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 :

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

Collaborating researcher

Felix Andreas Nahrstedt

Research Intern - Université de Montréal

Juan Nathaniel Nathaniel

Collaborating researcher - Columbia university

Postdoctorate - McGill University

Co-supervisor :

Lena Podina

PhD - University of Waterloo

Co-supervisor :

PhD - Université de Montréal

Marlena Reil

Master's Research - McGill University

Carla Roesch

Collaborating researcher - Columbia university

Luca Marie Schmidt

Collaborating researcher - University of Tübingen

Collaborating researcher

Collaborating researcher - Karlsruhe Institute of Technology

Gabriel Tseng

PhD - McGill University

Donna Vakalis

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating researcher

[email protected]

Catherine Villeneuve

PhD - McGill University

Tiffany Vlaar

Collaborating Alumni - McGill University

Publications

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)

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… (see more)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)

A machine learning pipeline for automated insect monitoring

Aditya Jain

Fagner Cunha

M. J. Bunsen

L. Pasi

Anna Viklund

Maxim Larrivée

2024-06-01

arXiv (published)

Climate Variable Downscaling with Conditional Normalizing Flows

Christina Winkler

Paula Harder

Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations.… (see more) This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.

2024-05-31

ArXiv (preprint)

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

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

As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly i… (see more)mportant. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.

2024-03-26

ArXiv (preprint)

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… (see more)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 (preprint)

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 (preprint)