Portrait de Laurence Perreault-Levasseur

Laurence Perreault-Levasseur

Membre académique principal

levassel@mila.quebec

Professeure adjointe, Université de Montréal, Département de physique

Sujets de recherche

Apprentissage profond

Inférence bayésienne

Modèles génératifs

Modèles probabilistes

Systèmes dynamiques

Vision par ordinateur

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

Doctorat - UdeM

Tara Akhound-Sadegh

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siamak Ravanbakhsh

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Mohammed Chamma

Postdoctorat - McGill

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Christian Lemay

Stagiaire de recherche - UdeM

Postdoctorat - UdeM

Co-superviseur⋅e :

Michael Matesic

Doctorat - UdeM

Guillaume Payeur

Doctorat - UdeM

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Postdoctorat - McGill

Sammy Nasser Sharief

Maîtrise recherche - UdeM

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - McGill

Co-superviseur⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Publications

Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine

C. L. Rhea

J. Hlavacek-Larrondo

Ralph P. Kraft

Ákos Bogdán

Laurence Perreault-Levasseur

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new fr… (voir plus)ontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-

2024-09-15

ArXiv (prépublication)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Nikolay Malkin

Will Handley

Laurence Perreault-Levasseur

We present a performant, general-purpose gradient-guided nested sampling algorithm, …

2024-07-22

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

Assessing the Viability of Generative Modeling in Simulated Astronomical Observations

Patrick Janulewicz

Laurence Perreault-Levasseur

Tracy Webb

In this paper, we use methods for assessing the quality of generative models and apply them to a problem from the physical sciences. We turn… (voir plus) our attention to astrophysics, where cosmological simulations are often used to create mock observations that mimic telescope images. These simulations and their mock observations are often slow and challenging to generate, inspiring some to use generative modeling to enhance the amount of data available to study. In this work, we add realism to simulated images of galaxy clusters and use probability mass estimation to assess their fidelity compared to reality. We find that the simulations are biased compared to real observations and suggest that researchers applying generative modeling to these systems should proceed with caution.

2024-06-16

ICML.cc/2024/Workshop/SPIGM (poster)

Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing

Laurence Perreault-Levasseur

In recent years, there has been increasing interest in the field of astrophysics in applying Neural Ratio Estimators (NREs) to large-scale i… (voir plus)nference problems where both amortization and marginalization over a large number of nuisance parameters are needed. Here, in order to assess the true potential of this method to produce unbiased inference on real data, we investigate the robustness of NREs to distribution shifts and model misspecification in the specific scientific application of the measurement of dark matter population-level parameters using strong gravitational lensing. We investigate the behaviour of a trained NRE for test data presenting distributional shifts inside the bounds of training, as well as out of distribution, both in the linear and non-linear parameters of this problem. While our results show that NREs perform when tested perfectly in distribution, we find that they exhibit significant biases and drawbacks when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs to real astrophysical data, where underlying distributions are not perfectly known and models do not perfectly reconstruct the true underlying distributions.

2024-06-16

ICML.cc/2024/Workshop/SPIGM (poster)

Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics

Antoine Bourdin

Matthew Ho

Laurence Perreault-Levasseur

2024-06-15

ICML.cc/2024/Workshop/AI4Science (poster)

Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at $z\sim6.6$

Manuela Bischetti

Hyunseop 현섭 Choi 최

Fabrizio Fiore

Chiara Feruglio

Stefano Carniani

Valentina D'Odorico

Eduardo Banados

Huanqing Chen

Roberto Decarli

Simona Gallerani

J. Hlavacek-Larrondo

Samuel Lai

Karen M. Leighly

Chiara Mazzucchelli

Laurence Perreault-Levasseur

Roberta Tripodi

Fabian Walter

Feige Wang

Jinyi Yang

Maria Vittoria Zanchettin … (voir 1 de plus)

Yongda Zhu

2024-04-17

ArXiv (prépublication)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

M. J. Yantovski-Barth

Landung Setiawan

Cordero Core

Charles Wilson

Gabriel Missael Barco

Laurence Perreault-Levasseur

2023-12-31

Journal of Open Source Software (publié)

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

Mario Pasquato

Alessandro Alberto Trani

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.

2023-11-28

arXiv (prépublication)

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Micah Bowles

A. Scaife

Laurence Perreault-Levasseur

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-28

arXiv (prépublication)

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

Laurence Perreault-Levasseur

Carolina Cuesta-Lazaro

Chirag Modi

2023-11-28

arXiv (prépublication)

Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra

C. L. Rhea

J. Hlavacek-Larrondo

Ralph P. Kraft

Ákos Bogdán

Laurence Perreault-Levasseur

2023-11-28

ArXiv (prépublication)

The search for the lost attractor

Mario Pasquato

Syphax Haddad

Pierfrancesco Di Cintio

Mircea Petrache

Ugo Niccolò Di Carlo

Alessandro Alberto Trani

Laurence Perreault-Levasseur

2023-11-26

arXiv (prépublication)