Laurence Perreault-Levasseur

Google Scholar

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

Alexandre Adam

PhD - Université de Montréal

Google Scholar

Tara Akhound-Sadegh

PhD - McGill University

Principal supervisor :

Siamak Ravanbakhsh

eirini angeloudi

Research Intern - Université de Montréal

Co-supervisor :

Missael Barco

Master's Research - Université de Montréal

Co-supervisor :

Misha Barth

PhD - Université de Montréal

Principal supervisor :

Antoine Bourdin

Research Intern - McGill University

Co-supervisor :

Joseph Choi

Postdoctorate - Université de Montréal

Etienne Collin

Research Intern - Université de Montréal

Principal supervisor :

contact@etiennecollin.com

Noé Dia

Research Intern - Université de Montréal

Co-supervisor :

Andreas Filipp

PhD - Université de Montréal

Co-supervisor :

andreas.filipp@umontreal.ca

Patrick Janulewicz

Master's Research - McGill University

Rawan Karam

Master's Research - Université de Montréal

Jenna Karcheski

Research Intern - Université de Montréal

Principal supervisor :

Stephanie Lee

Research Intern - Université de Montréal

Principal supervisor :

Ronan Legin

PhD - Université de Montréal

Principal supervisor :

Pablo Lemos

Postdoctorate - Université de Montréal

Co-supervisor :

Research Intern - Université de Montréal

Co-supervisor :

Parker Levesque

Master's Research - Université de Montréal

Co-supervisor :

Michael Matesic

PhD - Université de Montréal

Mario Pasquato

Independent visiting researcher - University of Padua

Master's Research - Université de Montréal

Principal supervisor :

Sacha Perry-Fagant

PhD - Université de Montréal

Co-supervisor :

Salma Salhi

Master's Research - Université de Montréal

Sammy Nasser Sharief

Master's Research - Université de Montréal

Nolan Smyth

Postdoctorate - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Yitian Sun

Postdoctorate - McGill University

Co-supervisor :

Justine Zeghal

Postdoctorate - Université de Montréal

Co-supervisor :

Publications

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

Pablo Lemos

Sammy N. Sharief

Nikolay Malkin

2024-02-06

ArXiv (preprint)

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… (see more)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 (preprint)

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 (preprint)

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… (see more) 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 (preprint)

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

Nicolas Payot

Pablo Lemos

Carolina Cuesta-lazaro

C. Modi

2023-11-29

ArXiv (preprint)

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 (preprint)

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 (preprint)

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.

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 (published)

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 (preprint)

Lie Point Symmetry and Physics-Informed Networks

Tara Akhound-Sadegh