Alex Hernández-García

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

Sanad Abu Baker

Research Intern - Université de Montréal

Tim Arni

Research Intern - Université de Montréal

Dionessa Biton

Master's Research - Université de Montréal

Hyeonah Kim

Postdoctorate - Université de Montréal

Co-supervisor :

Pierre-Louis Lemaire

Collaborating researcher - Polytechnique Montréal Montréal

Co-supervisor :

David Rolnick

Oriol Masdéu

Independent visiting researcher - Universitat Politècnica de Catalunya

Ameer Nizami

PhD - Concordia University

Ola Onabola

PhD - Université de Montréal

Master's Research - Université de Montréal

Luca Scimeca

Collaborating Alumni - Université de Montréal

Dounia Shaaban Kabakibo

PhD - Université de Montréal

Principal supervisor :

Postdoctorate

Principal supervisor :

Publications

Hard-Constrained Deep Learning for Climate Downscaling

Paula Harder

Venkatesh Ramesh

Qidong Yang

Prasanna Sattegeri

D. Szwarcman

Campbell Watson

David Rolnick

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 generate coarse-resolution predictions. Statistical downscaling, including super-resolution methods from deep learning, can provide an efficient method of upsampling low-resolution data. However, despite achieving visually compelling results in some cases, such models frequently violate conservation laws when predicting physical variables. In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics. We compare different constraining approaches and demonstrate their applicability across different neural architectures as well as a variety of climate and weather data sets. Besides enabling faster and more accurate climate predictions through downscaling, we also show that our novel methodologies can improve super-resolution for satellite data and natural images data sets.

2022-12-31

Journal of Machine Learning Research (published)

arxiv.org

A Theory of Continuous Generative Flow Networks

Lena Nehale Ezzine

Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (see more)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.

2022-12-31

ICML (published)

proceedings.mlr.press

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

Siba Moussa

Michael Kilgour

Clara Jans

Miroslava Cuperlovic‐culf

Lena Simine

2022-08-09

ArXiv (preprint)

arxiv.org

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

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)

openreview.net

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

Paula Harder

Qidong Yang

Venkatesh Ramesh

Prasanna Sattegeri

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 … (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)