David Rolnick

karl.anderson@mila.quebec

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 :

Yoshua Bengio

berkes.anais@mila.quebec

Collaborating researcher

Julien Boussard

Postdoctorate - McGill University

julien.boussard@mila.quebec

Michael Bunsen

Collaborating researcher - McGill University

michael.bunsen@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 :

Yoshua Bengio

elena.podina@mila.quebec

andrew.quitmeyer@mila.quebec

Andy Quitmeyer

Independent visiting researcher - University of Washington

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

PhD - McGill University

raesetje.sefala@mila.quebec

hossein.shirali@mila.quebec

Hossein Shirali

Independent visiting researcher - Karlsruhe Institute of Technology

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@mila.quebec

Donna Vakalis

Collaborating Alumni - Université de Montréal

Principal supervisor :

Yoshua Bengio

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

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… (see more)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 … (see more)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 (preprint)

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… (see more)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 (published)

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… (see more) 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… (see more)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 (published)

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… (see more)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 (accepted)

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 (published)

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… (see more) 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… (see more)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 (unknown)

arxiv.org

Reverse-engineering deep ReLU networks