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 :

Postdoctorate - McGill University

Michael Bunsen

Collaborating researcher - McGill University

Shahana Chatterjee

Collaborating researcher - N/A

Co-supervisor :

Yoshua Bengio

Yuyan Chen

PhD - McGill University

Eya Cherif

Collaborating researcher - Leipzig University

Othmane Echchabi

Master's Research - McGill University

Collaborating researcher

Mohamed Elabbas

Collaborating researcher

Jannik Endres

Collaborating researcher

Jacopo Ghirri

Independent visiting researcher - Politecnico di Milano

Independent visiting researcher

Collaborating researcher - Johannes Kepler University

Christina Isaicu Isaicu

Collaborating researcher - University of Amsterdam

Gaurav Iyer

Master's Research - McGill University

Julia Kaltenborn

PhD - McGill University

Devin Kwok

PhD - McGill University

Independent visiting researcher - Université de Montréal

Pierre-Louis Lemaire

Collaborating researcher - Polytechnique Montréal Montréal

Principal supervisor :

Alex Hernández-García

David Mickisch

Collaborating researcher

Postdoctorate - McGill University

Co-supervisor :

Lena Podina

Collaborating researcher - University of Waterloo

Co-supervisor :

Yoshua Bengio

Marlena Reil

Master's Research - McGill University

Rasha Saha

Master's Research - McGill University

Luca Marie Schmidt

Collaborating researcher - University of Tübingen

Independent visiting researcher - Karlsruhe Institute of Technology

Wietze Suijker

Independent visiting researcher

Ilija Trajković

Collaborating researcher - Karlsruhe Institute of Technology

Gabriel Tseng

PhD - McGill University

Donna Vakalis

Collaborating Alumni - Université de Montréal

Principal supervisor :

Collaborating researcher

Catherine Villeneuve

PhD - McGill University

Mohamed Yebari

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

Co-supervisor :

Loubna Benabbou

Shan Zhao

Collaborating researcher - Technical University of Munich

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

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)

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.

2021-11-08

NeurIPS.cc/2021/Conference (spotlight)

openreview.net

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)

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.

2020-12-31

NeurIPS Datasets and Benchmarks (published)

openreview.net

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.

2020-12-31

ICLR (published)

openreview.net

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

arXiv (preprint)

arxiv.org

Reverse-engineering deep ReLU networks