Portrait of Yashar Hezaveh

Yashar Hezaveh

Associate Academic Member

yashar.hezaveh@mila.quebec

Assistant Professor, Université de Montréal, Department of Physics

Biography

Yashar Hezaveh is an associate academic member of Mila – Quebec Artificial Intelligence Institute and director of the Montréal Institute for Astrophysical Data Analysis and Machine Learning (Ciela). He is an assistant professor in the Department of Physics at Université de Montréal and the Canada Research Chair in Astrophysical Data Analysis and Machine Learning. In addition, Hezaveh is an associate member of McGill University’s Trottier Space Institute, and a visiting fellow at the Center for Computational Astrophysics at Flatiron Institute in New York and at the Perimeter Institute for Theoretical Physics in Waterloo, Ontario. He was previously a research fellow at the Flatiron Institute (2018–2019) and a NASA Hubble Fellow at Stanford University (2013–2018).

Hezaveh is a world leader in the analysis of astrophysical data using deep learning. His current research focuses primarily on Bayesian inference in AI, the goal being to learn about the distribution of dark matter in strongly lensed galaxies using data from large cosmological surveys. His research is supported by the Schmidt Futures Foundation and the Simons Foundation.

Current Students

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

andreas.filipp@mila.quebec

Antoine Bourdin

Research Intern - McGill University

Principal supervisor :

Laurence Perreault-Levasseur

antoine.bourdin@mila.quebec

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

connor.stone@mila.quebec

Master's Research - McGill University

dhvani.doshi@mila.quebec

Research Intern - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

etienne.collin@mila.quebec

Franco Del Balso

Research Intern - McGill University University

franco.delbalso@mila.quebec

Master's Research - Université de Montréal

Principal supervisor :

hasti.nafisi@mila.quebec

Research Intern - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

laura.leuzzi@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

michael.barth@mila.quebec

Master's Research - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

missael-gabriel.barco@mila.quebec

Master's Research - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

nicolas.payot@mila.quebec

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

pablo.lemos@mila.quebec

Parker Levesque

Master's Research - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

parker.levesque@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

ronan.legin@mila.quebec

Sacha Perry-Fagant

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

sacha.perry-fagant@mila.quebec

Research Intern - University of Toronto

Co-supervisor :

Laurence Perreault-Levasseur

yung-hsin.lee@mila.quebec

Publications

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 (published)

Searching for Strong Gravitational Lenses

Cameron Lemon

F. 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 (published)

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

Pablo Lemos

Sammy N. Sharief

Nikolay Malkin

Laurence Perreault-Levasseur

2024-02-06

ArXiv (preprint)

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… (see more)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)

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

Yordanka Apostolovski

Manuel Ruiz Aravena

Timo Anguita

Matthieu Bethermin

James R. Burgoyne

Scott C. Chapman

C. Breuck

Anthony R Gonzalez

Max Gronke

Lucia Guaita

R. Hill

S. Jarugula

Evelyn Johnston

M. Malkan

Desika Narayanan

Cassie A. Reuter

Manuel Solimano

Justin Scott Spilker

Nikolaus Sulzenauer … (see 5 more)

J. Vieira

Joaquin Daniel Vieira

David Vizgan

Axel Wei

A. Weiss

Deep spectroscopic surveys with the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed that some of the brightest infrared so… (see more)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 (published)

Improving Gradient-guided Nested Sampling for Posterior Inference

Pablo Lemos

Will Handley

Nikolay Malkin

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

Nicolas Payot

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… (see more)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 (preprint)

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Pablo Lemos

A. Scaife

Laurence Perreault-Levasseur

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-29

ArXiv (preprint)

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors

Alexandre Adam

Connor Stone

Connor Bottrell

Ronan Legin

Laurence Perreault-Levasseur

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (see more) barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.

2023-11-29

ArXiv (preprint)

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

Nicolas Payot

Pablo Lemos

Laurence Perreault-Levasseur

Carolina Cuesta-lazaro

C. Modi

2023-11-29

ArXiv (preprint)

The search for the lost attractor

Mario Pasquato

Syphax Haddad

Pierfrancesco Di Cintio

Alexandre Adam

Pablo Lemos

No'e Dia

Mircea Petrache

Ugo Niccolo Di Carlo

Alessandro A. Trani

Laurence Perreault-Levasseur

2023-11-27

ArXiv (preprint)

Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise

Ronan Legin

Alexandre Adam

Laurence Perreault-Levasseur

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (see more) 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)