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

Juan Sebastián Cañas

Collaborateur·rice de recherche

Collaborateur·rice de recherche - N/A

Co-superviseur⋅e :

Yuyan Chen

Maîtrise recherche - 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 - UdeM

Christina Humer

Collaborateur·rice de recherche - Johannes Kepler University

Christina Isaicu Isaicu

Collaborateur·rice de recherche - University of Amsterdam

Gaurav Iyer

Maîtrise recherche - McGill

Doctorat - McGill

Doctorat - McGill

Collaborateur·rice de recherche

Visiteur de recherche indépendant - Université de Montréal

Pierre-Louis Lemaire

Collaborateur·rice de recherche - Polytechnique Montréal

Superviseur⋅e principal⋅e :

Alex Hernandez-Garcia

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

Collaborateur·rice de recherche - Karlsruhe Institute of Technology

Doctorat - McGill

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche

Doctorat - McGill

Collaborateur·rice alumni - McGill

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

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Publications

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

Shiva Madadkhani

Olivia Mendivil Ramos

Millie Chapman

Jesse Dunietz

2024-03-08

ICLR.cc/2024/Workshop_Proposals (publié)

openreview.net

Dataset Difficulty and the Role of Inductive Bias

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.

2024-01-03

ArXiv (prépublication)

Dataset Difficulty and the Role of Inductive Bias

Nikhil Anand

Jonathan Frankle

2024-01-03

ArXiv (prépublication)

Application-Driven Innovation in Machine Learning

Alan 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.

2024-01-01

ICML (publié)

Linear Weight Interpolation Leads to Transient Performance Gains

Gaurav Iyer

2024-01-01

Trans. Mach. Learn. Res. (publié)

openreview.net

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

Alex Hernandez-Garcia

openreview.net

Simultaneous linear connectivity of neural networks modulo permutation

Ekansh Sharma

Tom Denton

Daniel M. Roy

2024-01-01

ECML/PKDD (7) (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

2023-12-22

Science (publié)

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

Nikolaos Ioannis Bountos

Ioannis Papoutsis

2023-12-15

ArXiv (prépublication)

FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models

Nikolaos Ioannis Bountos

Forests are an essential part of Earth's ecosystems and natural systems, as well as providing services on which humanity depends, yet they a… (voir plus)re rapidly changing as a result of land use decisions and climate change. Understanding and mitigating negative effects requires parsing data on forests at global scale from a broad array of sensory modalities, and recently many such problems have been approached using machine learning algorithms for remote sensing. To date, forest-monitoring problems have largely been addressed in isolation. Inspired by the rise of foundation models for computer vision and remote sensing, we here present the first unified Forest Monitoring Benchmark (FoMo-Bench). 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 (SAR) 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 further enhance the diversity of tasks and geographies represented in FoMo-Bench, we introduce a novel global dataset, TalloS, combining satellite imagery with ground-based annotations for tree species classification, encompassing 1,000+ categories across multiple hierarchical taxonomic levels (species, genus, family). Finally, we propose FoMo-Net, a baseline foundation model with the capacity to process any combination of commonly used spectral bands in remote sensing, across diverse ground sampling distances and geographical locations worldwide. This work aims to inspire research collaborations between machine learning and forest biology researchers in exploring scalable multi-modal and multi-task models for forest monitoring. All code and data will be made publicly available.

2023-12-15

ArXiv (prépublication)

FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models

Nikolaos Ioannis Bountos

Ioannis Papoutsis

2023-12-15

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.

2023-12-05

ArXiv (prépublication)