Portrait of Yashar Hezaveh

Yashar Hezaveh

Associate Academic Member

yashar.hezaveh@mila.quebec

Assistant Professor, Université de Montréal, Department of Physics

Research Topics

Computer Vision

Deep Learning

Representation Learning

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

missael-gabriel.barco@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

michael.barth@mila.quebec

Master's Research - McGill University

dhvani.doshi@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

andreas.filipp@umontreal.ca

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

ronan.legin@mila.quebec

Master's Research - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

nicolas.payot@mila.quebec

Sacha Perry-Fagant

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

sacha.perry-fagant@mila.quebec

Postdoctorate - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

nolan.smyth@mila.quebec

Postdoctorate - McGill University

Principal supervisor :

Laurence Perreault-Levasseur

yitian.sun@mila.quebec

Postdoctorate - Université de Montréal

Co-supervisor :

Pierre-Luc Bacon

wassim.tenachi@mila.quebec

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

justine.zeghal@mila.quebec

Publications

POSTELLAR: Posterior Stellar Spectrum Sampling—An Alternative to Approximate Stellar Spectra for Exoplanetary Analysis

Nicolas B. Cowan

Gabriel Missael Barco

Étienne Artigau

Abstract We present a novel approach to perform posterior sampling of the underlying stellar spectrum in high-resolution spectroscopic obser… (see more)vations. Our method, postellar , coherently combines information from empirical observations and physics-based models, enabling more accurate spectral recovery while providing uncertainties that can be propagated into downstream analyses. This is accomplished by treating the intrinsic stellar spectrum as a latent variable and performing posterior sampling under a Gaussian likelihood with an informative prior constructed using a score-based diffusion model trained on PHOENIX stellar models. We validate the framework on synthetic SPectropolarimètre InfraRouge (SPIRou) radial velocity (RV) observations generated from the empirical spectra of Barnard’s Star and Proxima Centauri. Spectra inferred with postellar recover the ground truth more accurately than standard empirical templates. For medium signal-to-noise observations, postellar improves RV accuracy by up to a factor of three, while RVs derived from empirical templates tend to be biased. This method performs particularly well in low signal-to-noise and low-cadence regimes. The postellar framework is broadly applicable to other high-resolution spectroscopy science cases, including stellar abundance analyses and exoplanet atmospheric characterization.

2026-09-13

Astrophysical Journal (published)

Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit

Laurence Perreault Levasseur

Pierre-Luc Bacon

Tabular foundation models (TFMs) learn to fill in tables the way language models fill in text, and tables are arguably the format in which m… (see more)ost physical measurement arrives. Did they learn any physics in the process? They are Bayesian by construction, so the question is what their prior contains. We probe it directly, evaluating four of them (TabPFN-3, TabICLv2, TabDPT and Real-TabPFN-2.5) against six baselines on datasets sampled from 316 physical equations, in and out of domain. TFMs dominate, out of the box and after tuning. But we show that their prior can represent neither a noiseless mechanism nor physical units, which is why they interpolate physics without yet being able to act as physical models.

2026-09-01

arXiv (preprint)

Microlensing Detection and Inference via Learned Bayes Factors

Laurence Perreault-Levasseur

We present a unified framework for gravitational microlensing event detection and parameter inference. Traditional pipelines use determinist… (see more)ic hard cuts on photometric statistics, systematically missing low-magnification events in the finite-source regime. We instead frame detection as Bayesian model comparison using Evidence Networks, which learn calibrated Bayes factors from binary-labeled simulations, and combine this with Neural Posterior Estimation (NPE) for amortized parameter inference. Both share a transformer encoder that handles irregularly-sampled time series without imputation. On simulated Roman Space Telescope data, our Evidence Network achieves

2026-05-29

AI4Physics @ International Conference on Machine Learning (published)

Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines

Guillaume Payeur

Laurence Perreault-Levasseur

Gabriel Missael Barco

Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground … (see more)galaxy is computationally challenging, particularly for high-resolution, high signal-to-noise observations. In this regime, high-dimensional representations of both the source and the foreground mass distribution are necessary to model the data down to the noise level. This inference problem has been challenging for both traditional and machine learning-based methods because of its high dimensionality and its non-linearity in the foreground mass distribution. We present a method to generate joint posterior samples of the source galaxy and foreground mass distribution as pixelated images conditioned on observations. The method combines diffusion-based generative modeling and recurrent inference machines. It can model realistic gravitational lensing simulations with background and foreground galaxies drawn from cosmological hydrodynamical simulations down to the noise level.

2026-05-29

AI4Physics @ International Conference on Machine Learning (published)

MIRA: A Score for Conditional Distribution Accuracy and Model Comparison

Sammy Sharief

Gabriel Missael Barco

Laurence Perreault-Levasseur

We present Mira, a method for estimating the expected probability that samples from a candidate conditional distribution match the true, unk… (see more)nown conditional distribution, for which only data-label pairs are available. We derive theoretical bounds obtained when the candidate distribution matches the true one and when the conditional distributions are independent. This framework thus enables model comparison by quantifying the alignment between the conditional distribution of a candidate model and the data-label pairs of the true model. Consequently, Mira enables Bayesian model comparison through direct posterior validation, bypassing the challenging evidence computation. We demonstrate its effectiveness across several toy problems and Bayesian inference tasks.

2026-04-29

International Conference on Machine Learning (spotlight)

LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators

Laurence Perreault-Levasseur

Daniel Gilman

LSST Dark Energy Science Collaboration

Strong gravitational lensing offers a unique probe of dark matter (DM) on sub-galactic scales, where the abundance and distribution of low-m… (see more)ass halos are highly sensitive to the underlying properties of DM particles. In this work, we forecast LSST's sensitivity to DM substructure in galaxy-galaxy strong lenses using simulated samples and neural ratio estimators (NREs). Our simulations include both subhalos within the main deflector and line-of-sight (LOS) halos, with halo masses down to

2026-04-07

arXiv (preprint)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

M. J. Yantovski-Barth

Landung Setiawan

Cordero Core

Charles Wilson

Gabriel Missael Barco

Laurence Perreault-Levasseur

2025-12-31

Zenodo (published)

Transformer Embeddings for Fast Microlensing Inference

Laurence Perreault-Levasseur

2025-12-11

ArXiv (preprint)

Neural Deprojection of Galaxy Stellar Mass Profiles

M. J. Yantovski-Barth

Hengyue Zhang

Martin Bureau

Laurence Perreault-Levasseur

We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable… (see more) mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.

2025-11-24

arXiv (preprint)

Mind the Information Gap: Unveiling Detailed Morphologies of z 0.5-1.0 Galaxies with SLACS Strong Lenses and Data-Driven Analysis

Gabriel Missael Barco

Nikolay Malkin

Laurence Perreault-Levasseur

2025-11-23

ArXiv (preprint)

Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images

Nikolay Malkin

Gabriel Missael Barco

Laurence Perreault-Levasseur

We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF charact… (see more)erization is critical for precision measurements in many fields including: weak lensing, astrometry, and photometry. Our method defines the posterior distribution of the pixelized PSF model through the combination of an analytic Gaussian likelihood and a highly expressive generative diffusion model prior, trained on a library of HST ePSF templates. Compared to traditional methods (parametric Moffat, ePSF template-based, and regularized likelihood), we demonstrate that our PSF models achieve orders of magnitude higher likelihood and residuals consistent with noise, all while remaining visually realistic. Further, the method applies even for faint and heavily masked point sources, merely producing a broader posterior. By recovering a realistic, pixel-level posterior distribution, our technique enables the first meaningful propagation of detailed PSF morphological uncertainty in downstream analysis. An implementation of our posterior sampling procedure is available on GitHub.

2025-11-23

ArXiv (preprint)

Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models

Gabriel Missael Barco

Laurence Perreault-Levasseur

2025-11-05

ArXiv (preprint)