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

Master's Research - McGill University

akeraben@mila.quebec

Gül Sena Altıntaş

Research Intern - Université de Montréal

gul-sena.altintas@mila.quebec

EL-Mustafa amna EL-Mustafa

Collaborating Researcher

amna.elmustafa@mila.quebec

Ayush Ayush Prasad

Collaborating Researcher

ayush.prasad@mila.quebec

Nikolaos Ioannis Bountos

Collaborating Researcher - National Observatory of Athens

nikolaos.bountos@mila.quebec

Julien Boussard

Collaborating Researcher

julien.boussard@mila.quebec

Michael Bunsen

Collaborating Researcher - McGill University

michael.bunsen@mila.quebec

Juan Sebastián Cañas

Collaborating Researcher

juan-sebastian.canas-silva@mila.quebec

Ruben Cartuyvels

Collaborating Researcher - KU Leuven

ruben.cartuyvels@mila.quebec

Yuyan Chen

Master's Research - McGill University

yuyan.chen@mila.quebec

Eya Cherif

Research Intern

eya.cherif@mila.quebec

Fagner Cunha

Collaborating Alumni

fagner.cunha@mila.quebec

Mickisch David Alexandre

Collaborating Researcher

david.mickisch@mila.quebec

Alexandre Duval

Collaborating Researcher - Université Paris-Saclay

Co-supervisor :

alexandre.duval@mila.quebec

Mehrab Hamidi

Master's Research - McGill University

mehrab.hamidi@mila.quebec

Paula Harder

Research Intern

paula.harder@mila.quebec

Alejandro Hernández Garcia

Postdoctorate - Université de Montréal

Principal supervisor :

hernanga@mila.quebec

Sebastian Hickman

Research Intern - Cambridge University

sebastian.hickman@mila.quebec

Christina Humer

Collaborating Researcher

christina.humer@mila.quebec

Gaurav Iyer

Master's Research - McGill University

gaurav.iyer@mila.quebec

Julia Kaltenborn

PhD - McGill University

julia.kaltenborn@mila.quebec

Devin Kwok

PhD - McGill University

devin.kwok@mila.quebec

Chandni Nagda

Collaborating Researcher

chandni.nagda@mila.quebec

Felix Andreas Nahrstedt

Research Intern - Université de Montréal

felix-andreas.nahrstedt@mila.quebec

Arthur Ouaknine

Postdoctorate - McGill University

arthur.ouaknine@mila.quebec

Venkatesh Ramesh

PhD - Université de Montréal

venkatesh.ramesh@mila.quebec

Ali Ramlaoui

Collaborating Researcher

ali.ramlaoui@mila.quebec

Marlena Reil

Research Intern

marlena.reil@mila.quebec

Theo Saulus

Collaborating Researcher

Co-supervisor :

theo.saulus@mila.quebec

Raesetje Sefala

PhD - McGill University

raesetje.sefala@mila.quebec

Pratinav Seth

Collaborating Researcher

pratinav.seth@learner.manipal.edu

Basile Terver

Collaborating Researcher

Co-supervisor :

basile.terver@mila.quebec

Ilija Trajković

Collaborating Researcher - Karlsruhe Institute of Technology

ilija.trajkovic@mila.quebec

Gabriel Tseng

PhD - McGill University

gabriel.tseng@mila.quebec

Donna Vakalis

Postdoctorate - Université de Montréal

Principal supervisor :

donna.vakalis@mila.quebec

Catherine Villeneuve

PhD - McGill University

catherine.villeneuve@mila.quebec

Tiffany Vlaar

Postdoctorate - McGill University

tiffany.vlaar@mila.quebec

Christina Winkler

Collaborating Researcher

christina.winkler@mila.quebec

Qidong Yang

Collaborating Researcher

qidong.yang@mila.quebec

Publications

Simultaneous linear connectivity of neural networks modulo permutation

Ekansh Sharma

Devin Kwok

Tom Denton

Daniel M. Roy

Gintare Karolina Dziugaite

2024-04-09

ArXiv (preprint)

Application-Driven Innovation in Machine Learning

Alán Aspuru-Guzik

Sara Beery

Bistra N. 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 Tramer

2024-03-11

ArXiv (preprint)

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

Gintare Karolina Dziugaite

2024-03-08

ICLR.cc/2024/Workshop_Proposals (published)

openreview.net

Dataset Difficulty and the Role of Inductive Bias

Devin Kwok

Nikhil Anand

Jonathan Frankle

Motivated by the goals of dataset pruning and defect identification, a growing body of methods have been developed to score individual examp… (see more)les within a dataset. These methods, which we call"example difficulty scores", are typically used to rank or categorize examples, but the consistency of rankings between different training runs, scoring methods, and model architectures is generally unknown. To determine how example rankings vary due to these random and controlled effects, we systematically compare different formulations of scores over a range of runs and model architectures. We find that scores largely share the following traits: they are noisy over individual runs of a model, strongly correlated with a single notion of difficulty, and reveal examples that range from being highly sensitive to insensitive to the inductive biases of certain model architectures. Drawing from statistical genetics, we develop a simple method for fingerprinting model architectures using a few sensitive examples. These findings guide practitioners in maximizing the consistency of their scores (e.g. by choosing appropriate scoring methods, number of runs, and subsets of examples), and establishes comprehensive baselines for evaluating scores in the future.

2024-01-03

ArXiv (preprint)

A landmark environmental law looks ahead

Robert L. Fischman

J. B. Ruhl

Brenna R. Forester

Tanya M. Lama

Marty Kardos

Grethel Aguilar Rojas

Nicholas A. Robinson

Patrick D. Shirey

Gary A. Lamberti

Amy W. Ando

Stephen Palumbi

Michael Wara

Mark W. Schwartz

Matthew A. Williamson

Tanya Berger-Wolf

Sara Beery

Justin Kitzes

David Thau

Devis Tuia … (see 8 more)

Daniel Rubenstein

Caleb R. Hickman

Julie Thorstenson

Gregory E. Kaebnick

James P. Collins

Athmeya Jayaram

Thomas Deleuil

Ying Zhao

2023-12-22

Science (published)

FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models

Nikolaos Ioannis Bountos

Arthur Ouaknine

2023-12-15

ArXiv (preprint)

Towards Causal Representations of Climate Model Data

Julien Boussard

Chandni Nagda

Julia Kaltenborn

Charlotte Emilie Elektra Lange

Philippe Brouillard

Yaniv Gurwicz

Peer Nowack

Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic… (see more) Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learning-based emulators trained on existing simulation data can project additional climate scenarios much faster and are computationally efficient. However, they often lack generalizability and interpretability. This work delves into the potential of causal representation learning, specifically the \emph{Causal Discovery with Single-parent Decoding} (CDSD) method, which could render climate model emulation efficient \textit{and} interpretable. We evaluate CDSD on multiple climate datasets, focusing on emissions, temperature, and precipitation. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.

2023-12-05

ArXiv (preprint)

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Hager Radi

Hugo Larochelle

Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary to ensure food, water, and human health and well-… (see more)being. Understanding the distribution of species and their habitats is crucial for conservation policy planning. However, traditional methods in ecology for species distribution models (SDMs) generally focus either on narrow sets of species or narrow geographical areas and there remain significant knowledge gaps about the distribution of species. A major reason for this is the limited availability of data traditionally used, due to the prohibitive amount of effort and expertise required for traditional field monitoring. The wide availability of remote sensing data and the growing adoption of citizen science tools to collect species observations data at low cost offer an opportunity for improving biodiversity monitoring and enabling the modelling of complex ecosystems. We introduce a novel task for mapping bird species to their habitats by predicting species encounter rates from satellite images, and present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird, considering summer (breeding) and winter seasons. We also provide a dataset in Kenya representing low-data regimes. We additionally provide environmental data and species range maps for each location. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks. SatBird opens up possibilities for scalably modelling properties of ecosystems worldwide.

2023-11-02

ArXiv (preprint)

OpenForest: A data catalogue for machine learning in forest monitoring

Arthur Ouaknine

Teja Kattenborn

Etienne Lalibert'e

2023-11-01

ArXiv (preprint)

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

Alvaro Carbonero

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

Alex Hernandez-Garcia

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (see more)ate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (poster)