Portrait of Alex Hernández-García

Alex Hernández-García

Core Academic Member

alex.hernandez-garcia@mila.quebec

Assistant Professor, Université de Montréal

Research Topics

Active Learning

AI and Sustainability

AI for Science

Blackbox Optimization

Climate

Climate Variable Downscaling

Computational Biology

Deep Learning

Drug Discovery

Generative Models

GFlowNets

Molecular Modeling

Probabilistic Models

Representation Learning

Biography

Alex Hernandez-Garcia is an assistant professor at the Université de Montréal, a core academic member at Mila, IVADO professor and member of the Institut Courtois. His machine learning research is motivated by scientific applications to tackle the climate crisis and other societal challenges. In particular, a current focus of his work is active and generative machine learning to facilitate scientific discoveries, such as new materials and antibiotics. He also advocates for a critical examination of the impacts of artificial intelligence, is a strong proponent of open science and is active in initiatives about making science more inclusive, equitable, open, reproducible, transparent and environmentally conscious.

Current Students

Research Intern - Université de Montréal

Master's Research - Université de Montréal

Postdoctorate - Université de Montréal

Co-supervisor :

Pierre-Louis Lemaire

Collaborating researcher - Polytechnique Montréal Montréal

Co-supervisor :

PhD - Concordia University

PhD - Université de Montréal

Master's Research - Université de Montréal

Collaborating Alumni - Université de Montréal

Dounia Shaaban Kabakibo

PhD - Université de Montréal

Principal supervisor :

Postdoctorate

Principal supervisor :

Publications

ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods

Alexandra Luccioni

Mélisande Teng

Sunand Raghupathi

Gautier Cosne

Adrien Juraver

Alex Hernández-García

Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both poli… (see more)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)

Generating physically-consistent high-resolution climate data with hard-constrained neural networks

Venkatesh Ramesh

Prasanna Sattegeri

Alex Hernández-García

Campbell Watson

D. Szwarcman

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (see more)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 (preprint)