DrRossella Arcucci
Associate Professor in Data Learning and AI for Good
Department of Earth Science & Engineering - Faculty of Engineering
Orcid identifier0000-0002-9471-0585 (opens in a new tab)
- Associate Professor in Data Learning and AI for GoodDepartment of Earth Science & Engineering - Faculty of Engineering
- Royal School of Mines, South Kensington Campus, United Kingdom
BIO
Elected member of the WMO (World Meteorological Organization), Rossella contributes to the development of AI models for Climate and Environmental impact as part of the data assimilation and observing systems working group.
Director of the Ada Lovelace Academy.
Director of Research at the Data Science Institute at Imperial.
Head of the AI4Good & Data Learning group.
She collaborates with the Leonardo Centre at Imperial College Business School, where she contributes to the development of integrative, just and sustainable models of economic and social development by discovering, testing and diffusing new logics of business enterprise.
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.
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 funded projects:
- Leverhulme Centre for Wildfires, Environment and Society.
- the EPSRC - INHALE (Health assessment across biological length scales for personal pollution exposure and its mitigation) project.
- the EPSRC Marine Wave Energy Programme - New Generation Modelling Suite for the Survivability of Wave Energy Convertors in Marine Environments (WavE-Suite)
- the UKRI - Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19 project.
- the EPSRC - PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE) project.
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 organises an annual workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS), a thematic track of the A-ranked International Conference on Computational Science (ICCS).
Director of the Ada Lovelace Academy.
Director of Research at the Data Science Institute at Imperial.
Head of the AI4Good & Data Learning group.
She collaborates with the Leonardo Centre at Imperial College Business School, where she contributes to the development of integrative, just and sustainable models of economic and social development by discovering, testing and diffusing new logics of business enterprise.
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.
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 funded projects:
- Leverhulme Centre for Wildfires, Environment and Society.
- the EPSRC - INHALE (Health assessment across biological length scales for personal pollution exposure and its mitigation) project.
- the EPSRC Marine Wave Energy Programme - New Generation Modelling Suite for the Survivability of Wave Energy Convertors in Marine Environments (WavE-Suite)
- the UKRI - Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19 project.
- the EPSRC - PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE) project.
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 organises an annual workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS), a thematic track of the A-ranked International Conference on Computational Science (ICCS).
FACULTY
- Faculty of Engineering
POSITION NAME
- Associate Professor in Data Learning and AI for Go