Pierre Vandergheynst
EPFL STI IEL LTS2
ELE 235 (Bâtiment ELE)
Station 11
CH-1015 Lausanne
+41 21 693 56 45
+41 21 693 26 01
Office:
ELE 235
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LTS2
Web site: Web site: https://lts2.epfl.ch/
Fields of expertise
data science
machine learning
computational harmonic analysis
inverse problems
compressive sensing
computer vision
machine learning
computational harmonic analysis
inverse problems
compressive sensing
computer vision
Mission
Data nowadays come in overwhelming volume. In order to cope with this deluge, we explore and use the benefits of geometry and symmetry in higher dimensional data. But volume is not the only problem: data models are also increasingly complex, mixing various components. We thus use redundant dictionaries as a dimensionality reduction tool to dig out information from complicated high-dimensional datasets and multichannel signals, or to model complex behaviours in more classical signals. Finally data can also be complex because they are collected on surfaces, or more generally manifolds, or because they are not scalar-valued. We thus explore extensions of Computational Harmonic Analysis in higher dimensions, in complex geometries or for non-scalar data.Visit our lab web pages.
Biography
Pierre Vandergheynst received the M.S. degree in physics and the Ph.D. degree in mathematicalphysics from the Université catholique de Louvain, Louvain-la-Neuve, Belgium, in 1995 and 1998, respectively. From 1998 to 2001, he was a Postdoctoral Researcher with the Signal Processing Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. He was Assistant Professor at EPFL (2002-2007), where he is now a Full Professor of Electrical Engineering and, by courtesy, of Computer and Communication Sciences. As of 2015, Prof. Vandergheynst serves as EPFL’s Vice-Provost for Education.
His research focuses on harmonic analysis, sparse approximations and mathematical data processing in general with applications covering signal, image and high dimensional data processing, computer vision, machine learning, data science and graph-based data processing.
He was co-Editor-in-Chief of Signal Processing (2002-2006), Associate Editor of the IEEE Transactions on Signal Processing (2007-2011), the flagship journal of the signal processing community and currently serves as Associate Editor of Computer Vision and Image Understanding and SIAM Imaging Sciences. He has been on the Technical Committee of various conferences, serves on the steering committee of the SPARS workshop and was co-General Chairman of the EUSIPCO 2008 conference.
Pierre Vandergheynst is the author or co-author of more than 70 journal papers, one monograph and several book chapters. He has received two IEEE best paper awards. Professor Vandergheynst is a laureate of the Apple 2007 ARTS award and of the 2009-2010 De Boelpaepe prize of the Royal Academy of Sciences of Belgium.
Publications
Infoscience publications
Research
Topics
Data nowadays come in overwhelming volume. In order to cope with this deluge, we explore and use the benefits of geometry and symmetry in higher dimensional data. But volume is not the only problem: data models are also increasingly complex, mixing various components. We thus use sparse representations and dictionaries as dimensionality reduction tools to dig out information from complicated high-dimensional datasets and multichannel signals, or to model complex behaviours in more classical signals. Finally data can also be complex because they are collected on surfaces, or more generally manifolds, or because they are not scalar-valued. We thus explore extensions of Computational Harmonic Analysis in higher dimensions, in complex geometries, on graphs, networks or for non-scalar data.Teaching & PhD
Teaching
Electrical and Electronics Engineering
Microengineering
PhD Students
Atzeni Mattia, Boulougouri Maria, Guo Pengkang, Haget Anaïs Betsabeh, Hariri Ali, Karalias Nikolaos, Matoba Kyle Michael, Mc Cann Anna Mary, Nallapareddy Mohan Vamsi,Past EPFL PhD Students
Alahi Alexandre Massoud , Bagnato Luigi , Bayram Eda , Benzi Kirell Maël , Cerqueira Gonzalez Pena Rodrigo , Courtois Gilles André , D'Angelo Emmanuel , Defferrard Michaël , Dong Xiaowen , Golbabaee Mohammad , Grimaldi Vincent Pierre Olivier , Guicquéro William , Hosseini Kamal Mahdad , Jost Philippe , Kalofolias Vasilis , Llagostera Casanovas Anna , Mamaghanian Hossein , Martin Lionel Jérémie , Miz Volodymyr , Paratte Johann , Peic Tukuljac Helena , Perraudin Nathanaël , Pham Vu Thach , Pitas Konstantinos , Puy Gilles , Rivet Etienne Thierry Jean-Luc , Schnass Karin , Sergeev Stanislav , Shahid Nauman ,Courses
Matrix analysis
These lectures are intended as an applied linear algebra course, with a particular focus on providing intuition on the most standard tools. A particular emphasis is put on practice and digital notebooks help getting familiar with the most important concepts.
Signals and systems II (for MT)
This course is an introduction to the theory of discrete linear time invariant systems. Their properties and fundamental characteristics are discussed as well as the fundamental tools that are used to study and design them (Fourier transform, Z transform).
Signals and systems II (for SV)
This course is an introduction to the theory of discrete linear time invariant systems. Their properties and fundamental characteristics are discussed as well as the fundamental tools that are used to study and design them (Fourier transform, Z transform).