Amélie completed an MSc in applied mathematics and statistics from the university of Lyon 1, France. During her final internship she worked on the automatic classification of tread wear pattern using random forest. She is particularly interested in statistical genetics and mathematical biology. Outside of mathematics, she likes playing basket- ball and watching weird films.

Research project title:
Detection of underwater acoustic events in a large dataset with machine learning

Supervisor(s):
Philippe Blondel, Kari Heine

Project description:
Acoustic remote sensing listens to ambient noise underwater and uses it to recognise the sources of the sounds (e.g. marine life, human activities, weather). Passive sensors acquire data at very high rates (up to a million samples/second) for long periods (up to several years). In this project, Amélie is working on automating the processing and exploration of the large dataset using machine learning techniques and high-performance computing system. The project aims to detect long-term trends, like the increase in shipping or seasonal variations in marine life, and transient events, loud sounds associated to seismic prospection, vocalisations by animals (e.g. whales or dolphins), or small-scale weather observations. The key research questions are in the processing and analysing the vast amounts of continuous data and in deciding the best time scale to look at specific processes.