Portrait de Yashar Hezaveh

Yashar Hezaveh

Membre académique associé
yashar.hezaveh@mila.quebec
Professeur adjoint, Université de Montréal, Département de physique
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Vision par ordinateur
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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

Publications

Posterior samples of source galaxies in strong gravitational lenses with score-based priors
Alexandre Adam
Adam Coogan
Nikolay Malkin
Ronan Legin
Laurence Perreault-Levasseur
Yashar Hezaveh
Yoshua Bengio
Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge,… (voir plus) in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.
2022-11-06
ArXiv (prépublication)
doi.org
arxiv.org
Machine Learning Advantages in Canadian Astrophysics
Kim Venn
Sébastien Fabbro
Adrian Liu
Yashar Hezaveh
Laurence Perreault-Levasseur
Gwendolyn Eadie
Sara Ellison
Joanna Woo
JJ Kavelaars
Kwang Moo Yi
Renée Hložek
Jo Bovy
Hossen Teimoorinia
Siamak Ravanbakhsh
Locke Spencer
The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing pow… (voir plus)er and complex algorithms become more powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010.
With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers.
2018-12-31
Zenodo (publié)
doi.org
arxiv.org