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

Benjamin Akera Binen

Collaborateur·rice alumni - McGill

Collaborateur·rice de recherche - Cambridge University

Co-superviseur⋅e :

julien.boussard@mila.quebec

berkes.anais@mila.quebec

Postdoctorat - McGill

Michael Bunsen

Collaborateur·rice de recherche - McGill

michael.bunsen@mila.quebec

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Co-superviseur⋅e :

shahana.chatterjee@mila.quebec

Yuyan Chen

Doctorat - McGill

yuyan.chen@mila.quebec

Graham Clyne

Postdoctorat - McGill

graham.clyne@mila.quebec

Ruth Crasto

Doctorat - McGill

ruth.crasto@mila.quebec

othmane.echchabi@mila.quebec

Othmane Echchabi

Maîtrise recherche - McGill

Collaborateur·rice de recherche

amna.elmustafa@mila.quebec

mohamed.elabbas@mila.quebec

Mohamed Elabbas

Collaborateur·rice de recherche

jannik.endres@mila.quebec

Jannik Endres

Collaborateur·rice de recherche

jacopo.ghirri@mila.quebec

Jacopo Ghirri

Visiteur de recherche indépendant - Politecnico di Milano

Visiteur de recherche indépendant

paula.harder@mila.quebec

Collaborateur·rice de recherche - Johannes Kepler University

christina.humer@mila.quebec

Gaurav Iyer

Maîtrise recherche - McGill

gaurav.iyer@mila.quebec

julia.kaltenborn@mila.quebec

Julia Kaltenborn

Doctorat - McGill

vincent.lefalher@mila.quebec

Devin Kwok

Doctorat - McGill

devin.kwok@mila.quebec

Visiteur de recherche indépendant - Université de Montréal

Pierre-Louis Lemaire

Collaborateur·rice de recherche - Polytechnique Montréal

Superviseur⋅e principal⋅e :

Alex Hernández-García

pierre-louis.lemaire@mila.quebec

david.mickisch@mila.quebec

David Mickisch

Collaborateur·rice de recherche

Postdoctorat - McGill

Co-superviseur⋅e :

Étienne Laliberté

arthur.ouaknine@mila.quebec

Lena Podina

Collaborateur·rice de recherche - University of Waterloo

Co-superviseur⋅e :

elena.podina@mila.quebec

Marlena Reil

Maîtrise recherche - McGill

marlena.reil@mila.quebec

Rasha Saha

Maîtrise recherche - McGill

sulagna.saha@mila.quebec

luca.schmidt@uni-tuebingen.de

Luca Marie Schmidt

Collaborateur·rice de recherche - University of Tübingen

Visiteur de recherche indépendant - Karlsruhe Institute of Technology

hossein.shirali@mila.quebec

Wietze Suijker

Visiteur de recherche indépendant

wietze.suijker@gmail.com

ilija.trajkovic@mila.quebec

Ilija Trajković

Collaborateur·rice de recherche - Karlsruhe Institute of Technology

gabriel.tseng@mila.quebec

Gabriel Tseng

Doctorat - McGill

Donna Vakalis

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

donna.vakalis@mila.quebec

Collaborateur·rice de recherche

anna.viklund@mila.quebec

catherine.villeneuve@mila.quebec

Catherine Villeneuve

Doctorat - McGill

mohamed.yebari@mila.quebec

Mohamed Yebari

Collaborateur·rice de recherche - Ecole Polytechnique Fédérale de Lausanne (EPFL)

Co-superviseur⋅e :

Loubna Benabbou

Shan Zhao

Collaborateur·rice de recherche - Technical University of Munich

zhaoshanuestc@gmail.com

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

Predicting Species Occurrence Patterns from Partial Observations

Hager Radi Abdelwahed

Mélisande Teng

To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are chang… (voir plus)ing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.

2024-03-25

arXiv (prépublication)

Tackling Climate Change with Machine Learning: Fostering the Maturity of ML Applications for Climate Change

Shiva Madadkhani

Olivia Mendivil Ramos

Millie Chapman

Jesse Dunietz

Arthur Ouaknine

2024-03-07

ICLR.cc/2024/Workshop_Proposals (publié)

Multi-variable Hard Physical Constraints for Climate Model Downscaling

Jose González-Abad

Alex Hernández-García

Paula Harder

José Manuel Gutiérrez

Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they ofte… (voir plus)n operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Statistical downscaling methods leveraging deep learning offer a solution to this problem by approximating local-scale climate fields from coarse variables, thus enabling regional GCM projections. Typically, climate fields of different variables of interest are downscaled independently, resulting in violations of fundamental physical properties across interconnected variables. This study investigates the scope of this problem and, through an application on temperature, lays the foundation for a framework introducing multi-variable hard constraints that guarantees physical relationships between groups of downscaled climate variables.

2024-01-21

AAAI Symposium Series (publié)

Gintare Karolina Dziugaite

Dataset Difficulty and the Role of Inductive Bias

Devin Kwok

Nikhil Anand

Jonathan Frankle

Motivated by the goals of dataset pruning and defect identification, a growing body of methods have been developed to score individual examp… (voir plus)les within a dataset. These methods, which we call"example difficulty scores", are typically used to rank or categorize examples, but the consistency of rankings between different training runs, scoring methods, and model architectures is generally unknown. To determine how example rankings vary due to these random and controlled effects, we systematically compare different formulations of scores over a range of runs and model architectures. We find that scores largely share the following traits: they are noisy over individual runs of a model, strongly correlated with a single notion of difficulty, and reveal examples that range from being highly sensitive to insensitive to the inductive biases of certain model architectures. Drawing from statistical genetics, we develop a simple method for fingerprinting model architectures using a few sensitive examples. These findings guide practitioners in maximizing the consistency of their scores (e.g. by choosing appropriate scoring methods, number of runs, and subsets of examples), and establishes comprehensive baselines for evaluating scores in the future.

2024-01-02

arXiv (prépublication)

Application-Driven Innovation in Machine Learning

Alán Aspuru-Guzik

Sara Beery

Bistra Dilkina

Priya L. Donti

Marzyeh Ghassemi

Hannah Kerner

Claire Monteleoni

Esther Rolf

Milind Tambe

Adam White

As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly i… (voir plus)mportant. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.

2023-12-31

ICML (publié)

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang

Alex Hernández-García

Paula Harder

Venkatesh Ramesh

Prasanna Sattegeri

Daniela Szwarcman

Campbell D. Watson

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (voir plus)ly expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.

2023-12-31

Journal of Machine Learning Research (publié)

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)

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.

2023-12-11

Neural Information Processing Systems (Accept (Poster))

Normalization Layers Are All That Sharpness-Aware Minimization Needs

Maximilian Müller

Tiffany Vlaar

Matthias Hein

Sharpness-aware minimization (SAM) was proposed to reduce sharpness of minima and has been shown to enhance generalization performance in va… (voir plus)rious settings. In this work we show that perturbing only the affine normalization parameters (typically comprising 0.1% of the total parameters) in the adversarial step of SAM can outperform perturbing all of the parameters.This finding generalizes to different SAM variants and both ResNet (Batch Normalization) and Vision Transformer (Layer Normalization) architectures. We consider alternative sparse perturbation approaches and find that these do not achieve similar performance enhancement at such extreme sparsity levels, showing that this behaviour is unique to the normalization layers. Although our findings reaffirm the effectiveness of SAM in improving generalization performance, they cast doubt on whether this is solely caused by reduced sharpness.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

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

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Hager Radi Abdelwahed

Hugo Larochelle

2023-12-11

Neural Information Processing Systems (Accept (Poster))

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)