Dr Rossella Arcucci will give the ESE Departmental Seminar on 20 January, “Data Learning: Integrating Data Assimilation and Machine Learning”.
Join us online by clicking “Livestream” on the seminar page at 12pm. The livestream link will be added in due course.
Data Learning: Integrating Data Assimilation and Machine Learning
Data Assimilation (DA) is the approximation of the true state of some physical system by combining observations with a dynamic model. DA incorporates observational data into a prediction model to improve forecasted results. These models have increased in sophistication to better fit application requirements and circumvent implementation issues. Nevertheless, these approaches are incapable of fully overcoming their unrealistic assumptions.
Machine Learning (ML) shows great capability in approximating nonlinear systems and extracting meaningful features from high–dimensional data. ML algorithms are capable of assisting or replacing traditional forecasting methods. However, the data used during training in any ML algorithm include numerical, approximation and round off errors, which are trained into the forecasting model. Integration of ML with DA increases the reliability of prediction by including information with a physical meaning. This talk introduces Data Learning, a field that integrates Data Assimilation and Machine Learning to overcome limitations in applying these fields to real-world data. The fundamental equations of DA and ML are presented and developed to show how they can be combined into Data Learning. We present a number of Data Learning methods and results for some test cases, though the equations are general and can easily be applied elsewhere.
Dr Rossella Arcucci is a Lecturer in Data Science and Machine Learning at the Department of Earth Science and Engineering, Imperial College London.
Rossella has been with the Data Science Institute at Imperial College since 2017, where she has created the Data Assimilation and Machine Learning (Data Learning) Working Group. The group 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 supervise students, PhD students and Post-Doc Researchers.
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 on 27 November 2017.
She is a member of the Artificial Intelligence (AI) network at Imperial College. She is the principal organiser of the annual workshop on Machine Learning and Data Assimilation for Dynamical Systems hosted every year by the International Conference on Computational Science. She has been co-investigator of several renowned projects: INHALE, RELIANT for COVID-19, WavE-Suite and PREMIERE grant. She is part of the Leverhulme Centre for Wildfires, Environment and Society.
A full list underlining the development of some data science and machine learning models for specific applications is available on this webpage.