David Rolnick

Biography

David Rolnick is an assistant professor at McGill University’s School of Computer Science, a core academic member of Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair. Rolnick’s work focuses on applications of machine learning to help address climate change. He is the co-founder and chair of Climate Change AI, and scientific co-director of Sustainability in the Digital Age. After completing his PhD in applied mathematics at the Massachusetts Institute of Technology (MIT), he was a NSF Mathematical Sciences Postdoctoral Research Fellow, an NSF Graduate Research Fellow and a Fulbright Scholar. He was named to MIT Technology Review’s “35 Innovators Under 35” in 2021.

Current Students

Benjamin Akera Binen

Collaborating Alumni - McGill University

Collaborating researcher - Cambridge University

Co-supervisor :

julien.boussard@mila.quebec

berkes.anais@mila.quebec

Postdoctorate - McGill University

Michael Bunsen

Collaborating researcher - McGill University

michael.bunsen@mila.quebec

Shahana Chatterjee

Collaborating researcher - N/A

Co-supervisor :

shahana.chatterjee@mila.quebec

Yuyan Chen

PhD - McGill University

yuyan.chen@mila.quebec

Graham Clyne

Postdoctorate - McGill University

graham.clyne@mila.quebec

Ruth Crasto

PhD - McGill University

ruth.crasto@mila.quebec

othmane.echchabi@mila.quebec

Othmane Echchabi

Master's Research - McGill University

Collaborating researcher

amna.elmustafa@mila.quebec

mohamed.elabbas@mila.quebec

Mohamed Elabbas

Collaborating researcher

jannik.endres@mila.quebec

Jannik Endres

Collaborating researcher

jacopo.ghirri@mila.quebec

Jacopo Ghirri

Independent visiting researcher - Politecnico di Milano

Independent visiting researcher

paula.harder@mila.quebec

Collaborating researcher - Johannes Kepler University

christina.humer@mila.quebec

Gaurav Iyer

Master's Research - McGill University

gaurav.iyer@mila.quebec

julia.kaltenborn@mila.quebec

Julia Kaltenborn

PhD - McGill University

vincent.lefalher@mila.quebec

Devin Kwok

PhD - McGill University

devin.kwok@mila.quebec

Independent visiting researcher - Université de Montréal

Pierre-Louis Lemaire

Collaborating researcher - Polytechnique Montréal Montréal

Principal supervisor :

Alex Hernández-García

pierre-louis.lemaire@mila.quebec

david.mickisch@mila.quebec

David Mickisch

Collaborating researcher

Postdoctorate - McGill University

Co-supervisor :

Étienne Laliberté

arthur.ouaknine@mila.quebec

Lena Podina

Collaborating researcher - University of Waterloo

Co-supervisor :

elena.podina@mila.quebec

Marlena Reil

Master's Research - McGill University

marlena.reil@mila.quebec

Rasha Saha

Master's Research - McGill University

sulagna.saha@mila.quebec

luca.schmidt@uni-tuebingen.de

Luca Marie Schmidt

Collaborating researcher - University of Tübingen

Independent visiting researcher - Karlsruhe Institute of Technology

hossein.shirali@mila.quebec

Wietze Suijker

Independent visiting researcher

wietze.suijker@gmail.com

ilija.trajkovic@mila.quebec

Ilija Trajković

Collaborating researcher - Karlsruhe Institute of Technology

gabriel.tseng@mila.quebec

Gabriel Tseng

PhD - McGill University

Donna Vakalis

Collaborating Alumni - Université de Montréal

Principal supervisor :

donna.vakalis@mila.quebec

Collaborating researcher

anna.viklund@mila.quebec

catherine.villeneuve@mila.quebec

Catherine Villeneuve

PhD - McGill University

mohamed.yebari@mila.quebec

Mohamed Yebari

Collaborating researcher - Ecole Polytechnique Montréal Fédérale de Lausanne (EPFL)

Co-supervisor :

Loubna Benabbou

Shan Zhao

Collaborating researcher - Technical University of Munich

zhaoshanuestc@gmail.com

Democratizing Access to Satellite Data with AI

Blog Posts

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

October 21, 2025

Gabriel Tseng

David Rolnick

Read the 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… (see more)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 (preprint)

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 (published)

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… (see more)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 (published)

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… (see more)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 (preprint)

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… (see more)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 (published)

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… (see more)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 (published)

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 … (see 8 more)

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… (see more), 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 (published)

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… (see more)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… (see more)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… (see more) 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 (preprint)

Towards Climate Variable Prediction with Conditioned Spatio-Temporal Normalizing Flows

Christina Winkler

2023-11-11

ArXiv (preprint)