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

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

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

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

Interpretable Machine Learning for Finding Intermediate-mass Black Holes

Mario Pasquato

Piero Trevisan

Abbas Askar

Gaia Carenini

Michela Mapelli

2024-04-09

The Astrophysical Journal (publié)

Searching for Strong Gravitational Lenses

Cameron Lemon

Frederic Courbin

Anupreeta More

Paul Schechter

Raoul Cañameras

Ludovic Delchambre

Calvin Leung

Yiping Shu

Chiara Spiniello

Jonas Klüter

Richard G. McMahon

2024-02-21

Space Science Reviews (publié)

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

Sammy N. Sharief

Laurence Perreault-Levasseur

We propose a comprehensive sample-based method for assessing the quality of generative models. The proposed approach enables the estimation … (voir plus)of the probability that two sets of samples are drawn from the same distribution, providing a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models trained on the same dataset. This comparison can be conducted by dividing the space into non-overlapping regions and comparing the number of data samples in each region. The method only requires samples from the generative model and the test data. It is capable of functioning directly on high-dimensional data, obviating the need for dimensionality reduction. Significantly, the proposed method does not depend on assumptions regarding the density of the true distribution, and it does not rely on training or fitting any auxiliary models. Instead, it focuses on approximating the integral of the density (probability mass) across various sub-regions within the data space.

2024-02-06

ArXiv (prépublication)

On Diffusion Modeling for Anomaly Detection

Victor Livernoche

Siamak Ravanbakhsh

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (voir plus)n. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.

2024-01-16

ICLR.cc/2024/Conference (spotlight)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

M. J. Yantovski-Barth

Landung Setiawan

Cordero Core

Charles Wilson

Gabriel Missael Barco

Laurence Perreault-Levasseur

2024-01-01

J. Open Source Softw. (publié)

Extended Lyman-alpha emission towards the SPT2349-56 protocluster at $z=4.3$

Yordanka Apostolovski

Manuel Aravena

Timo Anguita

Matthieu Béthermin

James R. Burgoyne

Scott Chapman

C. Breuck

Anthony R Gonzalez

Max Gronke

Lucia Guaita

Ryley Hill

Sreevani Jarugula

E. Johnston

M. Malkan

Desika Narayanan

Cassie Reuter

Manuel Solimano

Justin Spilker

Nikolaus Sulzenauer … (voir 3 de plus)

Joaquin Vieira

David Vizgan

Axel Weiß

Deep spectroscopic surveys with the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed that some of the brightest infrared so… (voir plus)urces in the sky correspond to concentrations of submillimeter galaxies (SMGs) at high redshift. Among these, the SPT2349-56 protocluster system is amongst the most extreme examples given its high source density and integrated star formation rate. We conducted a deep Lyman-alpha line emission survey around SPT2349-56 using the Multi-Unit Spectroscopic Explorer (MUSE) at the Very Large Telescope (VLT) in order to characterize this uniquely dense environment. Taking advantage of the deep three-dimensional nature of this survey, we performed a sensitive search for Lyman-alpha emitters (LAEs) toward the core and northern extension of the protocluster, which correspond to the brightest infrared regions in this field. Using a smoothed narrowband image extracted from the MUSE datacube around the protocluster redshift, we searched for possible extended structures. We identify only three LAEs at

2023-12-20

Astronomy & Astrophysics (publié)

Improving Gradient-guided Nested Sampling for Posterior Inference

Will Handley

Laurence Perreault-Levasseur

We present a performant, general-purpose gradient-guided nested sampling algorithm, …

2023-12-06

ArXiv (preprint)

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)