Yashar Hezaveh

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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 :

Missael Barco

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

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Misha Barth

Doctorat - UdeM

Co-superviseur⋅e :

Dhvani Doshi

Maîtrise recherche - McGill

Andreas Filipp

Doctorat - UdeM

Superviseur⋅e principal⋅e :

andreas.filipp@umontreal.ca

Ronan Legin

Doctorat - UdeM

Co-superviseur⋅e :

Pablo Lemos

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Pierre-Luc Bacon

Nicolas Payot

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Nolan Smyth

Postdoctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Zach Sumners

Maîtrise recherche - McGill

Yitian Sun

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Wassim Tenachi

Postdoctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Google Scholar

Publications

Strong gravitational lensing as a probe of dark matter

Simona Vegetti

Simon Birrer

Giulia Despali

C. Fassnacht

Daniel A. Gilman

J. McKean

D. Powell

Conor M. O'riordan

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)

Variable Star Light Curves in Koopman Space

Nicolas Mekhaël

Mario Pasquato

Gaia Carenini

V. Braga

Piero Trevisan

Giuseppe Bono

We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus… (voir plus) on light curves of RRLyrae type variables, in the Galactic globular cluster

2024-07-23

ArXiv (prépublication)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Pablo Lemos

Nikolay Malkin

Will Handley

Yoshua Bengio

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)

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

Andreas Filipp

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)

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

Andreas Filipp

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

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)

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

2024-05-13

The Astrophysical Journal (publié)

Interpretable Machine Learning for Finding Intermediate-mass Black Holes

Mario Pasquato

Piero Trevisan

Abbas Askar

Pablo Lemos

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

Pablo Lemos

Sammy N. Sharief

Nikolay Malkin

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

Vineet Jain

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)