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

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Doctorat - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅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

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

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

Mario Pasquato

Alessandro A. Trani

Laurence Perreault-Levasseur

Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to pred… (voir plus)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 (prépublication)

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Micah Bowles

A. Scaife

Laurence Perreault-Levasseur

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-29

ArXiv (prépublication)

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

Laurence Perreault-Levasseur

Carolina Cuesta-lazaro

C. Modi

2023-11-29

ArXiv (prépublication)

The search for the lost attractor

Mario Pasquato

Syphax Haddad

Pierfrancesco Di Cintio

No'e Dia

Mircea Petrache

Ugo Niccolo Di Carlo

Alessandro A. Trani

Laurence Perreault-Levasseur

2023-11-27

ArXiv (prépublication)

Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise

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)

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Matthew Ho

Laurence Perreault-Levasseur

Shirley Ho

Benjamin Wandelt

2023-10-13

Monthly Notices of the Royal Astronomical Society: Letters (publié)

Time Delay Cosmography with a Neural Ratio Estimator

Eve Campeau-Poirier

Laurence Perreault-Levasseur

Adam Coogan

We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…

2023-09-27

ArXiv (prépublication)

AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images

Connor J Stone

Stéphane Courteau

Jean-Charles Cuillandre

Laurence Perreault-Levasseur

Nikhil Arora

2023-08-16

Monthly Notices of the Royal Astronomical Society (publié)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

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

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

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)

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

Jessica Louise Smith

Manuel Solimano

Nikolaus Sulzenauer

Joaquin Vieira

David Vizgan

Axel Weiß

K. Whitaker

2023-06-05

Nature (publié)