Research Fellow at Data Science Institute, Rossella has been with the Data Science Institute since 2017.
She has joined the Leonardo Centre at Imperial College Business School as an Advanced Research Fellow/Data Scientist, for 50% of her time from March 2021.
At the DSI, she has created the Data Assimilation and Machine Learning (DataLearning) Working Group. which is now a focal point for researchers and students of several departments at Imperial and other Universities in UK and Europe. She leads and coordinate the group and she supervises students, PhD students and PDRAs.
The models Rossella has developed have produced impact in many applications such as finance (to estimate optimal parameters of economic models), social science (to merge twitter and pooling data to better estimate the sentiment of people), engineering (to optimise the placement of sensors and reduce the costs), geoscience (to improve accuracy of forecasting), climate changes and others. She has developed accurate and efficient models with data analysis, fusion and data assimilation for incomplete, noisy or Big Data problems, always including uncertainty quantifications and minimizations.
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.
At the Leonardo Centre, Rossella will contribute to the development of integrative, just and sustainable models of economic and social development by discovering, testing and diffusing new logics of business enterprise.
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 is CO-I of several projects at DSI-ICL:
- the EPSRC - INHALE (Health assessment across biological length scales for personal pollution exposure and its mitigation) project.
- the UKRI - Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19 project.
- the EPSRC - PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE) project
- Research Supervisor at the Leverhulme Centre for Wildfires, Environment and Society.
- also involved in several other projects which include the MAGIC (Managing Air in Green Inner Cities) project.
- She was PI of the H2020-RISE-2015-iNnovative Approaches for Scalable Data Assimilation in oCeanography project until September 2017.
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. MLDADS is a Thematic Track at ICCS 2021
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., 2021, Variational Gaussian process for optimal sensor placement, Applications of Mathematics, Vol:66, Pages:287-317
et al., 2021, Fast data assimilation (FDA): Data assimilation by machine learning for faster optimize model state, Journal of Computational Science, Vol:51, ISSN:1877-7503
et al., 2021, An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments., Science of the Total Environment, Vol:756, ISSN:0048-9697, Pages:1-22
et al., 2021, Deep Data Assimilation: Integrating Deep Learning with Data Assimilation, Applied Sciences-basel, Vol:11