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 Alumni - Université de Montréal

Mohit Anand

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

Ayush Ayush Prasad

Collaborating researcher

Viktoriia Baibakova

Collaborating researcher

Nikolaos Ioannis Bountos

Collaborating researcher - National Observatory of Athens

Collaborating researcher

Michael Bunsen

Collaborating researcher - McGill University

Juan Sebastián Cañas

Collaborating researcher

Collaborating researcher - N/A

Co-supervisor :

Yuyan Chen

Master's Research - McGill University

Eya Cherif

Research Intern - Leipzig University

Alexandre Duval

Collaborating researcher - Université Paris-Saclay

Amna El-Mustafa

Collaborating researcher

Paula Harder

Collaborating researcher

Alex Hernandez-Garcia

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating researcher - Université de Montréal

Christina Humer

Collaborating researcher - Johannes Kepler University

Gaurav Iyer

Master's Research - McGill University

Julia Kaltenborn

PhD - McGill University

Devin Kwok

PhD - McGill University

Vanessa Mac

Collaborating researcher - University of Waterloo

David Alexandre Mickisch

Collaborating researcher

Felix Andreas Nahrstedt

Research Intern - Université de Montréal

Arthur Ouaknine

Postdoctorate - McGill University

Co-supervisor :

Lena Podina

PhD - University of Waterloo

Co-supervisor :

Venkatesh Ramesh

PhD - Université de Montréal

Ali Ramlaoui

Collaborating researcher

Marlena Reil

Master's Research - McGill University

Pratinav Seth

Collaborating researcher

seth.pratinav@gmail.com

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

Co-supervisor :

Ilija Trajković

Collaborating researcher - Karlsruhe Institute of Technology

Gabriel Tseng

PhD - McGill University

Donna Vakalis

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating researcher

anna.viklund@mila.quebec

Catherine Villeneuve

PhD - McGill University

Tiffany Vlaar

Collaborating Alumni - McGill University

Qidong Yang

Collaborating researcher

qidong.yang@mila.quebec

Publications

Multi-variable Hard Physical Constraints for Climate Model Downscaling

Jose Gonz'alez-Abad

'Alex Hern'andez-Garc'ia

Paula Harder

Jos'e Manuel Guti'errez

2023-08-02

ArXiv (preprint)

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

Alexandre AGM Duval

Victor Schmidt

Alex Hernandez-Garcia

Santiago Miret

Fragkiskos D. Malliaros

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)

Hidden Symmetries of ReLU Networks

J. Grigsby

Elisenda Grigsby

Kathryn Lindsey

2023-07-03

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

Maximal Initial Learning Rates in Deep ReLU Networks

Gaurav Iyer

Boris Hanin

2023-07-03

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

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang

Alex Hernandez-Garcia

Paula Harder

Venkatesh Ramesh

Prasanna Sattegeri

D. Szwarcman

C. Watson

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)

Bird Distribution Modelling using Remote Sensing and Citizen Science data

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Hugo Larochelle

2023-05-01

ArXiv (preprint)

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Gabriel Tseng

Ruben Cartuyvels

Ivan Zvonkov

Mirali Purohit

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)

Maximal Initial Learning Rates in Deep ReLU Networks

Gaurav Iyer

Boris Hanin

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)

Semi-Supervised Object Detection for Agriculture

Gabriel Tseng

Krisztina Sinkovics

Tom Watsham

Thomas C. Walters

2023-01-26

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

Bugs in the Data: How ImageNet Misrepresents Biodiversity

Alexandra Luccioni

ImageNet-1k is a dataset often used for benchmarking machine learning (ML) models and evaluating tasks such as image recognition and object … (see more)detection. Wild animals make up 27% of ImageNet-1k but, unlike classes representing people and objects, these data have not been closely scrutinized. In the current paper, we analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set, with the participation of expert ecologists. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled, with some classes having >90% of images incorrect. We also find that both the wildlife-related labels and images included in ImageNet-1k present significant geographical and cultural biases, as well as ambiguities such as artificial animals, multiple species in the same image, or the presence of humans. Our findings highlight serious issues with the extensive use of this dataset for evaluating ML systems, the use of such algorithms in wildlife-related tasks, and more broadly the ways in which ML datasets are commonly created and curated.

2023-01-01

AAAI (published)

Deep Networks as Paths on the Manifold of Neural Representations

Richard D Lange

Devin Kwok

Jordan Kyle Matelsky

Xinyue Wang

Konrad Paul Kording

2023-01-01

TAG-ML (published)

proceedings.mlr.press

General Purpose AI Systems in the AI Act: Trying to Fit a Square Peg Into a Round Hole

Claire Boine

2023-01-01

SSRN Electronic Journal (published)