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

Causal Representation Learning in Temporal Data via Single-Parent Decoding

Yaniv Gurwicz

Peer Nowack

Jakob Runge

Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scienti… (voir plus)sts study how phenomena, such as El Niño, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Niño phenomenon and other processes, as well as the causal model over them. The challenge is that this task, called causal representation learning, is highly underdetermined from observational data alone, requiring other constraints during learning to resolve the indeterminacies. In this work, we consider a temporal model with a sparsity assumption, namely single-parent decoding: each observed low-level variable is only affected by a single latent variable. Such an assumption is reasonable in many scientific applications that require finding groups of low-level variables, such as extracting regions from geographically gridded measurement data in climate research or capturing brain regions from neural activity data. We demonstrate the identifiability of the resulting model and propose a differentiable method, Causal Discovery with Single-parent Decoding (CDSD), that simultaneously learns the underlying latents and a causal graph over them. We assess the validity of our theoretical results using simulated data and showcase the practical validity of our method in an application to real-world data from the climate science field.

2024-10-08

ArXiv (prépublication)

Gintare Karolina Dziugaite

Linear Weight Interpolation Leads to Transient Performance Gains

Gaurav Iyer

2024-09-27

TMLR (accepté)

Pushing the frontiers in climate modelling and analysis with machine learning

Veronika Eyring

William D. Collins

Pierre Gentine

Elizabeth A. Barnes

Marcelo Barreiro

Tom Beucler

Marc Bocquet

Christopher S. Bretherton

Hannah M. Christensen

Katherine Dagon

David John Gagne

David Hall

Dorit Hammerling

Stephan Hoyer

Fernando Iglesias-Suarez

Ignacio Lopez-Gomez

Marie C. McGraw

Gerald A. Meehl

Maria J. Molina

Claire Monteleoni … (voir 9 de plus)

Juliane Mueller

Michael S. Pritchard

Jakob Runge

Philip Stier

Oliver Watt-Meyer

Katja Weigel

Rose Yu

Laure Zanna

2024-08-22

Nature Climate Change (publié)

Simultaneous linear connectivity of neural networks modulo permutation

Ekansh Sharma

Devin Kwok

Tom Denton

Daniel M. Roy

Gintare Karolina Dziugaite

2024-08-21

Lecture Notes in Computer Science (publié)

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

Alexandre Duval

Victor Schmidt

Santiago Miret

Alex Hernández-García

Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electro… (voir plus)chemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the energy spent on such activities, we must quickly discover more efficient catalysts to drive electrochemical reactions. Machine learning (ML) holds the potential to efficiently model materials properties from large amounts of data, accelerating electrocatalyst design. The Open Catalyst Project OC20 dataset was constructed to that end. However, ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. In this paper, we propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy. This includes improvements in (1) the graph creation step, (2) atom representations, (3) the energy prediction head, and (4) the force prediction head. We describe these contributions, referred to as PhAST, and evaluate them thoroughly on multiple architectures. Overall, PhAST improves energy MAE by 4 to 42

2024-07-22

International Conference on Machine Learning (Accept (Poster))

Evaluating the transferability potential of deep learning models for climate downscaling

Ayush Prasad

Paula Harder

Qidong Yang

Prasanna Sattegeri

D. Szwarcman

Campbell Watson

Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding … (voir plus)and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types.

2024-07-16

ArXiv (prépublication)

Position: 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

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

Stealing part of a production language model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dj Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

Florian Tramèr

We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like … (voir plus)OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \\

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

A machine learning pipeline for automated insect monitoring

Aditya Jain

F. Cunha

M. J. Bunsen

L. Pasi

Anna Viklund

Maxim Larrivée

Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosyste… (voir plus)m services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.

2024-06-17

ArXiv (prépublication)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study

Fragkiskos D. Malliaros

Alexandre AGM Duval

2024-06-16

ICML.cc/2024/Workshop/ML4LMS (poster)

The Butterfly Effect: Tiny Perturbations Cause Neural Network Training to Diverge

Gül Sena Altıntaş

Devin Kwok

Neural network training begins with a chaotic phase in which the network is sensitive to small perturbations, such as those caused by stocha… (voir plus)stic gradient descent (SGD). This sensitivity can cause identically initialized networks to diverge both in parameter space and functional similarity. However, the exact degree to which networks are sensitive to perturbation, and the sensitivity of networks as they transition out of the chaotic phase, is unclear. To address this uncertainty, we apply a controlled perturbation at a single point in training time and measure its effect on otherwise identical training trajectories. We find that both the

2024-06-15

ICML.cc/2024/Workshop/HiLD (poster)

Climate Variable Downscaling with Conditional Normalizing Flows

Christina Winkler

Paula Harder

Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations.… (voir plus) This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.

2024-05-30

ArXiv (prépublication)