Valentin Resseguier, Long Li, Gabriel Jouan, Pierre Dérian, Etienne Mémin, Chapron Bertrand – New trends in ensemble forecast strategy: uncertainty quantification for coarse-grid computational fluid dynamics – Archives of Computational Methods in Engineering, 2020.
• Werner Bauer, Pranav Chandramouli, Bertrand Chapron, Long Li, Etienne Mémin – Deciphering the role of small-scale inhomogeneity on geophysical flow structuration: a stochastic approach – Journal of Physical Oceanography, 2020.
• Pierre Etoré, Clémentine Prieur, Dang Khoi Pham and Long Li – Global sensitivity analysis for models described by stochastic differential equations – Methodology and Computing in Applied Probability, Springer Verlag, 2019.