David Rolnick

Core Academic Member

drolnick@mila.quebec

Canada CIFAR AI Chair

Assistant Professor, McGill University, School of Computer Science

Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research

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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

Alex Hernandez-Garcia

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating researcher - Université Paris-Saclay

Co-supervisor :

Yoshua Bengio

alexandre.duval@mila.quebec

Website

Ali Ramlaoui

Collaborating researcher

ali.ramlaoui@mila.quebec

Github

Amna El-Mustafa

Collaborating researcher

amna.elmustafa@mila.quebec

Anna Viklund

Collaborating researcher

anna.viklund@mila.quebec

Github

Arthur Ouaknine

Postdoctorate - McGill University

arthur.ouaknine@mila.quebec

Collaborating researcher

ayush.prasad@mila.quebec

Basile Terver

Collaborating researcher

Co-supervisor :

Yoshua Bengio

basile.terver@mila.quebec

Benjamin Akera Binen

Master's Research - McGill University

PhD - McGill University

catherine.villeneuve@mila.quebec

Github

Chandni Nagda

Collaborating researcher

chandni.nagda@mila.quebec

Christina Winkler

Collaborating researcher

christina.winkler@mila.quebec

Research Intern - Johannes Kepler University

christina.humer@mila.quebec

Website

Github

Google Scholar

David Alexandre Mickisch

Collaborating researcher

david.mickisch@mila.quebec

Github

Devin Kwok

PhD - McGill University

devin.kwok@mila.quebec

Postdoctorate - Université de Montréal

Principal supervisor :

Yoshua Bengio

donna.vakalis@mila.quebec

Github

Google Scholar

Eya Cherif

Research Intern

eya.cherif@mila.quebec

Felix Andreas Nahrstedt

Research Intern - Université de Montréal

felix-andreas.nahrstedt@mila.quebec

Website

Fagner Cunha

Collaborating Alumni

fagner.cunha@mila.quebec

PhD - McGill University

gabriel.tseng@mila.quebec

Website

Github

Gaurav Iyer

Master's Research - McGill University

gaurav.iyer@mila.quebec

Google Scholar

Gül Sena Altıntaş

Research Intern - Université de Montréal

gul-sena.altintas@mila.quebec

Collaborating researcher - Karlsruhe Institute of Technology

ilija.trajkovic@mila.quebec

Github

Juan Sebastián Cañas

Collaborating researcher

juan-sebastian.canas-silva@mila.quebec

PhD - McGill University

julia.kaltenborn@mila.quebec

Collaborating researcher

julien.boussard@mila.quebec

Marlena Reil

Research Intern - Osnabrueck university

marlena.reil@mila.quebec

Github

Mehrab Hamidi

Master's Research - McGill University

mehrab.hamidi@mila.quebec

Github

Google Scholar

Michael Bunsen

Collaborating researcher - McGill University

michael.bunsen@mila.quebec

Website

Github

Mohit Anand

Collaborating researcher - The University of Dresden, Helmholtz Centre for Environmental Research Leipzig

mohit.anand@mila.quebec

Github

Nikolaos Ioannis Bountos

Collaborating researcher - National Observatory of Athens

nikolaos.bountos@mila.quebec

Collaborating researcher

paula.harder@mila.quebec

Collaborating researcher

pratinav.seth@learner.manipal.edu

Website

Github

Google Scholar

Qidong Yang

Collaborating researcher

qidong.yang@mila.quebec

Raesetje Sefala

PhD - McGill University

raesetje.sefala@mila.quebec

Website

Ruben Cartuyvels

Collaborating researcher - KU Leuven

ruben.cartuyvels@mila.quebec

Sebastian Hickman

Research Intern - Cambridge University

sebastian.hickman@mila.quebec

Theo Saulus

Collaborating researcher

Co-supervisor :

Yoshua Bengio

theo.saulus@mila.quebec

Tiffany Vlaar

Postdoctorate - McGill University

tiffany.vlaar@mila.quebec

Google Scholar

Venkatesh Ramesh

PhD - Université de Montréal

venkatesh.ramesh@mila.quebec

Todosijevic Viktor Todosijevic

Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Co-supervisor :

Yoshua Bengio

viktor.todosijevic@mila.quebec

Github

Yuyan Chen

Master's Research - McGill University

yuyan.chen@mila.quebec

Website

Github

Publications

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

Alvaro Carbonero

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

Alex Hernandez-Garcia

Yoshua Bengio

David Rolnick

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (see more)ate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

doi.org

openreview.net

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning

Julia Kaltenborn

Charlotte Emilie Elektra Lange

Venkatesh Ramesh

Philippe Brouillard

Yaniv Gurwicz

Chandni Nagda

Jakob Runge

Peer Nowack

David Rolnick

Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) c… (see more)ommunity has taken an increased interest in supporting climate scientists’ efforts on various tasks such as climate model emulation, downscaling, and prediction tasks. Many of those tasks have been addressed on datasets created with single climate models. However, both the climate science and ML communities have suggested that to address those tasks at scale, we need large, consistent, and ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset containing the inputs and outputs of 36 climate models from the Input4MIPs and CMIP6 archives. In addition, we provide a modular dataset pipeline for retrieving and preprocessing additional climate models and scenarios. We showcase the potential of our dataset by using it as a benchmark for ML-based climate model emulation. We gain new insights about the performance and generalization capabilities of the different ML models by analyzing their performance across different climate models. Furthermore, the dataset can be used to train an ML emulator on several climate models instead of just one. Such a “super-emulator” can quickly project new climate change scenarios, complementing existing scenarios already provided to policymakers. We believe ClimateSet will create the basis needed for the ML community to tackle climate-related tasks at scale.

openreview.net

SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Hager Radi

Multi-variable Hard Physical Constraints for Climate Model Downscaling

Jose Gonz'alez-Abad

'Alex Hern'andez-Garc'ia

Paula Harder

David Rolnick

Jos'e Manuel Guti'errez

2023-08-02

ArXiv (preprint)

doi.org

arxiv.org

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

Alexandre AGM Duval

Victor Schmidt

Alex Hernandez-Garcia

Santiago Miret

Fragkiskos D. Malliaros

Yoshua Bengio

David Rolnick

Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (see more)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (published)

doi.org

openreview.net

Hidden Symmetries of ReLU Networks

J. Grigsby

Elisenda Grigsby

Kathryn Lindsey

David Rolnick

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (published)

doi.org

openreview.net

Maximal Initial Learning Rates in Deep ReLU Networks

Gaurav Iyer

Boris Hanin

David Rolnick

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (published)

doi.org

openreview.net

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang

Alex Hernandez-Garcia

Paula Harder

Venkatesh Ramesh

Prasanna Sattegeri

D. Szwarcman

C. Watson

David Rolnick

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (see more)ly expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.

2023-05-23

ArXiv (preprint)

doi.org

arxiv.org

Bird Distribution Modelling using Remote Sensing and Citizen Science data

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Hugo Larochelle

David Rolnick

2023-05-01

ArXiv (preprint)

doi.org

arxiv.org

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Gabriel Tseng

Ruben Cartuyvels

Ivan Zvonkov

Mirali Purohit

David Rolnick

Hannah Kerner

Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficu… (see more)lt or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data, including the temporal dimension (which is critical for many applications, such as monitoring crop growth) and availability of data from many complementary sensors (which can significantly improve a model's predictive performance). We present Presto (the Pretrained Remote Sensing Transformer), a model pre-trained on remote sensing pixel-timeseries data. By designing Presto specifically for remote sensing data, we can create a significantly smaller but performant model. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.

2023-04-27

ArXiv (preprint)

doi.org

openreview.net

Maximal Initial Learning Rates in Deep ReLU Networks

Gaurav Iyer

Boris Hanin

David Rolnick

Training a neural network requires choosing a suitable learning rate, which involves a trade-off between speed and effectiveness of converge… (see more)nce. While there has been considerable theoretical and empirical analysis of how large the learning rate can be, most prior work focuses only on late-stage training. In this work, we introduce the maximal initial learning rate

2023-04-24

ICML.cc/2023/Conference (poster)

doi.org

openreview.net

Semi-Supervised Object Detection for Agriculture

Gabriel Tseng

Krisztina Sinkovics

Tom Watsham

David Rolnick

Thomas C. Walters

2023-01-26

AAAI.org/2023/Workshop/AIAFS (poster)

openreview.net

AI Research Driven by Real-World Problems

AI Policy Compass

Student Life and Resources

David Rolnick

Biography

Current Students

Publications

AI Research Driven by Real-World Problems

AI Policy Compass

Student Life and Resources

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David Rolnick

Biography

Current Students

Publications