Imperial College London


Faculty of EngineeringDepartment of Earth Science & Engineering




r.arcucci Website




Electrical EngineeringSouth Kensington Campus





Data Assimilation ± Machine Learning = Data Learning

Rossella is a lecturer in Data Science and Machine Learning at Imperial College London where she leads the  Data Assimilation and Machine Learning (Data Learning) Group.

Since February 2022, she is the elected representative of the Artificial Intelligence Network of Excellence at Imperial College, where she represents ~250 academics working on AI.

Rossella has been with the Data Science Institute at Imperial College since 2017, where she has created the Data Learning Group which is now a focal point for researchers and students of several departments at Imperial and other Universities in UK and Europe. 

She collaborates with the Leonardo Centre at Imperial College Business School, where she contributes to the development of integrative, just and sustainable models of economic and social development by discovering, testing and diffusing new logics of business enterprise.

The models Rossella has developed have produced impact in many applications such as finance (to estimate optimal parameters of economic models), social science (to merge twitter and pooling data to better estimate the sentiment of people), engineering (to optimise the placement of sensors and reduce the costs), geoscience (to improve accuracy of forecasting), climate changes and others. She has developed accurate and efficient models with data analysis, fusion and data assimilation for incomplete, noisy or Big Data problems, always including uncertainty quantifications and minimizations. 

She works on numerical and parallel techniques for accurate and efficient Data Assimilation and Machine Learning models. Efficiency is achieved by virtue of designing models specifically to take full advantage of massively parallel computers.

She finished her PhD in Computational and Computer Science in February 2012. She received the acknowledgement of Marie Sklodowska-Curie fellow from European Commission Research Executive Agency in Brussels in February 2017. 


She is CO-I of several funded projects:


She organises the annual workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS), a thematic track of the A-ranked International Conference on Computational Science (ICCS). Enjoy the videos of the previous editions of MLDADS




Cheng S, Prentice IC, Huang Y, et al., 2022, Data-driven surrogate model with latent data-assimilation: application to wildfire forecasting, Journal of Computational Physics, Vol:464, ISSN:0021-9991

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

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, ISSN:1473-0189

Schneider R, Bonavita M, Geer A, et al., 2022, ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction, Npj Climate and Atmospheric Science, Vol:5, ISSN:2397-3722


Lever J, Arcucci R, Cai J, 2022, Social Data Assimilation of Human Sensor Networks for Wildfires, Pages:455-462

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