Imperial College London

DrRossellaArcucci

Faculty of EngineeringDepartment of Earth Science & Engineering

Lecturer
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Summary

Data Assimilation ± Machine Learning = Data Learning

Elected member of the WMO (World Meteorological Organization), Rossella contributes to the development of AI models for Climate and Environmental impact as part of the data assimilation and observing systems working group.

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.

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. 

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

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. 

Projects:

She is CO-I of several funded projects:

Events:

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

Posts:


Publications

Journals

Cheng S, Chen J, Anastasiou C, et al., 2023, Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models, Journal of Scientific Computing, Vol:94, ISSN:0885-7474

Gong H, Cheng S, Chen Z, et al., 2022, An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics, Annals of Nuclear Energy, Vol:179, ISSN:0306-4549

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

Chagot L, Quilodran-Casas C, Kalli M, et al., 2022, Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach, Lab on a Chip, Vol:22, ISSN:1473-0197, Pages:3848-3859

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

More Publications