Portrait of Yashar Hezaveh

Yashar Hezaveh

Associate Academic Member

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

eirini angeloudi

Research Intern - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Master's Research - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Master's Research - McGill University

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

andreas.filipp@umontreal.ca

PhD - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Parker Levesque

Master's Research - Université de Montréal

Co-supervisor :

Pierre-Luc Bacon

Master's Research - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Sacha Perry-Fagant

PhD - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Co-supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Master's Research - McGill University

Postdoctorate - McGill University

Principal supervisor :

Laurence Perreault-Levasseur

Postdoctorate - Université de Montréal

Co-supervisor :

Pierre-Luc Bacon

Postdoctorate - Université de Montréal

Principal supervisor :

Laurence Perreault-Levasseur

Publications

IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Laurence Perreault-Levasseur

Anna M. M. Scaife

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (see more)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.

2025-01-05

ArXiv (preprint)

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

Laurence Perreault-Levasseur

2024-11-22

Journal of Open Source Software (published)

Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing

Andreas Filipp

Laurence Perreault-Levasseur

2024-11-08

ArXiv (preprint)

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Andrea Maccio

2024-10-30

NeurIPS.cc/2024/Workshop/CRL (poster)

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization

Ronan Legin

Maximiliano Isi

Kaze W. K. Wong

Laurence Perreault-Levasseur

2024-10-25

ArXiv (preprint)

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis

Zehao Jin

Mario Pasquato

Benjamin L. Davis

A. Macciò

2024-10-18

ArXiv (preprint)

Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Tristan Deleu

Yu Luo

Changhyun Cho

Pablo Lemos

Laurence Perreault-Levasseur

Xi Kang

Andrea Maccio

Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure… (see more) (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)--galaxy interaction has long been constrained by observed scaling relations, that is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date dataset, reveals a causal link between galaxy properties and dynamically-measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.

2024-10-01

ArXiv (preprint)

A Data-driven Discovery of the Causal Connection between Galaxy and Black Hole Evolution

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Tristan Deleu

Yu Luo

Changhyun Cho

Pablo Lemos

Laurence Perreault-Levasseur

Xi Kang

Andrea Maccio

2024-10-01

ArXiv (preprint)

Strong gravitational lensing as a probe of dark matter

Simona Vegetti

Simon Birrer

Giulia Despali

C. Fassnacht

Daniel A. Gilman

L.

Laurence Perreault-Levasseur

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… (see more) 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 (published)

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems

Gabriel Missael Barco

Alexandre Adam

Connor Stone

Laurence Perreault-Levasseur

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-… (see more)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.

2024-07-24

ArXiv (preprint)

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… (see more) on light curves of RRLyrae type variables, in the Galactic globular cluster

2024-07-23

ArXiv (preprint)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Pablo Lemos

Nikolay Malkin

Will Handley

Laurence Perreault-Levasseur

We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable … (see more)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 (published)