Research Fellow at Data Science Institute.
At DSI, she created the Data Assimilation and Machine Learning (DataLearning) Working Group. She leads and coordinates the activities of the group where she supervises students and early career researchers.
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.
She was PI of the H2020-RISE-2015-iNnovative Approaches for Scalable Data Assimilation in oCeanography project until September 2017.
She is CO-I of the EPSRC - INHALE (Health assessment across biological length scales for personal pollution exposure and its mitigation) project and she is Co-I 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 CO-I of the EPSRC - RELIAN for COVID19 project just started in October 2020 at ICL.
She is also involved in several other projects which include the MAGIC (Managing Air in Green Inner Cities) project.
She is organising the Third Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS 2021) as part of the A-ranked International Conference on Computational Science (ICCS) 2021 in Kraków, Poland, 16-18 June 2021.
Call for papers still open, you are welcome to join us submitting either 1 page abstract or a (short 7 pages - long 14 pages) paper:
She also organised the First Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS 2019) and the Second Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS 2020).
Please enjoy the video of MLDADS 2020:
- Click HERE to watch the video on youtube
- Data Assimilating real images in Sneezing And Coughing Simulations for realistic predictions of Covid19 spread: https://sites.google.com/view/rossella-arcucci/covid19
- Imagination is the key: Imagine it ... then, do it!!!
et al., 2020, A time-series clustering methodology for knowledge extraction in energy consumption data, Expert Systems With Applications, Vol:160, ISSN:0957-4174
et al., 2020, Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation, Computer Methods in Applied Mechanics and Engineering, Vol:372, ISSN:0045-7825
et al., 2020, An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments., Science of the Total Environment, ISSN:0048-9697
et al., 2020, A Reduced Order Deep Data Assimilation model, Physica D-nonlinear Phenomena, Vol:412, ISSN:0167-2789
et al., 2020, A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19, Ieee Computational Intelligence Magazine, Vol:15, ISSN:1556-603X, Pages:23-33