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

Antoine Bourdin

Stagiaire de recherche - McGill

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

contact@etiennecollin.com

Franco Del Balso

Stagiaire de recherche - UdeM

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

Jenna Karcheski

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Doctorat - UdeM

Co-superviseur⋅e :

Laurence Perreault-Levasseur

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Parker Levesque

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

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

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Laurence Perreault-Levasseur

Publications

Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing

Andreas Filipp

Laurence Perreault-Levasseur

2024-11-08

ArXiv (prépublication)

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Andrea Maccio

2024-10-30

NeurIPS.cc/2024/Workshop/CRL (poster)

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization

Ronan Legin

Maximiliano Isi

Kaze W. K. Wong

Laurence Perreault-Levasseur

2024-10-25

ArXiv (prépublication)

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis

Zehao Jin

Mario Pasquato

Benjamin L. Davis

A. Macciò

2024-10-18

ArXiv (prépublication)

A Data-driven Discovery of the Causal Connection between Galaxy and Black Hole Evolution

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Tristan Deleu

Yu Luo

Changhyun Cho

Pablo Lemos

Laurence Perreault-Levasseur

Xi Kang

Andrea Maccio

2024-10-01

ArXiv (prépublication)

Strong gravitational lensing as a probe of dark matter

Simona Vegetti

Simon Birrer

Giulia Despali

C. Fassnacht

Daniel A. Gilman

L.

Laurence Perreault-Levasseur

J. McKean

D. Powell

Conor M. O'riordan

G.

Vernardos

Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple… (voir plus) images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution follow-up observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing.

2024-07-30

Space Science Reviews (publié)

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems

Gabriel Missael Barco

Alexandre Adam

Connor Stone

Laurence Perreault-Levasseur

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-… (voir plus)level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations and showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates.

2024-07-24

ArXiv (prépublication)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Pablo Lemos

Nikolay Malkin

Will Handley

Laurence Perreault-Levasseur

We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable … (voir plus)programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows GGNS to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

Laurence Perreault-Levasseur

2024-06-21

ArXiv (prépublication)

Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing

Andreas Filipp

Laurence Perreault-Levasseur

In recent years, there has been increasing interest in the field of astrophysics in applying Neural Ratio Estimators (NREs) to large-scale i… (voir plus)nference problems where both amortization and marginalization over a large number of nuisance parameters are needed. Here, in order to assess the true potential of this method to produce unbiased inference on real data, we investigate the robustness of NREs to distribution shifts and model misspecification in the specific scientific application of the measurement of dark matter population-level parameters using strong gravitational lensing. We investigate the behaviour of a trained NRE for test data presenting distributional shifts inside the bounds of training, as well as out of distribution, both in the linear and non-linear parameters of this problem. While our results show that NREs perform when tested perfectly in distribution, we find that they exhibit significant biases and drawbacks when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs to real astrophysical data, where underlying distributions are not perfectly known and models do not perfectly reconstruct the true underlying distributions.

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics

Antoine Bourdin

Ronan Legin

Matthew Ho

Alexandre Adam

Laurence Perreault-Levasseur

2024-06-16

ICML.cc/2024/Workshop/AI4Science (poster)

Variable Star Light Curves in Koopman Space

Nicolas Mekhaël

Mario Pasquato

Gaia Carenini

Vittorio F. Braga

Piero Trevisan

Giuseppe Bono

2024-06-16

ICML.cc/2024/Workshop/AI4Science (spotlight)