Portrait de Alex Hernandez-Garcia

Alex Hernandez-Garcia

Membre académique principal
alex.hernandez-garcia@mila.quebec
Professeur adjoint, Université de Montréal
Sujets de recherche
Apprentissage actif
Apprentissage de représentations
Apprentissage profond
Biologie computationnelle
Climat
Découverte de médicaments
GFlowNets
IA et durabilité
IA pour la science
Modèles génératifs
Modèles probabilistes
Modélisation moléculaire
Optimisation en boîte noire
Réduction d'échelle des variables climatiques
Site web
Google Scholar
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Biographie

Alex Hernandez-Garcia est professeur adjoint à l’Université de Montréal, membre académique principal de Mila, professeur IVADO et membre de l’Institut Courtois. Ses recherches en apprentissage automatique sont motivées par des applications scientifiques visant à relever la crise climatique et d’autres défis sociétaux. Un axe actuel de ses travaux porte en particulier sur l’apprentissage automatique actif et génératif afin de faciliter les découvertes scientifiques, telles que de nouveaux matériaux et antibiotiques. Il plaide également pour un examen critique des impacts de l’intelligence artificielle, est un fervent défenseur de la science ouverte et participe activement à des initiatives visant à rendre la science plus inclusive, équitable, ouverte, reproductible, transparente et respectueuse de l’environnement.

Étudiants actuels

Tim Arni
Stagiaire de recherche - UdeM
Github
LinkedIn
Dionessa Biton
Maîtrise recherche - UdeM
Github
Google Scholar
Hyeonah Kim
Postdoctorat - UdeM
Co-superviseur⋅e :
Yoshua Bengio
Pierre-Louis Lemaire
Collaborateur·rice de recherche - Polytechnique Montréal
Co-superviseur⋅e :
David Rolnick
Site web
Ameer Nizami
Doctorat - Concordia
Github
Google Scholar
Ola Onabola
Doctorat - UdeM
Site web
Github
LinkedIn
Google Scholar
Celine ROGET
Maîtrise recherche - UdeM
Github
Luca Scimeca
Collaborateur·rice alumni - UdeM
Site web
Github
Google Scholar
Dounia Shaaban Kabakibo
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Michel Côté
Github
Google Scholar
Divya Sharma
Postdoctorat
Superviseur⋅e principal⋅e :
Yoshua Bengio
Site web
Github

Publications

ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Victor Schmidt
Alexandra Luccioni
Mélisande Teng
Tianyu Zhang
Alexia Reynaud
Sunand Raghupathi
Gautier Cosne
Adrien Juraver
Vahe Vardanyan
Alex Hernandez-Garcia
Yoshua Bengio
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both poli… (voir plus)cy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the potential consequences of extreme climate events such as flooding in familiar places can help make the abstract impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website that projects extreme climate events onto user-chosen photos, we present our solution to simulate photo-realistic floods on authentic images. To address this complex task in the absence of suitable training data, we propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding.
2022-01-27
ICLR.cc/2022/Conference (poster)
doi.org
openreview.net
Generating physically-consistent high-resolution climate data with hard-constrained neural networks
Paula Harder
Qidong Yang
Venkatesh Ramesh
Prasanna Sattegeri
Alex Hernandez-Garcia
Campbell Watson
D. Szwarcman
David Rolnick
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (voir plus)mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often can only make coarse resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often us-ing image super-resolution methods from computer vision. Despite achieving visually compelling results in some cases, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: A renor-malization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.
2021-12-31
arXiv.org (prépublication)
doi.org