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Laurence Perreault-Levasseur

Membre académique associé

Professeure adjointe, Université de Montréal, Département de physique

Sujets de recherche

Apprentissage profond

Modèles génératifs

Modèles probabilistes

Réseaux de neurones en graphes

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

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

andreas.filipp@umontreal.ca

Maîtrise recherche - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Michael Matesic

Doctorat - UdeM

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Sammy Nasser Sharief

Maîtrise recherche - UdeM

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - McGill

Co-superviseur⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Publications

Multiphase Black Hole Feedback and a Bright [C ii] Halo in a LoBAL Quasar at z ∼ 6.6

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)

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)

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

Pablo Lemos

Sammy N. Sharief

Nikolay Malkin

Laurence Perreault-Levasseur

We propose a comprehensive sample-based method for assessing the quality of generative models. The proposed approach enables the estimation … (voir plus)of the probability that two sets of samples are drawn from the same distribution, providing a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models trained on the same dataset. This comparison can be conducted by dividing the space into non-overlapping regions and comparing the number of data samples in each region. The method only requires samples from the generative model and the test data. It is capable of functioning directly on high-dimensional data, obviating the need for dimensionality reduction. Significantly, the proposed method does not depend on assumptions regarding the density of the true distribution, and it does not rely on training or fitting any auxiliary models. Instead, it focuses on approximating the integral of the density (probability mass) across various sub-regions within the data space.

2024-02-06

ArXiv (prépublication)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

Laurence Perreault-Levasseur

2024-01-01

J. Open Source Softw. (publié)

Improving Gradient-guided Nested Sampling for Posterior Inference

Pablo Lemos

Will Handley

Nikolay Malkin

Laurence Perreault-Levasseur

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

2023-12-06

ArXiv (preprint)

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

Nicolas Payot

Mario Pasquato

Alessandro A. 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.

2023-11-29

ArXiv (prépublication)

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Pablo Lemos

A. Scaife

Laurence Perreault-Levasseur

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-29

ArXiv (prépublication)

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

Nicolas Payot

Pablo Lemos

Laurence Perreault-Levasseur

Carolina Cuesta-lazaro

C. Modi

2023-11-29

ArXiv (prépublication)

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)

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

2023-11-27

ArXiv (prépublication)

Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise

Ronan Legin

Alexandre Adam

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)

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Ronan Legin

Matthew Ho

Pablo Lemos

Laurence Perreault-Levasseur

Shirley Ho

Benjamin Wandelt

2023-10-13

Monthly Notices of the Royal Astronomical Society: Letters (publié)