Laurence Perreault-Levasseur

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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

Doctorat - UdeM

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siamak Ravanbakhsh

eirini angeloudi

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Missael Barco

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Misha Barth

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Joseph Choi

Postdoctorat - UdeM

Andreas Filipp

Doctorat - UdeM

Co-superviseur⋅e :

andreas.filipp@umontreal.ca

Patrick Janulewicz

Maîtrise recherche - McGill

Rawan Karam

Maîtrise recherche - UdeM

Ronan Legin

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Pablo Lemos

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

Salma Salhi

Maîtrise recherche - UdeM

Sammy Nasser Sharief

Maîtrise recherche - UdeM

Nolan Smyth

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Yitian Sun

Postdoctorat - McGill

Co-superviseur⋅e :

Justine Zeghal

Postdoctorat - UdeM

Co-superviseur⋅e :

Publications

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

2024-01-01

J. Open Source Softw. (publié)

Improving Gradient-guided Nested Sampling for Posterior Inference

Pablo Lemos

Will Handley

Nikolay Malkin

Yoshua Bengio

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

2023-12-06

ArXiv (preprint)

openreview.net

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

Nicolas Payot

Mario Pasquato

Alessandro A. Trani

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

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-29

ArXiv (prépublication)

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

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)

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

Nicolas Payot

Pablo Lemos

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

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

2023-11-27

ArXiv (prépublication)

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… (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

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

2023-10-13

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

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 (…

2023-09-27

ArXiv (prépublication)

Lie Point Symmetry and Physics-Informed Networks

Tara Akhound-Sadegh