Portrait of Yashar Hezaveh

Yashar Hezaveh

Associate Academic Member

Assistant Professor, Université de Montréal, Department of Physics

Research Topics

Computer Vision

Deep Learning

Representation Learning

Biography

Yashar Hezaveh is an associate academic member of Mila – Quebec Artificial Intelligence Institute and director of the Montréal Institute for Astrophysical Data Analysis and Machine Learning (Ciela). He is an assistant professor in the Department of Physics at Université de Montréal and the Canada Research Chair in Astrophysical Data Analysis and Machine Learning. In addition, Hezaveh is an associate member of McGill University’s Trottier Space Institute, and a visiting fellow at the Center for Computational Astrophysics at Flatiron Institute in New York and at the Perimeter Institute for Theoretical Physics in Waterloo, Ontario. He was previously a research fellow at the Flatiron Institute (2018–2019) and a NASA Hubble Fellow at Stanford University (2013–2018).

Hezaveh is a world leader in the analysis of astrophysical data using deep learning. His current research focuses primarily on Bayesian inference in AI, the goal being to learn about the distribution of dark matter in strongly lensed galaxies using data from large cosmological surveys. His research is supported by the Schmidt Futures Foundation and the Simons Foundation.

Current Students

eirini angeloudi

Research Intern - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Master's Research - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Master's Research - McGill University

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

andreas.filipp@umontreal.ca

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Parker Levesque

Master's Research - Université de Montréal

Co-supervisor :

Pierre-Luc Bacon

Master's Research - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Sacha Perry-Fagant

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Master's Research - McGill University

Postdoctorate - McGill University

Principal supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Co-supervisor :

Pierre-Luc Bacon

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Publications

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Pablo Lemos

A. Scaife

Laurence Perreault-Levasseur

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-29

ArXiv (preprint)

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors

Alexandre Adam

Connor Stone

Connor Bottrell

Ronan Legin

Laurence Perreault-Levasseur

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (see more) barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.

2023-11-29

ArXiv (preprint)

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

Nicolas Payot

Pablo Lemos

Laurence Perreault-Levasseur

Carolina Cuesta-lazaro

C. Modi

2023-11-29

ArXiv (preprint)

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

2023-11-27

ArXiv (preprint)

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

Ronan Legin

Alexandre Adam

Laurence Perreault-Levasseur

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (see more) 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

Ronan Legin

Matthew Ho

Pablo Lemos

Laurence Perreault-Levasseur

Shirley Ho

Benjamin Wandelt

2023-10-13

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

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 (preprint)

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 (published)

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 (published)

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… (see more)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 (published)

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 (published)

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 … (see 19 more)

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 (published)