Research Fellow at Data Science Institute.
Data Assimilation to build Adaptive Transmission Models for Covid19: https://arxiv.org/abs/2004.12130
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.
At Data Science Institute, she created the DataLearning Working Group. She leads and coordinates the activities of the group and she supervises MSc students and early career researchers.
She was PI of the H2020-RISE-2015-iNnovative Approaches for Scalable Data Assimilation in oCeanography project until September 2017.
She is involved in 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 is also involved in several other projects which include the MAGIC (Managing Air in Green Inner Cities) project.
She received the acknowledgement of Marie Skłodowska-Curie fellow from European Commission Research Executive Agency in Brussels on the 27th of November 2017.
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., 2020, Weak Constraint Gaussian Processes for optimal sensor placement, Journal of Computational Science, Vol:42, ISSN:1877-7503
et al., 2020, Data-driven reduced order model with temporal convolutional neural network, Computer Methods in Applied Mechanics and Engineering, Vol:360, ISSN:0045-7825
et al., 2020, A domain decomposition reduced order model with data assimilation (DD-RODA), Pages:189-198, ISSN:0927-5452
Nadler P, Arcucci R, Guo YK, 2020, A scalable approach to econometric inference, Pages:59-68, ISSN:0927-5452