Yashar Hezaveh

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

Missael Barco

Doctorat - UdeM

Superviseur⋅e principal⋅e :

missael-gabriel.barco@mila.quebec

Misha Barth

Doctorat - UdeM

Co-superviseur⋅e :

michael.barth@mila.quebec

Noé Dia

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

noe.dia@mila.quebec

Dhvani Doshi

Maîtrise recherche - McGill

dhvani.doshi@mila.quebec

Andreas Filipp

Doctorat - UdeM

Superviseur⋅e principal⋅e :

andreas.filipp@umontreal.ca

Ronan Legin

Doctorat - UdeM

Co-superviseur⋅e :

ronan.legin@mila.quebec

Pablo Lemos

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

parker.levesque@mila.quebec

pablo.lemos@mila.quebec

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Pierre-Luc Bacon

Nicolas Payot

Maîtrise recherche - UdeM

Co-superviseur⋅e :

nicolas.payot@mila.quebec

Sacha Perry-Fagant

Doctorat - UdeM

Superviseur⋅e principal⋅e :

sacha.perry-fagant@mila.quebec

Nolan Smyth

Postdoctorat - UdeM

Co-superviseur⋅e :

nolan.smyth@mila.quebec

Maîtrise recherche - McGill

zach.sumners@mila.quebec

Yitian Sun

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

yitian.sun@mila.quebec

wassim.tenachi@mila.quebec

Wassim Tenachi

Postdoctorat - UdeM

Co-superviseur⋅e :

Pierre-Luc Bacon

Justine Zeghal

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

justine.zeghal@mila.quebec

Publications

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.

2024-06-16

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

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

Matthew Ho

2024-06-15

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

2024-06-15

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

Hakon 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

2024-05-12

The Astrophysical Journal (publié)

On Diffusion Modeling for Anomaly Detection

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.

2024-05-06

International Conference on Learning Representations (Accept (spotlight))

Interpretable machine learning for finding intermediate-mass black holes

Mario Pasquato

PIERO TREVISAN

ABBAS ASKAR

Pablo Lemos

GAIA CARENINI

MICHELA MAPELLI

Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained… (voir plus) on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to realistically simulate GCs, the simulation physics or initial conditions may fail to fully reflect reality. Therefore our training data may be biased, leading to a failure in generalization on observational data. Both the first issue -- explainability/interpretability -- and the second -- out of distribution generalization and fairness -- are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an XGBoost classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost and a global rule from CORELS.

2024-04-08

The Astrophysical Journal (publié)

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

Yordanka Apostolovski

Manuel Aravena

Timo Anguita

Matthieu Bethermin

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

2024-03-05

Astronomy & Astrophysics (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

2024-02-20

Space Science Reviews (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

2023-12-31

Journal of Open Source Software (publié)

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

Nicolas Payot

Mario Pasquato

Alessandro Alberto Trani

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.

2023-11-28

arXiv (prépublication)

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Pablo Lemos

A. Scaife