Research Fellow 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.
She was PI of the H2020-RISE-2015-iNnovative Approaches for Scalable Data Assimilation in oCeanography project until September 2017.
She is Co-investigator of the EPSRC - INHALE (Health assessment across biological length scales for personal pollution exposure and its mitigation) project and she is Co-investigator of the pump priming founding project “Drone/UAV based imaging combined with deep learning and data assimilation to support the ongoing development of the tidal energy sector” at ICL.
She received the acknowledgement of Marie Skłodowska-Curie fellow from European Commission Research Executive Agency in Brussels on the 27th of November 2017.
She leads and coordinates the activities of the DataLearning Working Group and supervises MSc students and early career researchers.
She is organising the Second Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS 2020) as part of the A-rank International Conference on Computational Science (ICCS) 2020 in Amsterdam 3-5 June 2020.
She also organised the First Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS 2019).
Posts: Imagine it ... then, do it!!!
et al., 2019, Enhancing CFD-LES air pollution prediction accuracy using data assimilation, Building and Environment, Vol:165, ISSN:0360-1323
et al., 2019, Model error correction in data assimilation by integrating neural networks, Big Data Mining and Analytics, Vol:2, ISSN:2096-0654, Pages:83-91
et al., 2019, Optimal reduced space for Variational Data Assimilation, Journal of Computational Physics, Vol:379, ISSN:0021-9991, Pages:51-69
Arcucci R, Pain C, Guo Y-K, 2018, Effective variational data assimilation in air-pollution prediction, Big Data Mining and Analytics, Vol:1, ISSN:2096-0654, Pages:297-307
Arcucci R, McIlwraith D, Guo YK, 2019, Scalable Weak Constraint Gaussian Processes, Pages:111-125, ISSN:0302-9743