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Laurence Perreault-Levasseur

Membre académique associé
Professeure adjointe, Université de Montréal, Département de physique
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Biographie

Laurence Perreault-Levasseur est titulaire de la Chaire de recherche du Canada en cosmologie computationnelle et en intelligence artificielle. Elle est professeure adjointe à l'Université de Montréal et membre associée de Mila – Institut québécois d’intelligence artificielle, où elle mène des recherches sur le développement et l'application de méthodes d'apprentissage automatique à la cosmologie. Elle est également chercheuse invitée au Flatiron Institute, à New York. Auparavant, elle a été chargée de recherche au Center for Computational Astrophysics du Flatiron Institute et boursière postdoctorale du KIPAC à l'Université de Stanford. Laurence Perreault-Levasseur a obtenu un doctorat de l'Université de Cambridge, où elle a travaillé sur les applications des méthodes de la théorie des champs effectifs ouverts au formalisme de l'inflation. Elle est titulaire d'une licence et d'une maîtrise en sciences de l'Université McGill.

Étudiants actuels

Alexandre Adam
Doctorat - UdeM
alexandre.adam@mila.quebec
Github
Google Scholar
Tara Akhound-Sadegh
Doctorat - McGill
Superviseur⋅e principal⋅e :
Siamak Ravanbakhsh
tara.akhoundsadegh@mila.quebec
Missael Barco
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
missael-gabriel.barco@mila.quebec
Site web
Github
Google Scholar
Misha Barth
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Yashar Hezaveh
michael.barth@mila.quebec
Site web
Antoine Bourdin
Stagiaire de recherche - McGill
Co-superviseur⋅e :
Yashar Hezaveh
antoine.bourdin@mila.quebec
Github
Joseph Choi
Postdoctorat - UdeM
joseph.choi@mila.quebec
Site web
Etienne Collin
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Yashar Hezaveh
etienne.collin@mila.quebec
Site web
Github
Noé Dia
Stagiaire de recherche - UdeM
noe.dia@mila.quebec
Andreas Filipp
Doctorat - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
andreas.filipp@mila.quebec
Github
Patrick Janulewicz
Maîtrise recherche - McGill
patrick.janulewicz@mila.quebec
Rawan Karam
Maîtrise recherche - UdeM
rawan.karam@mila.quebec
Jenna Karcheski
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Yashar Hezaveh
jenna.karcheski@mila.quebec
Github
Stephanie Lee
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Yashar Hezaveh
yung-hsin.lee@mila.quebec
Ronan Legin
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Yashar Hezaveh
ronan.legin@mila.quebec
Site web
Github
Pablo Lemos
Postdoctorat - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
pablo.lemos@mila.quebec
Site web
Github
Google Scholar
Laura Leuzzi
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
laura.leuzzi@mila.quebec
Parker Levesque
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
parker.levesque@mila.quebec
Github
Michael Matesic
Doctorat - UdeM
michael.matesic@mila.quebec
Github
Mario Pasquato
Visiteur de recherche indépendant - University of Padua
mario.pasquato@mila.quebec
Site web
Github
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Nicolas Payot
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Yashar Hezaveh
nicolas.payot@mila.quebec
Github
Sacha Perry-Fagant
Doctorat - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
sacha.perry-fagant@mila.quebec
Site web
Github
Salma Salhi
Maîtrise recherche - UdeM
salhi.salma@mila.quebec
Sammy Nasser Sharief
Maîtrise recherche - UdeM
sammy.sharief@mila.quebec
Site web
Github
Connor Stone
Postdoctorat - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
connor.stone@mila.quebec

Publications

Strong gravitational lensing as a probe of dark matter
Simona Vegetti
Simon Birrer
Giulia Despali
C. Fassnacht
Daniel A. Gilman
Yashar Hezaveh
L.
Laurence Perreault-Levasseur
J. McKean
D. Powell
Conor M. O'riordan
G.
Vernardos
Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple… (voir plus) images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution follow-up observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing.
2024-07-30
Space Science Reviews (publié)
doi.org
arxiv.org
Improving Gradient-Guided Nested Sampling for Posterior Inference
Pablo Lemos
Nikolay Malkin
Will Handley
Yoshua Bengio
Yashar Hezaveh
Laurence Perreault-Levasseur
We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable … (voir plus)programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows GGNS to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at $z\sim6.6$
Manuela Bischetti
Hyunseop Choi
Fabrizio Fiore
Chiara Feruglio
Stefano Carniani
Valentina D'Odorico
Eduardo Banados
Huanqing Chen
Roberto Decarli
Simona Gallerani
Julie Hlavacek-larrondo
Samuel Lai
K. Leighly
Chiara Mazzucchelli
Laurence Perreault-Levasseur
Roberta Tripodi
Fabian Walter
Feige Wang
Jinyi Yang
Maria Vittoria Zanchettin … (voir 1 de plus)
Yongda Zhu
2024-04-18
ArXiv (prépublication)
doi.org
arxiv.org
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Pablo Lemos
Sammy N. Sharief
Nikolay Malkin
Laurence Perreault-Levasseur
Yashar Hezaveh
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
Improving Gradient-guided Nested Sampling for Posterior Inference
Pablo Lemos
Will Handley
Nikolay Malkin
Yoshua Bengio
Yashar Hezaveh
Laurence Perreault-Levasseur
We present a performant, general-purpose gradient-guided nested sampling algorithm, …
2023-12-06
ArXiv (preprint)
doi.org
openreview.net
Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem
Nicolas Payot
Mario Pasquato
Alessandro A. Trani
Yashar Hezaveh
Laurence Perreault-Levasseur
Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to pred… (voir plus)ict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active Learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem's decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology.
2023-11-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Bayesian Imaging for Radio Interferometry with Score-Based Priors
No'e Dia
M. J. Yantovski-Barth
Alexandre Adam
Micah Bowles
Pablo Lemos
A. Scaife
Yashar Hezaveh
Laurence Perreault-Levasseur
U. Montŕeal
Ciela Institute
Flatiron Institute
2023-11-29
ArXiv (prépublication)
doi.org
arxiv.org
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Alexandre Adam
Connor Stone
Connor Bottrell
Ronan Legin
Yashar Hezaveh
Laurence Perreault-Levasseur
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (voir plus) barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.
2023-11-29
ArXiv (prépublication)
doi.org
arxiv.org
Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot
Pablo Lemos
Laurence Perreault-Levasseur
Carolina Cuesta-lazaro
C. Modi
Yashar Hezaveh
2023-11-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra
C. Rhea
Julie Hlavacek-larrondo
Ralph P. Kraft
Ákos Bogdán
Alexandre Adam
Laurence Perreault-Levasseur
2023-11-29
ArXiv (prépublication)
arxiv.org
arxiv.org
The search for the lost attractor
Mario Pasquato
Syphax Haddad
Pierfrancesco Di Cintio
Alexandre Adam
Pablo Lemos
No'e Dia
Mircea Petrache
Ugo Niccolo Di Carlo
Alessandro A. Trani
Laurence Perreault-Levasseur
Yashar Hezaveh
2023-11-27
ArXiv (prépublication)
arxiv.org
arxiv.org
Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise
Ronan Legin
Alexandre Adam
Yashar Hezaveh
Laurence Perreault-Levasseur
Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (voir plus) of complex, high-dimensional, non-Gaussian, and anisotropic noise. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.
2023-11-03
NeurIPS.cc/2023/Workshop/Deep_Inverse (poster)
openreview.net
openreview.net