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 :
Doctorat - UdeM
Co-superviseur⋅e :
Stagiaire de recherche - McGill
Superviseur⋅e principal⋅e :
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Stagiaire de recherche - UdeM
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Maîtrise recherche - McGill
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Co-superviseur⋅e :
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Postdoctorat - UdeM
Co-superviseur⋅e :
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Postdoctorat - McGill
Superviseur⋅e principal⋅e :

Publications

Strong gravitational lensing as a probe of dark matter
Simona Vegetti
Simon Birrer
Giulia Despali
C. Fassnacht
Daniel A. Gilman
L.
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.
Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
Gabriel Missael Barco
Alexandre Adam
Connor Stone
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.
Improving Gradient-Guided Nested Sampling for Posterior Inference
Pablo Lemos
Nikolay Malkin
Will Handley
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.
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
Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing
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.
Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics
Antoine Bourdin
Ronan Legin
Matthew Ho
Alexandre Adam
Variable Star Light Curves in Koopman Space
Nicolas Mekhaël
Mario Pasquato
Gaia Carenini
Vittorio F. Braga
Piero Trevisan
Giuseppe Bono
TEMPLATES: Characterization of a Merger in the Dusty Lensing SPT0418-47 System
Jared Cathey
Anthony H. Gonzalez
Sidney Lower
Kedar A. Phadke
Justin Spilker
Manuel Aravena
Matthew Bayliss
Jack E. Birkin
Simon Birrer
Scott Chapman
Håkon Dahle
Christopher C. Hayward
Ryley Hill
Taylor A. Hutchison
Keunho J. Kim
Guillaume Mahler
Daniel P. Marrone
Desika Narayanan
Alexander Navarre … (voir 7 de plus)
Cassie Reuter
Jane R Rigby
Keren Sharon
Manuel Solimano
Nikolaus Sulzenauer
Joaquin Vieira
David Vizgan
Interpretable Machine Learning for Finding Intermediate-mass Black Holes
Mario Pasquato
Piero Trevisan
Abbas Askar
Pablo Lemos
Gaia Carenini
Michela Mapelli
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
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Pablo Lemos
Sammy N. Sharief
Nikolay Malkin
On Diffusion Modeling for Anomaly Detection
Victor Livernoche
Vineet Jain
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.