Sibo started working as a Research Associate in November 2020. His research combines data assimilation and machine learning algorithms for predicting dynamical systems with the application to wildfire forecasting, disease spreading, hydrology etc. He recently completed his PhD at Paris-Saclay University, in cooperation with EDF R&D.
He is also broadly interested in a large range of problematics in applied mathematics, computational geoscience and machine learning (graph theory, data compression for dynamical systems, covariance estimation).
et al., 2023, Analyzing drop coalescence in microfluidic devices with a deep learning generative model, Physical Chemistry Chemical Physics, Vol:25, ISSN:1463-9076, Pages:15744-15755
et al., 2023, Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys, Physical Chemistry Chemical Physics, Vol:25, ISSN:1463-9076, Pages:15970-15987
et al., 2022, Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device, Lab on a Chip: Miniaturisation for Chemistry, Physics, Biology, Materials Science and Bioengineering, Vol:22, ISSN:1473-0189, Pages:3187-3202
et al., 2022, Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling, Remote Sensing, Vol:14, ISSN:2072-4292
Cheng S, Lucor D, Argaud J-P, 2021, Observation data compression for variational assimilation of dynamical systems (R), Journal of Computational Science, Vol:53, ISSN:1877-7503, Pages:1-12