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Dimitri Van De Ville received his M.S. and Ph.D. degrees in Computer Science from Ghent University, Belgium in 1998 and 2002, respectively. From 2002 to 2005, he was a post-doctoral fellow at the Biomedical Imaging Group of Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. In 2005, he became responsible for the Signal Processing Unit at the University of Geneva (UniGE) and Geneva University Hospital (HUG) as part of the Centre d’Imagerie BioMédicale (CIBM), a large imaging initiative of the Lemanic academic institutions. In 2009, he was awarded an SNSF professorship and he started a joint tenure-track professorship at the UniGE (Department of Radiology and Medical Informatics, Faculty of Medicine) and the EPFL (Institute of Bioengineering, School of Engineering). He has published more than 100 journal papers on signal and image processing, including on wavelet theory and network science, and their application to the biomedical field, in particular functional brain imaging.
He has published more than 200 journal papers on signal and image processing, including on wavelet theory and network science, and their application to the biomedical field, in particular functional brain imaging. Recent work on dynamic functional connectivity included evidence that resting-state functional networks can be disentangled in terms of their temporal overlap, which showed a more complete picture of dynamic organization of brain function and opens avenues for more sensitive biomarkers; e.g., in early diagnosis of patients with mild cognitive impairment. Other areas of interest include the application of graph signal processing to neuroimaging data, or the use of real-time fMRI for neurofeedback applications.
He has published more than 200 journal papers on signal and image processing, including on wavelet theory and network science, and their application to the biomedical field, in particular functional brain imaging. Recent work on dynamic functional connectivity included evidence that resting-state functional networks can be disentangled in terms of their temporal overlap, which showed a more complete picture of dynamic organization of brain function and opens avenues for more sensitive biomarkers; e.g., in early diagnosis of patients with mild cognitive impairment. Other areas of interest include the application of graph signal processing to neuroimaging data, or the use of real-time fMRI for neurofeedback applications.
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crossref(2024)
Aïda B. Fall,Maria Giulia Preti, Mohamed Eshmawey,Sonja M. Kagerer,Dimitri Van De Ville,Paul G. Unschuld, Alzheimer’s Disease Neuroimaging Initiative (ADNI)
crossref(2024)
Sylvain Harquel,Andéol Cadic-Melchior,Takuya Morishita,Lisa Fleury,Adrien Witon,Martino Ceroni, Julia Brügger,Nathalie H Meyer, Giorgia G Evangelista,Philip Egger, Elena Beanato,Pauline Menoud,
Stroke (2024)
BIOMEDICAL SIGNAL PROCESSING AND CONTROLno. Part B (2024): 105537-105537
Scientific Reportsno. 1 (2024): 1-16
Science Talkspp.100330, (2024)
Human brain mappingno. 5 (2024): e26649-e26649
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