Tobías I. Liaudat

DEDIP dept., IRFU, CEA, Saclay, France.

I am a staff research scientist at the Institute of research into the fundamental laws of the Universe (IRFU) in the (CEA) Saclay near Paris.

My research lies at the crossroads of signal processing, statistics, machine learning and physics. I am particularly interested in leveraging recent artificial intelligence techniques and developing new methodologies that can be applied to inverse problems in a wide range of scientific applications like astronomy, cosmology and radio interferometry. In other words, putting AI at the service of science.

I am a member of the Euclid consortium, and the Laser Interferometer Space Antenna (LISA) consortium, two missions from the European Space Agengy (ESA). In addition, I am also member of the James Webb Space Telescope (JWST) COSMOS-Web survey, and the UNIONS survey science collaboration.

I was a postdoctoral research fellow at the Mullard Space Science Laboratory (MSSL) and the Computer Science (CS) department at the University College London (UCL), where I worked with Prof. Jason McEwen, Prof. Marcelo Pereyra and Prof. Marta Betcke. Before that, I completed my PhD at the CosmoStat Laboratory at the CEA Saclay near Paris under the supervision of Dr. Jean-Luc Starck and Dr. Martin Kilbinger.

News

Jun 6, 2024 :loudspeaker: Job alert :loudspeaker: One open postdoctoral researcher position to work with me on machine learning for scientific imaging in astrophysics. Applications are considered on a rolling basis, so don’t be late to apply! :hourglass:The position has been filled.
Dec 1, 2023 I am thrilled to annouce that I started my new position at the CEA Saclay research centre in the Paris region as a staff research scientist! :tada: I am still looking for two motivated interns to work on exciting machine learning projects with an astro application.
Dec 1, 2023 :bell: New paper out of the oven at arXiv:2312.00125 with code available at github.com/astro-informatics/QuantifAI! The proposed method coined QuantifAI allows us to reconstruct radio interferometric images and quantify their uncertainty even in very large settings.

Selected publications

2024

  1. Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
    Tobías I. Liaudat, Matthijs Mars, Matthew A. Price, and 3 more authors
    RAS Techniques and Instruments, Aug 2024

2023

  1. Proximal Nested Sampling with Data-Driven Priors for Physical Scientists
    Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, and 2 more authors
    Physical Sciences Forum, Jun 2023
  2. Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies
    Tobias Liaudat, Jean-Luc Starck, and Martin Kilbinger
    Frontiers in Astronomy and Space Sciences, Jun 2023
  3. Rethinking data-driven point spread function modeling with a differentiable optical model
    Tobias Liaudat, Jean-Luc Starck, Martin Kilbinger, and 1 more author
    Inverse Problems, Feb 2023