Laurence Perreault Levasseur

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Membre Académique Associé
Laurence Perreault Levasseur
Professeure adjointe, Université de Montréal
Laurence Perreault Levasseur

Laurence Perreault Levasseur is an assistant professor at the University of Montréal and an Associate Member of Mila, where she conducts research in 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 Flatiron research fellow at the Center for Computational Astrophysics in the Flatiron Institute and a KIPAC postdoctoral fellow at Stanford University. Laurence completed her PhD degree at the University of Cambridge, where she worked on applications of open effective field theory methods to the formalism of inflation. She received her B.Sc. and M.Sc. degrees from McGill University.

Publications

2021-07

HInet: Generating Neutral Hydrogen from Dark Matter with Neural Networks
Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho and Laurence Perreault-Levasseur

2021-04

CosmicRIM : Reconstructing Early Universe by Combining Differentiable Simulations with Recurrent Inference Machines
Chirag Modi, François Lanusse, Uroš Seljak, David N. Spergel and Laurence Perreault-Levasseur
arXiv: Cosmology and Nongalactic Astrophysics
(2021-04-26)
ui.adsabs.harvard.eduPDF
deep21: a deep learning method for 21 cm foreground removal
T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro, Peter Melchior, Shirley Ho, Laurence Perreault-Levasseur and David N. Spergel
Journal of Cosmology and Astroparticle Physics
(2021-04-01)
ui.adsabs.harvard.edu
Analyzing the Kinematics of SITELLE Spectra using Machine Learning
Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet, Julie Hlavacek-Larrondo, Sébastien Fabbro, Natalia Vale Asari, Kathryn Grasha, Laurence Perreault Levasseur and Laurence Prasow-Émond
Extragalactic Spectroscopic Surveys: Past
(2021-04-01)
ui.adsabs.harvard.edu

2021-02

A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios
Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet, Myriam Prasow-Emond, Julie Hlavacek-Larrondo, Natalia Vale Asari, Kathryn Grasha and Laurence Perreault-Levasseur
arXiv preprint arXiv:2102.06230
(2021-02-11)
aps.arxiv.orgPDF

2020-12

Modeling assembly bias with machine learning and symbolic regression
Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho and Laurence Perreault-Levasseur
arXiv preprint arXiv:2012.00111
(2020-12-04)
inspirehep.netPDF

2020-10

deep21: a Deep Learning Method for 21cm Foreground Removal
T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro, Peter Melchior, Shirley Ho, Laurence Perreault-Levasseur and David N. Spergel
arXiv preprint arXiv:2010.15843
(2020-10-29)
ui.adsabs.harvard.eduPDF
$\texttt{deep21}$: a Deep Learning Method for 21cm Foreground Removal
T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro, Peter Melchior, Shirley Ho, Laurence Perreault-Levasseur and David N. Spergel
(venue unknown)
(2020-10-29)
arxiv.orgPDF
A Machine-learning Approach to Integral Field Unit Spectroscopy Observations. II. H II Region Line Ratios
Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet, Myriam Prasow-Émond, Julie Hlavacek-Larrondo, Natalia Vale Asari, Kathryn Grasha and Laurence Perreault-Levasseur
The Astrophysical Journal
(2020-10-05)
ui.adsabs.harvard.edu

2020-06

Bayesian Neural Networks.
Tom Charnock, Laurence Perreault-Levasseur and François Lanusse
arXiv preprint arXiv:2006.01490
(2020-06-02)
ui.adsabs.harvard.eduPDF

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