Research topics
My research lies at the crossroads of machine learning, signal processing, applied mathematics and data mining. This theme covers a wide range of work on the analysis and processing of signals and time series, with applications in medicine, biology and industry.
Event detection in time series
- Change-point detection: parametric and nonparametric techniques, supervised approaches, applications...
- Anomaly detection: unsupervised approaches, topological data analysis...
- Pattern and motif discovery: similarity search, elastic distances, shape analysis...
Representation learning for time series
- Dictionary learning and sparse coding: convolutional sparse coding, tensor approaches, graph dictionary learning...
- Symbolization for time series: adaptive representations, distances between multivariate time series...
- Unsupervised representation learning with deep learning
Graph signal processing for multivariate time series
- Graph signal processing: graph learning, dictionary learning, filtering, sampling, denoising, interpolation, sensor networks...
- Network Granger causality estimation
Interdisciplinarity: applications to medicine, biology and industry
- Biomedical signal processing: accelerometry data, 2D and 3D trajectories (eye tracking, posture and motion capture), ECG, EEG, respiratory signals, video, sound...
- Application to biomedical research and neurosciences: gait analysis, posture analysis, motion analysis, oculomotricity, plethysmography, general anesthesia, cardiology, mental workload, behavior analysis...
- Industrial applications: predictive maintenance
Reproducibility and open science
- Open-source software and libraries
- Online web applications and reproducible publications
- Publication of medical databases
More info on the Software page.
