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

Collaborating researcher - Cambridge University

Co-supervisor :

Mohit Anand

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

Ayush Ayush Prasad

Collaborating researcher

Nikolaos Ioannis Bountos

Collaborating researcher - National Observatory of Athens

Postdoctorate - McGill University

Michael Bunsen

Collaborating researcher - McGill University

Juan Sebastián Cañas

Collaborating researcher

Collaborating researcher - N/A

Co-supervisor :

Yoshua Bengio

Yuyan Chen

Master's Research - McGill University

Eya Cherif

Research Intern - Leipzig University

Amna El-Mustafa

Collaborating researcher

Mohamed Elabbas

Collaborating researcher

Paula Harder

Independent visiting researcher

Collaborating researcher - Université de Montréal

Christina Humer

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

Collaborating researcher

Vanessa Mac

Collaborating researcher - University of Waterloo

David 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 :

Yoshua Bengio

Venkatesh Ramesh

PhD - Université de Montréal

Marlena Reil

Master's Research - McGill University

luca.schmidt@uni-tuebingen.de

Luca Marie Schmidt

Collaborating researcher - University of Tübingen

Collaborating researcher

seth.pratinav@gmail.com

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

Co-supervisor :

Yoshua Bengio

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

Publications

Pushing the frontiers in climate modelling and analysis with machine learning

Veronika Eyring

William D. Collins

Pierre Gentine

Elizabeth A. Barnes

Marcelo Barreiro

Tom Beucler

Marc Bocquet

Christopher S. Bretherton

Hannah M. Christensen

Katherine Dagon

David John Gagne

David Hall

Dorit Hammerling

Stephan Hoyer

Fernando Iglesias-Suarez

Ignacio Lopez-Gomez

Marie C. McGraw

Gerald A. Meehl

Maria J. Molina

Claire Monteleoni … (see 9 more)

Juliane Mueller

Michael S. Pritchard

Jakob Runge

Philip Stier

Oliver Watt-Meyer

Katja Weigel

Rose Yu

Laure Zanna

2024-08-23

Nature Climate Change (published)

Pushing the frontiers in climate modelling and analysis with machine learning

Veronika Eyring

William D. Collins

Pierre Gentine

Elizabeth A. Barnes

Marcelo Barreiro

Tom Beucler

Marc Bocquet

Christopher S. Bretherton

Hannah M. Christensen

Katherine Dagon

David John Gagne

David Hall

Dorit Hammerling

Stephan Hoyer

Fernando Iglesias-Suarez

Ignacio Lopez-Gomez

Marie C. McGraw

Gerald A. Meehl

Maria J. Molina

Claire Monteleoni … (see 9 more)

Juliane Mueller

Michael S. Pritchard

Jakob Runge

Philip Stier

Oliver Watt-Meyer

Katja Weigel

Rose Yu

Laure Zanna

2024-08-23

Nature Climate Change (published)

Pushing the frontiers in climate modelling and analysis with machine learning

Veronika Eyring

William D. Collins

Pierre Gentine

Elizabeth A. Barnes

Marcelo Barreiro

Tom Beucler

Marc Bocquet

Christopher S. Bretherton

Hannah M. Christensen

Katherine Dagon

David John Gagne

David Hall

Dorit Hammerling

Stephan Hoyer

Fernando Iglesias-Suarez

Ignacio Lopez-Gomez

Marie C. McGraw

Gerald A. Meehl

Maria J. Molina

Claire Monteleoni … (see 9 more)

Juliane Mueller

Michael S. Pritchard

Jakob Runge

Philip Stier

Oliver Watt-Meyer

Katja Weigel

Rose Yu

Laure Zanna

2024-08-23

Nature Climate Change (published)

Pushing the frontiers in climate modelling and analysis with machine learning

Veronika Eyring

William D. Collins

Pierre Gentine

Elizabeth A. Barnes

Marcelo Barreiro

Tom Beucler

Marc Bocquet

Christopher S. Bretherton

Hannah M. Christensen

Katherine Dagon

David John Gagne

David Hall

Dorit Hammerling

Stephan Hoyer

Fernando Iglesias-Suarez

Ignacio Lopez-Gomez

Marie C. McGraw

Gerald A. Meehl

Maria J. Molina

Claire Monteleoni … (see 9 more)

Juliane Mueller

Michael S. Pritchard

Jakob Runge

Philip Stier

Oliver Watt-Meyer

Katja Weigel

Rose Yu

Laure Zanna

2024-08-23

Nature Climate Change (published)

Tree semantic segmentation from aerial image time series

Venkatesh Ramesh

Arthur Ouaknine

2024-07-18

ArXiv (preprint)

Evaluating the transferability potential of deep learning models for climate downscaling

Ayush Prasad

Paula Harder

Qidong Yang

Prasanna Sattegeri

Daniela Szwarcman

Campbell Watson

Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding … (see more)and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types.

2024-07-17

ArXiv (preprint)

Evaluating the transferability potential of deep learning models for climate downscaling

Ayush Prasad

Paula Harder

Qidong Yang

Prasanna Sattegeri

Daniela Szwarcman

Campbell Watson

2024-07-17

ArXiv (preprint)

Stealing part of a production language model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dj Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

Florian Tramèr

We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like … (see more)OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \\

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

openreview.net

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

D. B. Roy

David Roy

J. Alison

Tom August

M. Bélisle

K. Bjerge

J. J. Bowden

M. J. Bunsen

F. Cunha

Q. Geissmann

K. Goldmann

Alba Gomez-Segura

A. Jain

C. Huijbers

M. Larrivée

J. L. Lawson

H. M. Mann

M. J. Mazerolle

K. P. McFarland

L. Pasi … (see 8 more)

S. Peters

N. Pinoy

G. L. Skinner

O. T. Strickson

A. Svenning

S. Teagle

Toke Thomas Høye

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and faste… (see more)st developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects—from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.

2024-06-24

Philosophical Transactions of the Royal Society B: Biological Sciences (published)

Insect Identification in the Wild: The AMI Dataset

Aditya Jain

Fagner Cunha

M. Bunsen

Juan Sebasti'an Canas

L. Pasi

N. Pinoy

Flemming Helsing

JoAnne Russo

Marc Botham

Michael Sabourin

Jonathan Fr'echette

Alexandre Anctil

Yacksecari Lopez

Eduardo Navarro

Filonila Perez Pimentel

Ana Cecilia Zamora

José Alejandro Ramirez Silva

Jonathan Gagnon

T. August

Kim Bjerge … (see 8 more)

Alba Gomez Segura

Marc B'elisle

Yves Basset

K. P. McFarland

David Roy

Toke Thomas Høye

Maxim Larriv'ee

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems… (see more) and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups.

2024-06-18

ArXiv (preprint)

A machine learning pipeline for automated insect monitoring

Aditya Jain

Fagner Cunha

M. Bunsen

L. Pasi

Anna Viklund

Maxim Larriv'ee

Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosyste… (see more)m services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.

2024-06-18

ArXiv (preprint)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study

Ali Ramlaoui

Théo Saulus

Basile Terver

Victor Schmidt

Fragkiskos D. Malliaros

Alexandre AGM Duval

2024-06-17

ICML.cc/2024/Workshop/ML4LMS (poster)