Dr Alix Marie d’Avigneau

Name: Dr Alix MARIE d’AVIGNEAU 

Academic Division: Civil Engineering

Research Group: Construction Engineering – Digital Roads  

Email: agem2@cam.ac.uk 

Phone: +44 7733 715372 

 

Research Interests

As part of the DRF project (see below), recent interests include Bayesian and machine learning methods for data integration and uncertainty quantification. More broadly, research interest are: Bayesian modelling and inference, Monte Carlo methods (including sequential Monte Carlo, Markov chain Monte Carlo), data analysis and computational statistics (including efficient parallel implementations). 

Strategic Themes

Synthetic data generation 

Predictive modelling of pavement condition 

Low-resolution pavement monitoring 

Research Project

Digital Roads of the Future (DRF) initiative to develop a connected physical and digital road infrastructure system that is sustainable. For more information, see https://drf.eng.cam.ac.uk/research. 

Alix is part of the Data Science work package for the project, which, among other things, focuses on developing Bayesian and machine learning methods for data integration of digital twins data, as well as assessing the quality of the data via uncertainty quantification. For more information, see https://drf.eng.cam.ac.uk/research/data-science. 

Biography

French-born Alix MARIE d’AVIGNEAU is an Industrial Research Fellow at Costain Group, working on the Digital Roads of the Future initiative and specialising in Data Science branch of the project. After receiving a Master’s degree in Mathematics from the University of St Andrews in 2016, she completed her PhD in Information Engineering (2016-2020) at the University of Cambridge, as part of the Signal Processing and Communications laboratory. Between 2020 and 2022 she worked as a post-doctoral researcher in single-molecule microscopy for Ward Ober Lab, part of the Centre for Cancer Immunology at the University of Southampton. 

  

Her research interests include Bayesian modelling and inference, Monte Carlo methods, data analysis and computational statistics. Her research in generic and efficient statistical tools and methodology has allowed Alix to explore a wide variety of applications, such as changepoint detection, predator-prey models, single-molecule fluorescence microscopy, and most recently, construction and maintenance of roads and infrastructure.