Imperial College London


Faculty of EngineeringDepartment of Computing

Research Associate







Huxley BuildingSouth Kensington Campus





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).



Zhu K, Cheng S, Kovalchuk N, 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

Xia Z, Ma K, Cheng S, 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

Zhuang Y, Cheng S, Kovalchuk N, 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

Cheng S, Jin Y, Harrison S, 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

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