David Rolnick

Biographie

David Rolnick est professeur adjoint et titulaire d’une chaire en IA Canada-CIFAR à l'École d'informatique de l'Université McGill et membre académique principal de Mila – Institut québécois d’intelligence artificielle. Ses travaux portent sur les applications de l'apprentissage automatique dans la lutte contre le changement climatique. Il est cofondateur et président de Climate Change AI et codirecteur scientifique de Sustainability in the Digital Age. David Rolnick a obtenu un doctorat en mathématiques appliquées du Massachusetts Institute of Technology (MIT). Il a été chercheur postdoctoral en sciences mathématiques à la National Science Foundation (NSF), chercheur diplômé à la NSF et boursier Fulbright. Il a figuré sur la liste des « 35 innovateurs de moins de 35 ans » de la MIT Technology Review en 2021.

Étudiants actuels

Benjamin Akera Binen

Collaborateur·rice alumni - McGill

Collaborateur·rice alumni - UdeM

Collaborateur·rice de recherche - Cambridge University

Co-superviseur⋅e :

Mohit Anand

Collaborateur·rice de recherche - The University of Dresden, Helmholtz Centre for Environmental Research Leipzig

Ayush Ayush Prasad

Collaborateur·rice de recherche

Nikolaos Ioannis Bountos

Collaborateur·rice de recherche - National Observatory of Athens

Postdoctorat - McGill

Michael Bunsen

Collaborateur·rice de recherche - McGill

Juan Sebastián Cañas

Collaborateur·rice de recherche

Collaborateur·rice de recherche - N/A

Co-superviseur⋅e :

Yoshua Bengio

Yuyan Chen

Maîtrise recherche - McGill

Eya Cherif

Stagiaire de recherche - Leipzig University

Amna El-Mustafa

Collaborateur·rice de recherche

Mohamed Elabbas

Collaborateur·rice de recherche

Paula Harder

Visiteur de recherche indépendant

Collaborateur·rice de recherche - UdeM

Christina Humer

Collaborateur·rice de recherche - Johannes Kepler University

Christina Isaicu Isaicu

Collaborateur·rice de recherche - University of Amsterdam

Gaurav Iyer

Maîtrise recherche - McGill

Doctorat - McGill

Devin Kwok

Doctorat - McGill

Collaborateur·rice de recherche

Vanessa Mac

Collaborateur·rice de recherche - University of Waterloo

David Mickisch

Collaborateur·rice de recherche

Felix Andreas Nahrstedt

Stagiaire de recherche - UdeM

Arthur Ouaknine

Postdoctorat - McGill

Co-superviseur⋅e :

Lena Podina

Doctorat - University of Waterloo

Co-superviseur⋅e :

Doctorat - UdeM

Maîtrise recherche - McGill

luca.schmidt@uni-tuebingen.de

Luca Marie Schmidt

Collaborateur·rice de recherche - University of Tübingen

Collaborateur·rice de recherche

seth.pratinav@gmail.com

Collaborateur·rice de recherche - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Co-superviseur⋅e :

Yoshua Bengio

Ilija Trajković

Collaborateur·rice de recherche - Karlsruhe Institute of Technology

Doctorat - McGill

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche

anna.viklund@mila.quebec

Doctorat - McGill

Collaborateur·rice alumni - McGill

Publications

Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery

Mélisande Teng

Arthur Ouaknine

Etienne Lalibert'e

Yoshua Bengio

Hugo Larochelle

2025-03-26

ArXiv (prépublication)

A Joint Space-Time Encoder for Geographic Time-Series Data

David Mickisch

Konstantin Klemmer

Mélisande Teng

Many real-world processes are characterized by complex spatio-temporal dependencies, from climate dynamics to disease spread. Here, we intro… (voir plus)duce a new neural network architecture to model such dynamics at scale: the \emph{Space-Time Encoder}. Building on recent advances in \emph{location encoders}, models that take as inputs geographic coordinates, we develop a method that takes in geographic and temporal information simultaneously and learns smooth, continuous functions in both space and time. The inputs are first transformed using positional encoding functions and then fed into neural networks that allow the learning of complex functions. We implement a prototype of the \emph{Space-Time Encoder}, discuss the design choices of the novel temporal encoding, and demonstrate its utility in climate model emulation. We discuss the potential of the method across use cases, as well as promising avenues for further methodological innovation.

2025-03-06

ICLR.cc/2025/Workshop/MLMP (poster)

openreview.net

Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Laura J. Pollock

Justin Kitzes

Sara Beery

Kaitlyn M. Gaynor

Marta A. Jarzyna

Oisin Mac Aodha

Bernd Meyer

Graham W. Taylor

Devis Tuia

Tanya Berger-Wolf

2025-02-20

Nature Reviews Biodiversity (publié)

Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models

Gabriel Tseng

A. Fuller

Marlena Reil

Henry Herzog

Patrick Beukema

Favyen Bastani

James R. Green

Evan Shelhamer

Hannah Kerner

From crop mapping to flood detection, machine learning in remote sensing has a wide range of societally beneficial applications. The commona… (voir plus)lities between remote sensing data in these applications present an opportunity for pretrained machine learning models tailored to remote sensing to reduce the labeled data and effort required to solve individual tasks. However, such models must be: (i) flexible enough to ingest input data of varying sensor modalities and shapes (i.e., of varying spatial and temporal dimensions), and (ii) able to model Earth surface phenomena of varying scales and types. To solve this gap, we present Galileo, a family of pretrained remote sensing models designed to flexibly process multimodal remote sensing data. We also introduce a novel and highly effective self-supervised learning approach to learn both large- and small-scale features, a challenge not addressed by previous models. Our Galileo models obtain state-of-the-art results across diverse remote sensing tasks.

2025-02-13

ArXiv (prépublication)

Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models

Gabriel Tseng

A. Fuller

Marlena Reil

Henry Herzog

Patrick Beukema

Favyen Bastani

James R. Green

Evan Shelhamer

Hannah Kerner

2025-02-13

ArXiv (prépublication)

Using Image-based AI for insect monitoring and conservation - InsectAI COST Action

Tom August

Mario Balzan

Paul Bodesheim

Gunnar Brehm

Lisette Cantú-Salazar

Sílvia Castro

Joseph Chipperfield

Guillaume Ghisbain

Alba Gomez-Segura

Jérémie Goulnik

Quentin Groom

Laurens Hogeweg

Chantal Huijbers

Andreas Kamilaris

Karolis Kazlauskis

Wouter Koch

Dimitri Korsch

João Loureiro

Youri Martin

Angeliki Martinou … (voir 27 de plus)

Kent McFarland

Xavier Mestdagh

Denis Michez

Charlie Outhwaite

Luca Pegoraro

Nadja Pernat

Lars Pettersson

Pavel Pipek

Cristina Preda

Tobias Roth

David Roy

Helen Roy

Veljo Runnel

Martina Sasic

Dmitry Schigel

Julie Sheard

Cecilie Svenningsen

Heliana Teixeira

Nicolas Titeux

Thomas Tscheulin

Elli Tzirkalli

Marijn van der Velde

Roel van Klink

Nicolas Vereecken

Sarah Vray

Toke Thomas Høye

2025-02-10

Research Ideas and Outcomes (publié)

Insect Identification in the Wild: The AMI Dataset

Aditya Jain

Fagner Cunha

M. J. Bunsen

Juan Sebastián Cañas

L. Pasi

N. Pinoy

Flemming Helsing

JoAnne Russo

Marc Botham

Michael Sabourin

Jonathan Fréchette

Alexandre Anctil

Yacksecari Lopez

Eduardo Navarro

Filonila Perez Pimentel

Ana Cecilia Zamora

José Alejandro Ramirez Silva

Jonathan Gagnon

Tom August

K. Bjerge … (voir 8 de plus)

Alba Gomez Segura

Marc Bélisle

Yves Basset

K. P. McFarland

David Roy

Toke Thomas Høye

Maxim Larrivée

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems… (voir plus) 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.

2024-12-02

Lecture Notes in Computer Science (publié)

Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery

Pratinav Seth

Michelle Lin

BREFO DWAMENA YAW

Jade Boutot

Mary Kang

Millions of abandoned oil and gas wells are scattered across the world, leaching methane into the atmosphere and toxic compounds into the gr… (voir plus)oundwater. Many of these locations are unknown, preventing the wells from being plugged and their polluting effects averted. Remote sensing is a relatively unexplored tool for pinpointing abandoned wells at scale. We introduce the first large-scale benchmark dataset for this problem, leveraging medium-resolution multi-spectral satellite imagery from Planet Labs. Our curated dataset comprises over 213,000 wells (abandoned, suspended, and active) from Alberta, a region with especially high well density, sourced from the Alberta Energy Regulator and verified by domain experts. We evaluate baseline algorithms for well detection and segmentation, showing the promise of computer vision approaches but also significant room for improvement.

2024-10-11

ArXiv (prépublication)

Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery

Pratinav Seth

Michelle Lin

BREFO DWAMENA YAW

Jade Boutot

Mary Kang

2024-10-11

ArXiv (prépublication)

Causal Representation Learning in Temporal Data via Single-Parent Decoding

Philippe Brouillard

Sébastien Lachapelle

Julia Kaltenborn

Yaniv Gurwicz

Dhanya Sridhar

Alexandre Drouin

Peer Nowack

Jakob Runge

Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scienti… (voir plus)sts study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\~no phenomenon and other processes, as well as the causal model over them. The challenge is that this task, called causal representation learning, is highly underdetermined from observational data alone, requiring other constraints during learning to resolve the indeterminacies. In this work, we consider a temporal model with a sparsity assumption, namely single-parent decoding: each observed low-level variable is only affected by a single latent variable. Such an assumption is reasonable in many scientific applications that require finding groups of low-level variables, such as extracting regions from geographically gridded measurement data in climate research or capturing brain regions from neural activity data. We demonstrate the identifiability of the resulting model and propose a differentiable method, Causal Discovery with Single-parent Decoding (CDSD), that simultaneously learns the underlying latents and a causal graph over them. We assess the validity of our theoretical results using simulated data and showcase the practical validity of our method in an application to real-world data from the climate science field.

2024-10-09

ArXiv (prépublication)

Gintare Karolina Dziugaite

Linear Weight Interpolation Leads to Transient Performance Gains

Gaurav Iyer

2024-09-28

TMLR (accepté)

openreview.net

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 … (voir 9 de plus)

Juliane Mueller

Michael S. Pritchard

Jakob Runge

Philip Stier

Oliver Watt-Meyer

Katja Weigel

Rose Yu

Laure Zanna

2024-08-23

Nature Climate Change (publié)