Imperial College London


Faculty of EngineeringDepartment of Computing

Research Associate



r.arcucci Website




Electrical EngineeringSouth Kensington Campus





Research Associate at Data Science Institute. She works on numerical and parallel techniques for accurate and efficient Data Assimilation by exploiting the power of machine learning models. Efficiency is achieved by virtue of designing models specifically to take full advantage of massively parallel computers and general purpose graphics processing units.

PhD in Computational and Computer Science on February 2012. The subject of her thesis was Data Assimilation (DA).

Her expertise covers the main models for DA which are the Kalman Filtering models and the Variational models.

During her previous job position, she coordinated the H2020-RISE-2015-NASDAC project as PI until September 2017, when she joined the DSI.

She received the acknowledgement of Marie Skłodowska-Curie fellow from European Commission Research Executive Agency in Brussels on the 27th of November 2017.

Workshops (open call for papers):

MLDADS 2019 -

Posts: Imagine it ... then, do it!!!



Arcucci R, Pain C, Guo Y-K, 2018, Effective variational data assimilation in air-pollution prediction, Big Data Mining and Analytics, Vol:1, Pages:297-307

Arcucci R, Carracciuolo L, Toumi R, 2018, Toward a preconditioned scalable 3DVAR for assimilating Sea Surface Temperature collected into the Caspian Sea, Journal of Numerical Analysis, Industrial and Applied Mathematics, Vol:12, ISSN:1790-8140, Pages:9-28

Song J, Fan S, Lin W, et al., 2018, Natural ventilation in cities: the implications of fluid mechanics, Building Research and Information, Vol:46, ISSN:0961-3218, Pages:809-828


D’Amore L, Mantovani F, 2018, Energy analysis of a 4D variational data assimilation algorithm and evaluation on ARM-based HPC systems, Pages:37-47, ISSN:0302-9743

Phillipson L, Toumi R, 2018, Performance assessment of the incremental strong constraints 4DVAR algorithm in ROMS, Pages:48-57, ISSN:0302-9743

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