David Rolnick

karl.anderson@mila.quebec

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 :

Yoshua Bengio

berkes.anais@mila.quebec

Collaborateur·rice de recherche

Julien Boussard

Postdoctorat - McGill

julien.boussard@mila.quebec

Michael Bunsen

Collaborateur·rice de recherche - McGill

michael.bunsen@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 :

Yoshua Bengio

elena.podina@mila.quebec

andrew.quitmeyer@mila.quebec

Andy Quitmeyer

Visiteur de recherche indépendant - University of Washington

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

Doctorat - McGill

raesetje.sefala@mila.quebec

hossein.shirali@mila.quebec

Hossein Shirali

Visiteur de recherche indépendant - Karlsruhe Institute of Technology

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@mila.quebec

Donna Vakalis

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Yoshua Bengio

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

Deep ReLU Networks Preserve Expected Length

Boris Hanin

Ryan Jeong

Assessing the complexity of functions computed by a neural network helps us understand how the network will learn and generalize. One natura… (voir plus)l measure of complexity is how the network distorts length - if the network takes a unit-length curve as input, what is the length of the resulting curve of outputs? It has been widely believed that this length grows exponentially in network depth. We prove that in fact this is not the case: the expected length distortion does not grow with depth, and indeed shrinks slightly, for ReLU networks with standard random initialization. We also generalize this result by proving upper bounds both for higher moments of the length distortion and for the distortion of higher-dimensional volumes. These theoretical results are corroborated by our experiments.

2022-01-27

ICLR.cc/2022/Conference (poster)

Generating physically-consistent high-resolution climate data with hard-constrained neural networks

Paula Harder

Qidong Yang

Venkatesh Ramesh

Prasanna Sattegeri

Alex Hernández-García

Campbell Watson

D. Szwarcman

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (voir plus)mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often can only make coarse resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often us-ing image super-resolution methods from computer vision. Despite achieving visually compelling results in some cases, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: A renor-malization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.

2021-12-31

arXiv.org (prépublication)

TIML: Task-Informed Meta-Learning for crop type mapping

Gabriel Tseng

Hannah Kerner

Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a previously explor… (voir plus)ed approach is to use transfer learning from data-rich regions. While standard transfer learning approaches typically leverage only direct inputs and outputs, geospatial imagery and agricultural data is rich in metadata that can inform transfer learning algorithms, such as the spatial coordinates of data-points. We build on previous work exploring use of meta-learning to crop type mapping in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning which takes advantage of this metadata. We apply TIML to the CropHarvest dataset, a global dataset of agricultural class labels paired with remote sensing data. In addition, we introduce the concept of forgetfulness when training meta-learning models on many similar tasks to mitigate memorization of training tasks. We find that TIML significantly improves average performance across the CropHarvest evaluation tasks compared to a range of benchmark models, measured using AUC ROC and F1 scores.

2021-12-31

AAAI.org/2022/Workshop/AIAFS (publié)

Techniques for Symbol Grounding with SATNet

Sever Topan

Xujie Si

Many experts argue that the future of artificial intelligence is limited by the field's ability to integrate symbolic logical reasoning into… (voir plus) deep learning architectures. The recently proposed differentiable MAXSAT solver, SATNet, was a breakthrough in its capacity to integrate with a traditional neural network and solve visual reasoning problems. For instance, it can learn the rules of Sudoku purely from image examples. Despite its success, SATNet was shown to succumb to a key challenge in neurosymbolic systems known as the Symbol Grounding Problem: the inability to map visual inputs to symbolic variables without explicit supervision ("label leakage"). In this work, we present a self-supervised pre-training pipeline that enables SATNet to overcome this limitation, thus broadening the class of problems that SATNet architectures can solve to include datasets where no intermediary labels are available at all. We demonstrate that our method allows SATNet to attain full accuracy even with a harder problem setup that prevents any label leakage. We additionally introduce a proofreading method that further improves the performance of SATNet architectures, beating the state-of-the-art on Visual Sudoku.

2021-12-06

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

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks

Christopher Hillar

Tenzin Chan

Rachel Taubman

In 1943, McCulloch and Pitts introduced a discrete recurrent neural network as a model for computation in brains. The work inspired breakthr… (voir plus)oughs such as the first computer design and the theory of finite automata. We focus on learning in Hopfield networks, a special case with symmetric weights and fixed-point attractor dynamics. Specifically, we explore minimum energy flow (MEF) as a scalable convex objective for determining network parameters. We catalog various properties of MEF, such as biological plausibility, and then compare to classical approaches in the theory of learning. Trained Hopfield networks can perform unsupervised clustering and define novel error-correcting coding schemes. They also efficiently find hidden structures (cliques) in graph theory. We extend this known connection from graphs to hypergraphs and discover n-node networks with robust storage of 2Ω(n1−ϵ) memories for any ϵ>0. In the case of graphs, we also determine a critical ratio of training samples at which networks generalize completely.

2021-11-10

Entropy (publié)

ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models

Salva Rühling Cachay

Venkatesh Ramesh

Jason N. S. Cole

Howard Barker

Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in repla… (voir plus)cing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To fill this gap, we build a large dataset, ClimART, with more than 10 million samples from present, pre-industrial, and future climate conditions, based on the Canadian Earth System Model. ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed. We also present several novel baselines that indicate shortcomings of datasets and network architectures used in prior work.

2021-10-10

NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round2 (accepté)

Digitizing a sustainable future

Lucia A. Reisch

Lucas Joppa

Peter Howson

Artur Gil

Panayiota Alevizou

Nina Michaelidou

Ruby Appiah-Campbell

Tilman Santarius

Susanne Köhler

Massimo Pizzol

Pia-Johanna Schweizer

Dipti Srinivasan

Lynn H. Kaack

Priya L. Donti

2021-06-17

One Earth (publié)

DC3: A learning method for optimization with hard constraints

Priya L. Donti

J Zico Kolter

Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the… (voir plus) use of deep networks as cheap"approximate solvers."Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infeasible solutions. In this work, we present Deep Constraint Completion and Correction (DC3), an algorithm to address this challenge. Specifically, this method enforces feasibility via a differentiable procedure, which implicitly completes partial solutions to satisfy equality constraints and unrolls gradient-based corrections to satisfy inequality constraints. We demonstrate the effectiveness of DC3 in both synthetic optimization tasks and the real-world setting of AC optimal power flow, where hard constraints encode the physics of the electrical grid. In both cases, DC3 achieves near-optimal objective values while preserving feasibility.

2021-01-11

ICLR.cc/2021/Conference (poster)

Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification

Charles A. Kantor

Marta Skreta

Brice Rauby

Léonard Boussioux

Emmanuel Jehanno

Alexandra Luccioni

Hugues Talbot

Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute deta… (voir plus)ils. Our primary challenges come from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of wildlife, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue.

2020-12-31

IJCAI Workshop on AI for Social Good (inconnu)

arxiv.org

Reverse-engineering deep ReLU networks