Research topics
Information processing: Signals, Images, Graphs
The group has a strong history of research in signal and image processing and other processing methods for heterogenous data. Key-words of our main contributions are: nonstationary signals, multiscale analysis, inverse problems, signal/image processing and machine learning, deep learning, graph signal processing, and network science.
Physics of Information
Information is central in physics and data processing. Experiments on the nature of information in physics are built and conducted at the laboratory. Methods and models to extract information are studied, in relation also to signal processing techniques.
Machine Learning, Computational Physics, and Numerical Studies
Existing synergies between Machine Learning, computational physics and studies about numerical approaches are conducted in this theme. With that, we want to develop and promote AI for Science, as well as Science for AI, and integrate that in Computational physics and Data processing.
Information processing and Phyics for social studies, biology, and health
Specific applications are conducted alongside methodological works in physics and information processing, on selected domains: study of human activities (for social systems, in economics, or in geography), analysis in biology, especially in genomics, and biomedical signal or image processing.
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