Laurence Perreault Levasseur

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

2020-10

$\texttt{deep21}$: 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
arXiv preprint arXiv:2010.15843
(2020-10-29)
arxiv.orgPDF

2020-07

HInet: Generating neutral hydrogen from dark matter with neural networks
Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho and Laurence Perreault-Levasseur
arXiv preprint arXiv:2007.10340
(2020-07-20)
ui.adsabs.harvard.eduPDF

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

Publications collected and formatted using Paperoni

array(1) { ["wp-wpml_current_language"]=> string(2) "fr" }