David Rolnick

Biographie

David Rolnick est professeur adjoint et titulaire d’une chaire en IA Canada-CIFAR à l'École d'informatique de l'Université McGill et membre académique principal de Mila – Institut québécois d’intelligence artificielle. Ses travaux portent sur les applications de l'apprentissage automatique dans la lutte contre le changement climatique. Il est cofondateur et président de Climate Change AI et codirecteur scientifique de Sustainability in the Digital Age. David Rolnick a obtenu un doctorat en mathématiques appliquées du Massachusetts Institute of Technology (MIT). Il a été chercheur postdoctoral en sciences mathématiques à la National Science Foundation (NSF), chercheur diplômé à la NSF et boursier Fulbright. Il a figuré sur la liste des « 35 innovateurs de moins de 35 ans » de la MIT Technology Review en 2021.

Étudiants actuels

Aditya Aditya

Collaborateur·rice de recherche

Collaborateur·rice alumni - McGill

Collaborateur·rice de recherche - Cambridge University

Co-superviseur⋅e :

Postdoctorat - McGill

Michael Bunsen

Collaborateur·rice de recherche - McGill

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Co-superviseur⋅e :

Yuyan Chen

Doctorat - McGill

Eya Cherif

Collaborateur·rice de recherche - Leipzig University

Othmane Echchabi

Maîtrise recherche - McGill

Collaborateur·rice de recherche

Mohamed Elabbas

Collaborateur·rice de recherche

Jannik Endres

Collaborateur·rice de recherche

Jacopo Ghirri

Visiteur de recherche indépendant - Politecnico di Milano

Paula Harder

Visiteur de recherche indépendant

Collaborateur·rice de recherche - Johannes Kepler University

Christina Isaicu Isaicu

Collaborateur·rice de recherche - University of Amsterdam

Gaurav Iyer

Maîtrise recherche - McGill

Doctorat - McGill

Devin Kwok

Doctorat - McGill

Visiteur de recherche indépendant - Université de Montréal

Pierre-Louis Lemaire

Collaborateur·rice de recherche - Polytechnique Montréal

Superviseur⋅e principal⋅e :

Alex Hernández-García

Joshi Manoj

Collaborateur·rice de recherche - University of East Anglia

David Mickisch

Collaborateur·rice de recherche

Juan Nathaniel Nathaniel

Collaborateur·rice de recherche - Columbia university

Postdoctorat - McGill

Co-superviseur⋅e :

Lena Podina

Collaborateur·rice de recherche - University of Waterloo

Co-superviseur⋅e :

Venkatesh Ramesh

Collaborateur·rice alumni - UdeM

Marlena Reil

Maîtrise recherche - McGill

Carla Roesch

Collaborateur·rice de recherche - Columbia university

Rasha Saha

Maîtrise recherche - McGill

Luca Marie Schmidt

Collaborateur·rice de recherche - University of Tübingen

Visiteur de recherche indépendant - Karlsruhe Institute of Technology

Wietze Suijker

Visiteur de recherche indépendant

Ilija Trajković

Collaborateur·rice de recherche - Karlsruhe Institute of Technology

Doctorat - McGill

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche

Catherine Villeneuve

Doctorat - McGill

Shan Zhao

Collaborateur·rice de recherche - Technical University of Munich

Démocratiser l'accès aux données satellitaires grâce à l'IA

Billets de blogue

diagram illustrating how the AI foundation model for Earth observation, Galileo, works

21 octobre 2025

par

Gabriel Tseng

David Rolnick

Lire l'article

Publications

A landmark environmental law looks ahead

Robert L. Fischman

J. B. Ruhl

Brenna R. Forester

Tanya M. Lama

Marty Kardos

Grethel Aguilar Rojas

Nicholas A. Robinson

Patrick D. Shirey

Gary A. Lamberti

Amy W. Ando

Stephen Palumbi

Michael Wara

Mark W. Schwartz

Matthew A. Williamson

Tanya Berger-Wolf

Sara Beery

Justin Kitzes

David Thau

Devis Tuia … (voir 8 de plus)

Daniel Rubenstein

Caleb R. Hickman

Julie Thorstenson

Gregory E. Kaebnick

James P. Collins

Athmeya Jayaram

Thomas Deleuil

Ying Zhao

In late December 1973, the United States enacted what some would come to call “the pitbull of environmental laws.” In the 50 years since… (voir plus), the formidable regulatory teeth of the Endangered Species Act (ESA) have been credited with considerable successes, obliging agencies to draw upon the best available science to protect species and habitats. Yet human pressures continue to push the planet toward extinctions on a massive scale. With that prospect looming, and with scientific understanding ever changing, Science invited experts to discuss how the ESA has evolved and what its future might hold. —Brad Wible

2023-12-20

Science (publié)

FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring

Nikolaos Ioannis Bountos

Arthur Ouaknine

Nikolaos Ioannis Bountos

Forests are vital to ecosystems, supporting biodiversity and essential services, but are rapidly changing due to land use and climate change… (voir plus). Understanding and mitigating negative effects requires parsing data on forests at global scale from a broad array of sensory modalities, and using them in diverse forest monitoring applications. Such diversity in data and applications can be effectively addressed through the development of a large, pre-trained foundation model that serves as a versatile base for various downstream tasks. However, remote sensing modalities, which are an excellent fit for several forest management tasks, are particularly challenging considering the variation in environmental conditions, object scales, image acquisition modes, spatio-temporal resolutions, etc. With that in mind, we present the first unified Forest Monitoring Benchmark (FoMo-Bench), carefully constructed to evaluate foundation models with such flexibility. FoMo-Bench consists of 15 diverse datasets encompassing satellite, aerial, and inventory data, covering a variety of geographical regions, and including multispectral, red-green-blue, synthetic aperture radar and LiDAR data with various temporal, spatial and spectral resolutions. FoMo-Bench includes multiple types of forest-monitoring tasks, spanning classification, segmentation, and object detection. To enhance task and geographic diversity in FoMo-Bench, we introduce TalloS, a global dataset combining satellite imagery with ground-based annotations for tree species classification across 1,000+ categories and hierarchical taxonomic levels. Finally, we propose FoMo-Net, a pre-training framework to develop foundation models with the capacity to process any combination of commonly used modalities and spectral bands in remote sensing.

2023-12-14

ArXiv (prépublication)

Towards Causal Representations of Climate Model Data

Julien Boussard

Chandni Nagda

Julia Kaltenborn

Charlotte Emilie Elektra Lange

Philippe Brouillard

Yaniv Gurwicz

Peer Nowack

Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic… (voir plus) Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learning-based emulators trained on existing simulation data can project additional climate scenarios much faster and are computationally efficient. However, they often lack generalizability and interpretability. This work delves into the potential of causal representation learning, specifically the \emph{Causal Discovery with Single-parent Decoding} (CDSD) method, which could render climate model emulation efficient \textit{and} interpretable. We evaluate CDSD on multiple climate datasets, focusing on emissions, temperature, and precipitation. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.

2023-12-04

ArXiv (prépublication)

Towards Climate Variable Prediction with Conditioned Spatio-Temporal Normalizing Flows

Christina Winkler

2023-11-11

ArXiv (prépublication)

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Hager Radi Abdelwahed

Hugo Larochelle

2023-11-01

ArXiv (prépublication)

openreview.net

OpenForest: a data catalog for machine learning in forest monitoring

Arthur Ouaknine

Teja Kattenborn

Étienne Laliberté

Forests play a crucial role in Earth's system processes and provide a suite of social and economic ecosystem services, but are significantly… (voir plus) impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation and forest biomass assessments. For this, the significance of open access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalogue open to contributions that strives to reference all available open access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at https://github.com/RolnickLab/OpenForest .

2023-10-31

ArXiv (prépublication)

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

Alvaro Carbonero

Alexandre Duval

Victor Schmidt

Santiago Miret

Alex Hernández-García

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (voir plus)ate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted.

2023-10-26

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

openreview.net

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning

Julia Kaltenborn

Charlotte Emilie Elektra Lange

Venkatesh Ramesh

Philippe Brouillard

Yaniv Gurwicz

Chandni Nagda

Jakob Runge

Peer Nowack

Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) c… (voir plus)ommunity has taken an increased interest in supporting climate scientists' efforts on various tasks such as climate model emulation, downscaling, and prediction tasks. Many of those tasks have been addressed on datasets created with single climate models. However, both the climate science and ML communities have suggested that to address those tasks at scale, we need large, consistent, and ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset containing the inputs and outputs of 36 climate models from the Input4MIPs and CMIP6 archives. In addition, we provide a modular dataset pipeline for retrieving and preprocessing additional climate models and scenarios. We showcase the potential of our dataset by using it as a benchmark for ML-based climate model emulation. We gain new insights about the performance and generalization capabilities of the different ML models by analyzing their performance across different climate models. Furthermore, the dataset can be used to train an ML emulator on several climate models instead of just one. Such a "super emulator" can quickly project new climate change scenarios, complementing existing scenarios already provided to policymakers. We believe ClimateSet will create the basis needed for the ML community to tackle climate-related tasks at scale.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

openreview.net

Multi-variable Hard Physical Constraints for Climate Model Downscaling

Jose Gonz'alez-Abad

'Alex Hern'andez-Garc'ia

Paula Harder

Jos'e Manuel Guti'errez

2023-08-01

ArXiv (prépublication)

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

Alexandre Duval

Victor Schmidt

Alex Hernandez Garcia

Santiago Miret

Fragkiskos D. Malliaros

Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (voir plus)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

Hidden Symmetries of ReLU Networks

J. Elisenda Grigsby

Kathryn Lindsey

The parameter space for any fixed architecture of feedforward ReLU neural networks serves as a proxy during training for the associated clas… (voir plus)s of functions - but how faithful is this representation? It is known that many different parameter settings can determine the same function. Moreover, the degree of this redundancy is inhomogeneous: for some networks, the only symmetries are permutation of neurons in a layer and positive scaling of parameters at a neuron, while other networks admit additional hidden symmetries. In this work, we prove that, for any network architecture where no layer is narrower than the input, there exist parameter settings with no hidden symmetries. We also describe a number of mechanisms through which hidden symmetries can arise, and empirically approximate the functional dimension of different network architectures at initialization. These experiments indicate that the probability that a network has no hidden symmetries decreases towards 0 as depth increases, while increasing towards 1 as width and input dimension increase.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

Maximal Initial Learning Rates in Deep ReLU Networks

Gaurav Iyer

Boris Hanin

Training a neural network requires choosing a suitable learning rate, which involves a trade-off between speed and effectiveness of converge… (voir plus)nce. While there has been considerable theoretical and empirical analysis of how large the learning rate can be, most prior work focuses only on late-stage training. In this work, we introduce the maximal initial learning rate

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)