Portrait de Yashar Hezaveh

Yashar Hezaveh

Membre académique associé
Professeur adjoint, Université de Montréal, Département de physique
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Biographie

Yashar Hezaveh est membre associé de Mila – Institut québécois d'intelligence artificielle et directeur de Ciela – Institut de Montréal pour l'analyse des données astrophysiques et l'apprentissage automatique. Il est professeur adjoint au Département de physique de l'Université de Montréal, titulaire d'une chaire de recherche du Canada en analyse de données astrophysiques et apprentissage automatique, membre associé de l'Institut spatial Trottier de l'Université McGill et chercheur invité au Center for Computational Astrophysics du Flatiron Institute (New York) et au Perimeter Institute. Auparavant, il a été chercheur au Flatiron Institute (2018-2019) et boursier Hubble de la NASA à l'Université de Stanford (2013-2018).

Il est un leader mondial dans l'analyse des données astrophysiques avec l'apprentissage automatique. Ses recherches actuelles portent principalement sur l'inférence bayésienne dans l'IA (par exemple, les modèles de diffusion) et visent à faire progresser les connaissances sur la distribution de la matière noire dans les galaxies fortement lenticulaires à l'aide de données provenant de grands relevés cosmologiques. Ses recherches sont soutenues par la Schmidt Futures Foundation et la Simons Foundation.

Étudiants actuels

Missael Barco
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
missael-gabriel.barco@mila.quebec
Site web
Github
Google Scholar
Misha Barth
Doctorat - UdeM
Co-superviseur⋅e :
Laurence Perreault-Levasseur
michael.barth@mila.quebec
Site web
Antoine Bourdin
Stagiaire de recherche - McGill
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
antoine.bourdin@mila.quebec
Github
Etienne Collin
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Laurence Perreault-Levasseur
etienne.collin@mila.quebec
Site web
Github
Franco Del Balso
Stagiaire de recherche - UdeM
franco.delbalso@mila.quebec
Github
Dhvani Doshi
Maîtrise recherche - McGill
dhvani.doshi@mila.quebec
Andreas Filipp
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
andreas.filipp@mila.quebec
Github
Jenna Karcheski
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Laurence Perreault-Levasseur
jenna.karcheski@mila.quebec
Github
Stephanie Lee
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Laurence Perreault-Levasseur
yung-hsin.lee@mila.quebec
Ronan Legin
Doctorat - UdeM
Co-superviseur⋅e :
Laurence Perreault-Levasseur
ronan.legin@mila.quebec
Site web
Github
Pablo Lemos
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
pablo.lemos@mila.quebec
Site web
Github
Google Scholar
Laura Leuzzi
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
laura.leuzzi@mila.quebec
Parker Levesque
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
parker.levesque@mila.quebec
Github
Hasti Nafisi
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Guy Wolf
hasti.nafisi@mila.quebec
Nicolas Payot
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Laurence Perreault-Levasseur
nicolas.payot@mila.quebec
Github
Sacha Perry-Fagant
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
sacha.perry-fagant@mila.quebec
Site web
Github
Connor Stone
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Laurence Perreault-Levasseur
connor.stone@mila.quebec

Publications

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot
Pablo Lemos
Laurence Perreault-Levasseur
Carolina Cuesta-lazaro
C. Modi
Yashar Hezaveh
2023-11-29
ArXiv (prépublication)
arxiv.org
arxiv.org
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
Laurence Perreault-Levasseur
Yashar Hezaveh
2023-11-27
ArXiv (prépublication)
arxiv.org
arxiv.org
Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise
Ronan Legin
Alexandre Adam
Yashar Hezaveh
Laurence Perreault-Levasseur
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
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
Laurence Perreault-Levasseur
Shirley Ho
Yashar Hezaveh
Benjamin Wandelt
2023-10-13
Monthly Notices of the Royal Astronomical Society: Letters (publié)
doi.org
arxiv.org
Time Delay Cosmography with a Neural Ratio Estimator
Eve Campeau-Poirier
Laurence Perreault-Levasseur
Adam Coogan
Yashar Hezaveh
We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…
2023-09-27
ArXiv (prépublication)
arxiv.org
arxiv.org
AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images
Connor J Stone
Stéphane Courteau
Jean-Charles Cuillandre
Yashar Hezaveh
Laurence Perreault-Levasseur
Nikhil Arora
2023-08-16
Monthly Notices of the Royal Astronomical Society (publié)
doi.org
arxiv.org
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Pablo Lemos
Adam Coogan
Yashar Hezaveh
Laurence Perreault-Levasseur
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines
Alexandre Adam
Laurence Perreault-Levasseur
Yashar Hezaveh
Max Welling
Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass densit… (voir plus)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.
2023-06-27
The Astrophysical Journal (publié)
doi.org
arxiv.org
Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise
Ronan Legin
Alexandre Adam
Yashar Hezaveh
Laurence Perreault-Levasseur
2023-06-06
The Astrophysical Journal Letters (publié)
doi.org
arxiv.org
Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy
Justin Spilker
Kedar A. Phadke
Manuel Aravena
Melanie Archipley
Matthew Bayliss
Jack E. Birkin
Matthieu Béthermin
James R. Burgoyne
Jared Cathey
Scott Chapman
Håkon Dahle
Anthony H. Gonzalez
Gayathri Gururajan
Christopher C Hayward
Yashar Hezaveh
Ryley Hill
Taylor A. Hutchison
Keunho J. Kim
Seonwoo Kim
D. Law … (voir 19 de plus)
Ronan Legin
M. Malkan
Daniel P. Marrone
E. Murphy
Desika Narayanan
Alexander Navarre
Grace M. Olivier
J. Rich
Jane R Rigby
Cassie Reuter
J. Rhoads
Keren Sharon
J. Smith
Manuel Solimano
Nikolaus Sulzenauer
Joaquin Vieira
David Vizgan
Axel Weiß
K. Whitaker
2023-06-05
Nature (publié)
doi.org
arxiv.org
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Pablo Lemos
Adam Coogan
Yashar Hezaveh
Laurence Perreault-Levasseur
2023-04-24
ICML.cc/2023/Conference (poster)
doi.org
openreview.net
Spectroscopy of CASSOWARY gravitationally-lensed galaxies in SDSS: characterisation of an extremely bright reionization-era analog at z = 1.42
Ramesh Mainali
Daniel P Stark
Tucker Jones
Richard S Ellis
Yashar Hezaveh
Jane R Rigby
We present new observations of sixteen bright (r = 19 − 21) gravitationally lensed galaxies at z ≃ 1 − 3 selected from the CASSOWARY s… (voir plus)urvey. Included in our sample is the z = 1.42 galaxy CSWA-141, one of the brightest known reionization-era analogs at high redshift (g=20.5), with a large sSFR (31.2 Gyr−1) and an [OIII]+Hβ equivalent width (EW[OIII] + Hβ=730 Å) that is nearly identical to the average value expected at z ≃ 7 − 8. In this paper, we investigate the rest-frame UV nebular line emission in our sample with the goal of understanding the factors that regulate strong CIII] emission. Whereas most of the sources in our sample show weak UV line emission, we find elevated CIII] in the spectrum of CSWA-141 (EWCIII]=4.6±1.9 Å) together with detections of other prominent emission lines (OIII], Si III], Fe II⋆, Mg II). We compare the rest-optical line properties of high redshift galaxies with strong and weak CIII] emission, and find that systems with the strongest UV line emission tend to have young stellar populations and nebular gas that is moderately metal-poor and highly ionized, consistent with trends seen at low and high redshift. The brightness of CSWA-141 enables detailed investigation of the extreme emission line galaxies which become common at z > 6. We find that gas traced by the CIII] doublet likely probes higher densities than that traced by [OII] and [SII]. Characterisation of the spectrally resolved Mg II emission line and several low ionization absorption lines suggests neutral gas around the young stars is likely optically thin, potentially facilitating the escape of ionizing radiation.
2023-02-08
Monthly Notices of the Royal Astronomical Society (publié)
doi.org
arxiv.org