André Ferrari

André Ferrari

Professor

Université Côte d'Azur

Biography

I am currently a professor at the Laboratoire J.-L. Lagrange, a joint laboratory between Université Côte d’Azur, CNRS and Observatoire de la Côte d’Azur.

My current research activity is centered around statistical data processing, inverse problems and machine learning. I have a particular interest in applications to Astrophysics:

targets
Created using wordcloud.jl with conference papers

Publications

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(2024). Leveraging distributed acoustic sensing for vehicle counting in high-traffic-density environments. Signals, Systems and Computers (ASILOMAR).

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(2024). A comparison of solar and stellar coronagraphs that make use of external occulters. SPIE Space Telescopes and Instrumentation.

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(2024). Computation of the Fresnel diffraction of Starshades based on a polygonal approximation. SPIE Space Telescopes and Instrumentation.

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(2024). A new approach for the numerical calculation of diffraction patterns using starshades. Astronomy and Astrophysics.

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(2024). From linear to nonlinear unfolded Condat-Vũ algorithm for spectro-polarimetric hight-constrast image recovery. European Signal Processing Conference (EUSIPCO).

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Projects

SequoIA Analytics

Converting optical fibers into smart sensors using Deep Learning

Origins

Image reconstruction in high-contrast polarimetric imaging using data driven models in AI data analysis WP.

TOSCA
Weak lensing statistics for cosmology; optimising the synergy between Euclid and SKA.
DARK ERA
Dataflow algorithm architecture co-design of SKA pipeline for exascale radio Astronomy.
MAGELLAN
Machine learning methods for the very large arrays in radio astronomy.
MAHI
Methodological Aspects of Hyperspectral Imaging Workshop
MASTODONS
Distributed processing for very large arrays in radioastronomy
POLCA
Processing of polychromatic interferometric data for Astrophysics
SSP 2011
Statistical Signal Processing Workshop
JITHDE
High dynamics and planet detection workshops
MATIS
the historical 2001 matis webpage

Courses

Digital signal processing

Learn how to analyze signals using filters and Fourier transforms.

Inverse problems and imaging

Learn how to numerically solve an inverse problem to reconstruct an image.

Digital Image and Signal Processing

Learn how to analyze signals and images using numerical tools.

Numerical optimization with applications

Learn how to numerically solve an optimization problem.

Bayesian analysis. Application to image reconstruction

Learn how to use Bayesian formalism to model and solve a problem.

Contact

  • Université Côte d'Azur, Parc Valrose, Nice, F 06108
  • Building Fizeau on Floor 4