Imperial College London


Faculty of EngineeringDepartment of Earth Science & Engineering




r.arcucci Website




Electrical EngineeringSouth Kensington Campus





Lecturer in Data Science and Machine Learning at the Department of Earth Science and Engineering.

Rossella has been with the Data Science Institute at Imperial College since 2017, where she has created the Data Assimilation and Machine Learning (DataLearning) Working Group. The group is now a focal point for researchers and students of several departments at Imperial and other Universities in UK and Europe. She leads and coordinate the group and she supervises students, PhD students and Post-Doc Researchers.

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




Wu P, Chang X, Yuan W, et al., 2021, Fast data assimilation (FDA): Data assimilation by machine learning for faster optimize model state, Journal of Computational Science, Vol:51, ISSN:1877-7503

Tajnafoi G, Arcucci R, Mottet L, et al., 2021, Variational Gaussian process for optimal sensor placement, Applications of Mathematics, Vol:66, ISSN:0373-6725, Pages:287-317

Kumar P, Kalaiarasan G, Porter AE, et al., 2021, An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments., Science of the Total Environment, Vol:756, ISSN:0048-9697, Pages:1-22

Quilodrán-Casas C, Silva VS, Arcucci R, et al., 2021, Digital twins based on bidirectional LSTM and GAN for modelling COVID-19


Bonavita M, Arcucci R, Carrassi A, et al., 2021, Machine Learning for Earth System Observation and Prediction, AMER METEOROLOGICAL SOC, Pages:E710-E716, ISSN:0003-0007

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