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

Mohit Anand

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

Ayush Ayush Prasad

Collaborateur·rice de recherche

Viktoriia Baibakova

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

Alexandre Duval

Collaborateur·rice de recherche - Université Paris-Saclay

Amna El-Mustafa

Collaborateur·rice de recherche

Paula Harder

Collaborateur·rice de recherche

Alex Hernandez-Garcia

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Christina Humer

Collaborateur·rice de recherche - Johannes Kepler University

Gaurav Iyer

Maîtrise recherche - McGill

Doctorat - McGill

Devin Kwok

Doctorat - McGill

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

Pratinav Seth

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

Qidong Yang

Collaborateur·rice de recherche

qidong.yang@mila.quebec

Publications

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)

Gintare Karolina Dziugaite

Linear Weight Interpolation Leads to Transient Performance Gains

Gaurav Iyer

2024-09-28

TMLR (accepté)

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)

Tree semantic segmentation from aerial image time series

Venkatesh Ramesh

Arthur Ouaknine

2024-07-18

ArXiv (prépublication)

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 … (voir plus)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 (publié)

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)

Gintare Karolina Dziugaite

Linear Weight Interpolation Leads to Transient Performance Gains

Gaurav Iyer

2024-06-16

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

The Butterfly Effect: Tiny Perturbations Cause Neural Network Training to Diverge

Gül Sena Altıntaş

Devin Kwok

Neural network training begins with a chaotic phase in which the network is sensitive to small perturbations, such as those caused by stocha… (voir plus)stic gradient descent (SGD). This sensitivity can cause identically initialized networks to diverge both in parameter space and functional similarity. However, the exact degree to which networks are sensitive to perturbation, and the sensitivity of networks as they transition out of the chaotic phase, is unclear. To address this uncertainty, we apply a controlled perturbation at a single point in training time and measure its effect on otherwise identical training trajectories. We find that both the

2024-06-16

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

Position: Application-Driven Innovation in Machine Learning

Alan Aspuru-Guzik

Sara Beery

Bistra Dilkina

Priya L. Donti

Marzyeh Ghassemi

Hannah Kerner

Claire Monteleoni

Esther Rolf

Milind Tambe

Adam White

2024-05-01

ICML.cc/2024/Conference (poster)

proceedings.mlr.press

Predicting Species Occurrence Patterns from Partial Observations

Hager Radi

Mélisande Teng

To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are chang… (voir plus)ing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.

2024-03-26

ArXiv (prépublication)

Stealing Part of a Production Language Model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

Florian Tramèr

2024-03-11

ArXiv (prépublication)

Tackling Climate Change with Machine Learning: Fostering the Maturity of ML Applications for Climate Change

Shiva Madadkhani

Olivia Mendivil Ramos

Millie Chapman

Jesse Dunietz

Arthur Ouaknine

Yoshua Bengio

2024-03-08

ICLR.cc/2024/Workshop_Proposals (publié)