Portrait de Yashar Hezaveh

Yashar Hezaveh

Membre académique associé

Professeur adjoint, Université de Montréal, Département de physique

Sujets de recherche

Apprentissage de représentations

Apprentissage profond

Vision par ordinateur

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

eirini angeloudi

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Doctorat - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Maîtrise recherche - McGill

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

andreas.filipp@umontreal.ca

Doctorat - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Parker Levesque

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Pierre-Luc Bacon

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Sacha Perry-Fagant

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Postdoctorat - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Maîtrise recherche - McGill

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Postdoctorat - UdeM

Co-superviseur⋅e :

Pierre-Luc Bacon

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Publications

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

Laurence Perreault-Levasseur

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines

Alexandre Adam

Laurence Perreault-Levasseur

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)

Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise

Ronan Legin

Alexandre Adam

Laurence Perreault-Levasseur

2023-06-06

The Astrophysical Journal Letters (publié)

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

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)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

Laurence Perreault-Levasseur

2023-04-24

ICML.cc/2023/Conference (poster)

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

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)

A Framework for Obtaining Accurate Posteriors of Strong Gravitational Lensing Parameters with Flexible Priors and Implicit Likelihoods Using Density Estimation

Ronan Legin

Laurence Perreault-Levasseur

Benjamin Wandelt

We report the application of implicit likelihood inference to the prediction of the macroparameters of strong lensing systems with neural ne… (voir plus)tworks. This allows us to perform deep-learning analysis of lensing systems within a well-defined Bayesian statistical framework to explicitly impose desired priors on lensing variables, obtain accurate posteriors, and guarantee convergence to the optimal posterior in the limit of perfect performance. We train neural networks to perform a regression task to produce point estimates of lensing parameters. We then interpret these estimates as compressed statistics in our inference setup and model their likelihood function using mixture density networks. We compare our results with those of approximate Bayesian neural networks, discuss their significance, and point to future directions. Based on a test set of 100,000 strong lensing simulations, our amortized model produces accurate posteriors for any arbitrary confidence interval, with a maximum percentage deviation of 1.4% at the 21.8% confidence level, without the need for any added calibration procedure. In total, inferring 100,000 different posteriors takes a day on a single GPU, showing that the method scales well to the thousands of lenses expected to be discovered by upcoming sky surveys.

2023-01-19

The Astrophysical Journal (publié)

Posterior samples of source galaxies in strong gravitational lenses with score-based priors

Alexandre Adam

Adam Coogan

Nikolay Malkin

Ronan Legin

Laurence Perreault-Levasseur

2022-11-07

ArXiv (prépublication)