Benjamin Remy
PHD STUDENT
Contact Information  
Email:  benjamin.remy [at] cea [dot] fr 
Phone:  
Office:  274 
Affiliation:  IRFU/DApAIM 
Supervisors:  François Lanusse, JeanLuc Starck 
Research Interests
Joint Estimation of Cosmic Shear, PSF, and Galaxy Morphologies
The goal of my PhD thesis is to develop a hierarchical probabilistic model of the observed Euclid images combining physical models with Deep Learning components accounting for unknowns factors. In particular, I aim to build a forward model of Euclid field of views accounting for the PSF, cosmic shear, and galaxy morphology. Fitting this model to observed exposures is a theoretically optimal way to jointly estimate the cosmic shear field and perform the calibration.
Variational Inference and Hierarchical models
So far, solving such inference problem at scale was intractable. I am very interested in efficient optimizationbased inference approaches, such as Variational Inference, replacing expensive Markov Chain Monte Carlo methods, to solve this problem.
Publications
 Probabilistic MassMapping with Neural Score Estimation
Benjamin Remy, François Lanusse, Niall Jeffrey, Jia Liu, and JeanLuc Starck, Ken Osato, Tim Schrabback
accepted at Astronomy & Astrophysics
(arXiv, code)
Workshops proceedings
 Towards solving model bias in cosmic shear forward modeling
Benjamin Remy, François Lanusse and JeanLuc Starck.
Machine Learning and the Physical Sciences Workshop, NeurIPS 2022.
(arXiv)  Neural Posterior Estimation with Differentiable Simulators
Justine Zeghal, François Lanusse, Alewandre Boucaud, Benjamin Remy and Eric Aubourg
Workshop on Machine Learning for Astrophysics, ICML 2022.
(arXiv)  Probabilistic Mapping of Dark Matter by Neural Score Matching
Benjamin Remy, François Lanusse, Zaccharie Ramzi, Jia Liu, Niall Jeffrey and JeanLuc Starck.
Machine Learning and the Physical Sciences Workshop, NeurIPS 2020.
(arXiv, code, poster)  Denoising ScoreMatching for Uncertainty Quantification in Inverse Problems
Zaccharie Ramzi, Benjamin Remy, François Lanusse, JeanLuc Starck and Philippe Ciuciu
Deep Learning and Inverse Problems Workshop, NeurIPS 2020.
(arXiv)
Talks

Astromerique speaker series, University of Montreal, 27th Sep 2022 (invited)

Learning to Discover, Institut Pascal, Saclay, 29th Apr 2022.

Likelihoodfree in Paris, Paris, 21st Apr 2022.

CosmoClub, ETH Zurich Cosmology group, remote, Feb 2022 (invited)
 Recontres de Moriond Cosmology, La Thuile, 29th Jan 2022.

Udopia Doctoral Students Day, Central Supélec, GifsurYvette, Dec 2021.
 Machine learning in astronomical surveys conference. IAP, Paris, Oct 2021.
 IN2P3/IRFU Machine Learning Workshop, March 17th, online. Slides
 Euclid Workshop on Machine Learning and Deep learning. December, 14th 2020, online
 Denoising Score Matching for Uncertainty Quantification in Inverse Problems: Application to gravitational lensing and Magnetic Resonance Imaging, with Zaccharie Ramzi. Machine Learning Club, Nov 18th 2020, online.
Set of slides at https://github.com/bremy/talks.