Portrait of Laurence Perreault-Levasseur is unavailable

Laurence Perreault-Levasseur

Associate Academic Member
Assistant Professor, Université de Montréal, Department of Physics
Research Topics
Computer Vision
Deep Learning
Dynamical Systems
Generative Models
Graph Neural Networks
Probabilistic Models

Biography

Laurence Perreault-Levasseur is the Canada Research Chair in Computational Cosmology and Artificial Intelligence. She is an assistant professor at Université de Montréal and an associate academic member of Mila – Quebec Artificial Intelligence Institute. Perreault-Levasseur’s research focuses on the development and application of machine learning methods to cosmology.

She is also a Visiting Scholar at the Flatiron Institute in New York City. Prior to that, she was a research fellow at their Center for Computational Astrophysics, and a KIPAC postdoctoral fellow at Stanford University.

For her PhD degree at the University of Cambridge, she worked on applications of open effective field theory methods to the formalism of inflation. She completed her BSc and MSc degrees at McGill University.

Current Students

PhD - Université de Montréal
PhD - McGill University
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Master's Research - Université de Montréal
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PhD - Université de Montréal
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Postdoctorate - Université de Montréal
PhD - Université de Montréal
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Master's Research - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Collaborating researcher - Université de Montréal
Master's Research - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Co-supervisor :
Master's Research - Université de Montréal
Master's Research - Université de Montréal
Postdoctorate - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
Postdoctorate - McGill University
Co-supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :

Publications

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot
Pablo Lemos
Carolina Cuesta-lazaro
C. Modi
Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra
C. L. Rhea
J. Hlavacek-Larrondo
Ralph P. Kraft
Ákos Bogdán
Alexandre Adam
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
Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise
Ronan Legin
Alexandre Adam
Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (see more) 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.
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
Shirley Ho
Benjamin Wandelt
Time Delay Cosmography with a Neural Ratio Estimator
Eve Campeau-Poirier
Adam Coogan
We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…
Lie Point Symmetry and Physics-Informed Networks
Tara Akhound-Sadegh
Johannes Brandstetter
Max Welling
Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equiv… (see more)ariant architectures. However, despite their potential, their integration into neural solvers for partial differential equations (PDEs) remains largely unexplored. We explore the integration of PDE symmetries, known as Lie point symmetries, in a major family of neural solvers known as physics-informed neural networks (PINNs). We propose a loss function that informs the network about Lie point symmetries in the same way that PINN models try to enforce the underlying PDE through a loss function. Intuitively, our symmetry loss ensures that the infinitesimal generators of the Lie group conserve the PDE solutions.. Effectively, this means that once the network learns a solution, it also learns the neighbouring solutions generated by Lie point symmetries. Empirical evaluations indicate that the inductive bias introduced by the Lie point symmetries of the PDEs greatly boosts the sample efficiency of PINNs.
AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images
Connor J Stone
Stéphane Courteau
Jean-Charles Cuillandre
Nikhil Arora
Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
Aritra Ghosh
C. Urry
Aayush Mishra
P. Natarajan
D. Sanders
Daisuke Nagai
Chuan Tian
Nico Cappelluti
J. Kartaltepe
M. Powell
Amrit Rau
Ezequiel Treister
We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for ∼… (see more)8 million galaxies in the Hyper Suprime-Cam Wide survey with z ≤ 0.75 and m ≤ 23. GaMPEN is a machine-learning framework that estimates Bayesian posteriors for a galaxy’s bulge-to-total light ratio (L B /L T ), effective radius (R e ), and flux (F). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with 1% of our data set. This two-step process will be critical for applying machine-learning algorithms to future large imaging surveys, such a
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines
Alexandre Adam
Max Welling
Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass densit… (see more)y in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the recurrent inference machine to reconstruct simultaneously an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation.
Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise
Ronan Legin
Alexandre Adam