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 :

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

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

Alexandra Volokhova

Artificial intelligence (AI) technologies are increasingly used in modern weapons systems. Notably, these systems have recently been involve… (see more)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 (preprint)

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

Dounia Shaaban Kabakibo

Félix Therrien

Michel Côté

Hongyu Guo

Homin Shin

Accurate prediction of ionic conductivity is critical for the design of highperformance solid-state electrolytes in next-generation batterie… (see more)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)

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

Louis Vaillancourt

Yves V. Brun

2026-02-03

arXiv (preprint)

Benchmarking the geographic generalization of deep learning models for precipitation downscaling

Paula Harder

Luca Schmidt

Francis Pelletier

Nicole Ludwig

Matthew Chantry

Christian Lessig

Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolu… (see more)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 (published)

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

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… (see more)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))

Adsorption energies are necessary but not sufficient to identify good catalysts

Alexander Davis

Alexandre AGM Duval

Oleksandr Voznyy

Alex Hern'andez-Garcia

Félix Therrien

2025-12-04

ArXiv (preprint)

Improved Off-policy Reinforcement Learning in Biological Sequence Design

Jinkyoo Park

Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although rei… (see more)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 (published)

proceedings.mlr.press

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

Lena Podina

Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despit… (see more)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)

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

Abdessamad El-Kabid

Loubna Benabbou

Redouane Lguensat

Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanogr… (see more)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 (preprint)

RainShift: A Benchmark for Precipitation Downscaling Across Geographies

Paula Harder

Luca Schmidt

Francis Pelletier

Nicole Ludwig

Matthew Chantry

Christian Lessig

Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolu… (see more)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 (preprint)