Dr Alix Marie d’Avigneau

  • Digital Roads Industry Researcher Data Science, Costain
Dr Alix Marie d’Avigneau

Contact

Location

  • Civil Engineering

About

Research interests 

As part of the DRF project, recent interests include large-scale Natural Language Processing (NLP) of repair logs and spatial modelling for causal inference with the goal of predicting defect occurrence. More broadly, research interests are machine learning, Bayesian modelling and inference, and efficient parallel implementations in cloud computing. 

Strategic Themes 

  • Defect diagnosis automation 
  • Pavement condition modelling 
  • Data preparation 

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 team that built and published the large-scale CAMHighways dataset from survey data spanning 42km of UK highways. She is also part of the Data Science team for the project, which is developing RoadGP, a defect diagnosis tool built in consultation with pavement experts powered by knowledge from both official guidelines and historical repair data. For more information, see https://drf.eng.cam.ac.uk/research/data-science. 

Biography 

Alix MARIE d’AVIGNEAU is an Industry Fellow at Costain Group, working on the DRF 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. She has been working on the DRF project since 2022.  

Her focus on core statistical and machine learning tools and methodology has allowed Alix to explore a wide variety of applications, such as data preparation, computer vision, geospatial modelling, natural language processing, changepoint detection, predator-prey models, single-molecule fluorescence microscopy.