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 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.
Workshops (open call for papers):
Posts: Imagine it ... then, do it!!!
et al., 2019, Optimal reduced space for Variational Data Assimilation, Journal of Computational Physics, Vol:379, ISSN:0021-9991, Pages:51-69
, 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
et al., 2018, Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems, 12th International Conference on Parallel Processing and Applied Mathematics (PPAM), SPRINGER INTERNATIONAL PUBLISHING AG, Pages:37-47, ISSN:0302-9743
et al., 2018, Performance Assessment of the Incremental Strong Constraints 4DVAR Algorithm in ROMS, 12th International Conference on Parallel Processing and Applied Mathematics (PPAM), SPRINGER INTERNATIONAL PUBLISHING AG, Pages:48-57, ISSN:0302-9743