Dimitri Van De Ville

Dimitri Van De Ville (born 1975 in Dendermonde) is a Switzerland and Belgians computer scientist and neuroscientist specialized in dynamical and network aspects of brain activity. He is a professor of neuroimaging and Computer science at École Polytechnique Fédérale de Lausanne and the head of the Medical Image Processing Laboratory at École Polytechnique Fédérale de Lausanne.
Van De Ville studied Computer science at Ghent University and received his Master's degree suma cum lauda in 1998. He then pursued a PhD at the same institution and graduated in 2002 with a thesis on "Linear, nonlinear, and fuzzy image interpolation techniques" (Lineaire, niet-lineaire en vaaglogische beeldinterpolatietechnieken) that was supervised by Ignace Lemahieu and Wilfried Philips. He joined the EPFL as a post-doctoral researcher in Michael Unser's Biomedical Imaging Group. In 2005, he became group leader of the Signal Processing Core at University of Geneva's Center for Biomedical Imaging (CIBM).
In 2009, enabled by a Swiss National Science Foundation Professorship Grant, he founded the Medical Image Processing Laboratory that is jointly held by École Polytechnique Fédérale de Lausanne Institute of Bioengineering and the University of Geneva's Faculty of Medicine, and that is situated at the Campus Biotech in Geneva. In 2015, he was appointed tenured associate professor at École Polytechnique Fédérale de Lausanne with a joint adjunct appointment at the University of Geneva. Since 2015 he has been the head of the CIBM's Signal Processing Section, and since 2020 he has been the ad-interim head of CIBM's Animal Imaging & Technology Section.
Van De Ville's research focuses on functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to measure dynamical and network aspects of human brain activity. He develops analysis methods for fMRI such as graph signal processing, and applies them to investigate brain functions. 
Van De Ville’s research interests through to the end of his post-doctoral studies were dedicated to wavelets and splines, specifically to hex-splines, isotropic polyharmonic B-spline wavelets, and operator wavelets. This research also found application in neuro-imaging by inspiring adaptation such as activelets and wavelet-based statistical parametric mapping.
Since 2009, Van De Ville has dedicated his research to computational neuroimaging with the aim to study brain functions related to behavior in health and disorder by employing fMRI and EEG data. He provides an explanation why fast EEG neural correlates (milliseconds timescale) can be correlated with slow fMRI hemodynamic fluctuations (seconds timescale) by demonstrating that sequences of EEG micro-state topographies represent scale-free organization. He also introduced machine learning methods to functional connectivity measures, and thereby initiated the field of connectivity decoding. 
In describing both the method and the application for imaging-based biomarkers, he was among the first to describe the dynamic functional connectome. He also helped to introduce brain states based on sliding-window functional connectivity. To allow for reliable identification of spatial patterns of transient brain activity, he introduced a novel method for sparsity-pursuing regularized hemodynamic deconvolution of fMRI time series. This method also enabled an important progress to the classical Wiener deconvolution, and brought insights in brain activity dynamics during sleep and alterations owing to neurological conditions. 
Van De Ville research is also dedicated to the emerging field of graph signal processing to resolve brain structure-function relationships, where the structural connectome is employed as a graph. This framework allows to quantify the amount of activity 'coupling' with the underlying structure, and thereby helps to elucidate hierarchical organization of the brain that is relevant for behavior. Recently his research helped to extend the regular atlas-based graphs including a few hundred nodes to fine-grained voxel-wise graphs representing around a million nodes. His theoretical work on graph signal processing encompasses the proposal of multi-slice graph wavelets, graph Slepians, and community-based graph filtering. Moreover, he is also actively developing real-time neuro-feedback application for fMRI test settings that allow for the self regulation of subjects in the scanner by informing them about their brain activity. 
Van De Ville is the recipient of the 2016 Leenaards Prize, the 2014 NARSAD Independent Investigator Award, the 2013 NeuroImage Editors’ Choice Award, and the 2012 Pfizer Prize.
He is a distinguished lecturer of the 2021-2022 Institute of Electrical and Electronics Engineers Signal Processing Society (SPS), an a fellow of the Institute of Electrical and Electronics Engineers(2020).
Category:1975 births
Category:Ghent University alumni
Category:École Polytechnique Fédérale de Lausanne faculty
Category:Computer scientists
Category:Belgian scientists