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FNRS Senior Research Associate and Professor

UCLouvain, Belgium

Biography

I am a Professor and FNRS Senior Research Associate at the Image and Signal Processing Group (ISPGroup), in the Mathematical engineering (INMA) department of the ICTEAM institute at UCLouvain, Belgium. My main research activities focus on compressive sensing (theory and applications), inverse problems solving in optics, astronomy and biomedical sciences, compressive learning and data sciences, sparse signal representations and other low-complexity signal priors.

Interests

  • Inverse Problems in Optics, Astronomy, and Biomedical Sciences

  • Compressive Sensing and Computational Imaging

  • Compressive Learning and Data Sciences

  • Image and Signal Processing

Education

  • PhD in Mathematical Physics, 2004

    UCLouvain, Belgium

  • MSc in Mathematical Physics, 1998

    UCLouvain, Belgium

  • BSc in Physics, 1996

    UCLouvain, Belgium

Appointments

  • IEEE Transactions on Signal Processing - Associate Editor (2022-now)

  • EURASIP Journal on Advances in Signal Processing - Associate Editor (2020-2022)

  • EURASIP Signal and Data Analytics for Machine Learning - Technical Area Committee member

Events

Recent & upcoming talks, seminars, …

Asymmetric compressive learning guarantees with applications to quantized sketches
Interferometric Lensless Endoscopy: Rank-one Projections of Image Frequencies with Speckle Illuminations
Interferometric Lensless Endoscopy: Rank-one Projections of Image Frequencies with Speckle Illuminations
The importance of phase in complex compressive sensing

Posts

Last 3 posts …

ROP inception

Here is a new short preprint: “ROP inception: signal estimation with quadratic random sketching”, available here and on arXiv. This is the first work of Rémi Delogne, carried out in collaboration with Vincent Schellekens and me.

There is time for dithering in a quantized world of reduced dimensionality!

I’m glad to announce here a new work made in collaboration with Valerio Cambareri (UCL, Belgium) on quantized embeddings of low-complexity vectors, such as the set of sparse (or compressible) signals in a certain basis/dictionary, the set of low-rank matrices or vectors living in (a union of) subspaces.

Quasi-isometric embeddings of vector sets with quantized sub-Gaussian projections

\(\newcommand{\cl}{\mathcal}\newcommand{\mathbb}{\mathbb}%\) Last January, I was honored to be invited in RWTH Aachen University by Holger Rauhut and Sjoerd Dirksen to give a talk on the general topic of quantized compressed sensing.

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