DataLearning group leader
Dr Rossella Arcucci is a Lecturer in Data Science and Machine Learning at Imperial College London where she leads the DataLearning Group and is the elected representative of the AI Network of Excellence.
Rossella has developed models which have been applied to many industries including 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) and climate change.
With an academic background in mathematics, Rossella completed her PhD in Computational and Computer Science in February 2012 and became a Marie Sklodowska-Curie fellow with the European Commission Research Executive Agency in Brussels in February 2017. In 2022, Rossella was elected as a member of the World Meterological Organization.
Postdoctoral Research Associate at the Data Science Institute and the Department of Earth Science and Engineering. César completed his PhD in data-driven oceanography and fast forecasting with the Space and Atmospheric Physics Group at Imperial College London. He is currently working on reduced-order models combined with generative networks to create synthetic data of small, realistic datasets and is also involved in projects working on wave energy converters, urban air pollution and microfluidics.
Postdoctoral researcher at the Data Science Institute. Sibo researches field reconstruction and parameter identification for dynamical systems. His work uses advanced machine learning, data assimilation and model reduction methods to tackle real-world applications in geoscience, fluid dynamics and medical science.
Research associate working in the Computational Cardiac Imaging group within the Faculty of Medicine at the Institute of Clinical Sciences. She designs smart software frameworks for real-time cardiac data analysis and interactive visualisations.
PhD student in the School of Earth Science and Engineering and affiliated with the Data Science Institute. Che's research is at the interface between deep learning and multimodal medical data processing, including biomedical signal processing, medical image segmentation, clinical text processing and image-text fusion.
PhD student in the Department of Earth Science and Engineering, with connections to the Data Science Institute and the Leverhulme Centre for Wildfires, Environment and Society. Jake researches real-time natural disaster forecasting and nowcasting using data science and machine learning methods. This includes integrating alternative data sources such as social media to create models which consider social and human aspects of disaster dynamics. The aim of the project is to develop models which are informative and actionable for disaster management and emergency services, facilitating a more socialised and integrated response to these events.
Mr Andrianirina Rakotoharisoa
PhD student in the Department of Earth Science and Engineering, affiliated with the Data Science Institute and the Leonardo Centre on Business for Society. Andrianirina's research applies machine learning and data science to the analysis of corporate environmental impact. His project analyses air pollution data from sensors and satellites to study the association between air pollution and nearby industries.
Mr Zhendan Shang
Data Scientist working at Royal Brompton Hospital and the Department of Earth Science and Engineering, and is funded by Pfizer. Zhendan uses data science to accurately identify and monitor fungal disease burden, antifungal use, and antifungal resistance at Royal Brompton to tackle the increasing prevalence of fungal disease, the difficult diagnosis due to low sensitivity of current tests and the global emergence of multiple triazole resistance over the past decade.
Mr Hongwei Fan
PhD student in the Centre for Environmental Policy, affiliated with the Data Science Institute. Hongwei works on fine-scale air pollution estimation based on data fusion and machine learning methods. Before his PhD, Hongwei completed an Masters degree in biomedical engineering at Tsinghua University and worked as an algorithm engineer at SenseTime.