Alex Hernández-García

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

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

Sanad Abu Baker

Stagiaire de recherche - UdeM

Tim Arni

Stagiaire de recherche - UdeM

Github

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

Oriol Masdéu

Visiteur de recherche indépendant - Universitat Politècnica de Catalunya

Ameer Nizami

Doctorat - Concordia

Github

Google Scholar

Ola Onabola

Doctorat - UdeM

Maîtrise recherche - UdeM

Github

Luca Scimeca

Collaborateur·rice alumni - UdeM

Site web

Github

Google Scholar

Dounia Shaaban Kabakibo

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat

Superviseur⋅e principal⋅e :

Yoshua Bengio

Site web

Github

Publications

The implicated scientist: on the role of AI researchers in the development of weapons systems

Alexandra Volokhova

Alex Hernández-García

Artificial intelligence (AI) technologies are increasingly used in modern weapons systems. Notably, these systems have recently been involve… (voir plus)d in mass killings and destruction at scale. Furthermore, there is currently a strong interest and competition among powerful players to accelerate the proliferation of weapons with automated or AI-based components, a phenomenon known as AI arms race. This competition poses a risk of causing even more deaths and devastation in the future, as well as increased power and wealth inequality. In this work, we aim to shed light on the role of AI researchers as implicated subjects in the harms caused by weapons enabled by AI technologies. We investigate and discuss the specifics of this implication and explore ways to transfigure this position of implication into one of differentiated, long-distance solidarity with the victims of technologically fortified injustices.

2026-04-19

arXiv (prépublication)

doi.org

arxiv.org

A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes

Dounia Shaaban Kabakibo

Félix Therrien

Yoshua Bengio

Michel Côté

Hongyu Guo

Homin Shin

Alex Hernández-García

Accurate prediction of ionic conductivity is critical for the design of highperformance solid-state electrolytes in next-generation batterie… (voir plus)s. We benchmark molecular dynamics (MD) approaches for computing ionic conductivity in 21 lithium solid electrolytes for which experimental ionic conductivity has been previously reported in the literature. Specifically, we compare simulations driven by density functional theory (DFT) and by universal machine-learning interatomic potentials (uMLIPs), namely a MACE foundation model. Our results suggest comparable performance between DFT and MACE, with MACE requiring only a fraction of the computational cost. The framework developed here is designed to enable systematic comparisons with additional uMLIPs and fine-tuned models in future work.

2026-03-01

AI4Mat @ International Conference on Learning Representations (poster)

openreview.net

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors

Louis Vaillancourt

Yves V. Brun

Yoshua Bengio

Alex Hernández-García

2026-02-03

arXiv (prépublication)

doi.org

openreview.net

Benchmarking the geographic generalization of deep learning models for precipitation downscaling

Paula Harder

Luca Schmidt

Francis Pelletier

Nicole Ludwig

Matthew Chantry

Christian Lessig

Alex Hernández-García

David Rolnick

Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolu… (voir plus)tion for local-scale risk-assessments is not computationally feasible. Deep learning-based super-resolution models offer a promising solution to downscale ESM outputs to higher resolutions by learning from data. Yet, due to regional variations in climatic processes, these models typically require retraining for each geographical area–demanding high-resolution observational data, which is unevenly available across the globe. This highlights the need to assess how well these models generalize across geographic regions. To address this, we introduce RainShift, a dataset and benchmark for evaluating downscaling under geographic distribution shifts. We evaluate state-of-the-art downscaling approaches including GANs and diffusion models in generalizing across data gaps between the Global North and Global South. Our findings reveal substantial performance drops in out-of-distribution regions, depending on model and geographic area. While expanding the training domain generally improves generalization, it is insufficient to overcome shifts between geographically distinct regions. We show that addressing these shifts through, for example, domain adaptation can improve spatial generalization. Our work advances the global applicability of downscaling methods and represents a step toward reducing inequities in access to high-resolution climate information.

2026-01-26

Scientific Reports (publié)

doi.org

Position: Irresponsible AI: big tech’s influence on AI research and associated impacts

Alex Hernández-García

Alexandra Volokhova

Ezekiel Williams

Dounia Kabakibo

Mélisande Teng

The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big t… (voir plus)ech in the AI field. This trend has been accompanied by growing ethical concerns and intensified societal and environmental impacts. This position paper argues that irresponsible AI development is strongly driven by big tech's influence and involvement in the field. We develop this argument by laying out the factors through which this influence leads to irresponsible AI. First, we examine the growing and disproportionate influence of big tech in AI research and argue that its drive for scaling and general-purpose systems is fundamentally at odds with the responsible, ethical, and sustainable development of AI. Second, we review key current environmental and societal negative impacts of AI and trace their connections to big tech's influence. Third, we discuss the underlying economic forces driving big tech's actions. Finally, as a call to action, we highlight the need for AI researchers to counter big tech's influence, and review and propose strategies that build on the responsibility of implicated actors and collective action.

2025-12-31

International Conference on Machine Learning (Accept (spotlight))

openreview.net

Adsorption energies are necessary but not sufficient to identify good catalysts

Alexander Davis

Alexandre AGM Duval

Oleksandr Voznyy

Alex Hern'andez-Garcia

David Rolnick

Félix Therrien

2025-12-04

ArXiv (prépublication)

arxiv.org

Improved Off-policy Reinforcement Learning in Biological Sequence Design

Alex Hernández-García

Jinkyoo Park

Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although rei… (voir plus)nforcement learning methods use proxy models for rapid reward evaluation, insufficient training data can cause proxy misspecification on out-of-distribution inputs. To address this, we propose a novel off-policy search,

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (publié)

doi.org

proceedings.mlr.press

Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study

Lena Podina

Alex Hernández-García

Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despit… (voir plus)e fluctuations in sources such as wind and solar. Electrocatalysts play a key role in hydrogen energy storage (HES), allowing the energy to be stored as hydrogen. However, the development of affordable and high-performance catalysts for this process remains a significant challenge. We introduce Catalyst GFlowNet, a generative model that leverages machine learning-based predictors of formation and adsorption energy to design crystal surfaces that act as efficient catalysts. We demonstrate the performance of the model through a proof-of-concept application to the hydrogen evolution reaction, a key reaction in HES, for which we successfully identified platinum as the most efficient known catalyst. In future work, we aim to extend this approach to the oxygen evolution reaction, where current optimal catalysts are expensive metal oxides, and open the search space to discover new materials. This generative modeling framework offers a promising pathway for accelerating the search for novel and efficient catalysts.

2025-09-19

AI4Mat @ Neural Information Processing Systems (poster)

doi.org

openreview.net

Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents

Abdessamad El-Kabid

Loubna Benabbou

Redouane Lguensat

Alex Hernández-García

Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanogr… (voir plus)aphy, high-resolution current data are critical for coastal management, environmental monitoring, and maritime safety. However, available satellite products, such as Copernicus data for sea water velocity at ~0.08 degrees spatial resolution and global ocean models, often lack the spatial granularity required for detailed local analyses. In this work, we (a) introduce a supervised deep learning framework based on neural operators for solving PDEs and providing arbitrary resolution solutions, and (b) propose downscaling models with an application to Copernicus ocean current data. Additionally, our method can model surrogate PDEs and predict solutions at arbitrary resolution, regardless of the input resolution. We evaluated our model on real-world Copernicus ocean current data and synthetic Navier-Stokes simulation datasets.

2025-07-23

ArXiv (prépublication)

doi.org

arxiv.org

RainShift: A Benchmark for Precipitation Downscaling Across Geographies

Paula Harder

Luca Schmidt

Francis Pelletier

Nicole Ludwig

Matthew Chantry

Christian Lessig

Alex Hernández-García

David Rolnick

Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolu… (voir plus)tion for local-scale risk-assessments is not computationally feasible. Deep learning-based super-resolution models offer a promising solution to downscale ESM outputs to higher resolutions by learning from data. Yet, due to regional variations in climatic processes, these models typically require retraining for each geographical area-demanding high-resolution observational data, which is unevenly available across the globe. This highlights the need to assess how well these models generalize across geographic regions. To address this, we introduce RainShift, a dataset and benchmark for evaluating downscaling under geographic distribution shifts. We evaluate state-of-the-art downscaling approaches including GANs and diffusion models in generalizing across data gaps between the Global North and Global South. Our findings reveal substantial performance drops in out-of-distribution regions, depending on model and geographic area. While expanding the training domain generally improves generalization, it is insufficient to overcome shifts between geographically distinct regions. We show that addressing these shifts through, for example, data alignment can improve spatial generalization. Our work advances the global applicability of downscaling methods and represents a step toward reducing inequities in access to high-resolution climate information.

2025-07-06

ArXiv (prépublication)

doi.org

arxiv.org

Torsional-GFN: a conditional conformation generator for small molecules

Lena Nehale Ezzine