Portrait of David Rolnick

David Rolnick

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, McGill University, School of Computer Science
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Machine Learning Theory

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

Master's Research - McGill University
Collaborating Alumni - Université de Montréal
Collaborating researcher - The University of Dresden, Helmholtz Centre for Environmental Research Leipzig
Collaborating researcher
Collaborating researcher - National Observatory of Athens
Collaborating researcher
Collaborating researcher - McGill University
Collaborating researcher - KU Leuven
Master's Research - McGill University
Research Intern
Collaborating Alumni
Collaborating researcher - Université Paris-Saclay
Co-supervisor :
Collaborating researcher
Collaborating researcher
Postdoctorate - Université de Montréal
Principal supervisor :
Collaborating researcher - Université de Montréal
Research Intern - Johannes Kepler University
Master's Research - McGill University
PhD - McGill University
PhD - McGill University
Collaborating researcher - University of Waterloo
Collaborating researcher
Collaborating researcher
Research Intern - Université de Montréal
Postdoctorate - McGill University
Co-supervisor :
PhD - University of Waterloo
Co-supervisor :
PhD - Université de Montréal
Collaborating researcher
Master's Research - Osnabrueck university
Collaborating researcher
Co-supervisor :
PhD - McGill University
Collaborating researcher
Co-supervisor :
Collaborating researcher - Karlsruhe Institute of Technology
PhD - McGill University
Postdoctorate - Université de Montréal
Principal supervisor :
Collaborating researcher
Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)
Co-supervisor :
PhD - McGill University
Postdoctorate - McGill University
Collaborating researcher
Collaborating researcher

Publications

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.
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Alexandre AGM Duval
Victor Schmidt
Santiago Miret
Alex Hernandez-Garcia
Simultaneous linear connectivity of neural networks modulo permutation
Ekansh Sharma
Devin Kwok
Tom Denton
Daniel M. Roy
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
FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models
Nikolaos Ioannis Bountos
Arthur Ouaknine
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.
SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data
Mélisande Teng
Amna Elmustafa
Benjamin Akera
Hager Radi
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.
OpenForest: A data catalogue for machine learning in forest monitoring
Arthur Ouaknine
Teja Kattenborn
Etienne Lalibert'e
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.
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Julia Kaltenborn
Charlotte Emilie Elektra Lange
Venkatesh Ramesh
Philippe Brouillard
Yaniv Gurwicz
Chandni Nagda
Jakob Runge
Peer Nowack
Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) c… (see more)ommunity has taken an increased interest in supporting climate scientists’ efforts on various tasks such as climate model emulation, downscaling, and prediction tasks. Many of those tasks have been addressed on datasets created with single climate models. However, both the climate science and ML communities have suggested that to address those tasks at scale, we need large, consistent, and ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset containing the inputs and outputs of 36 climate models from the Input4MIPs and CMIP6 archives. In addition, we provide a modular dataset pipeline for retrieving and preprocessing additional climate models and scenarios. We showcase the potential of our dataset by using it as a benchmark for ML-based climate model emulation. We gain new insights about the performance and generalization capabilities of the different ML models by analyzing their performance across different climate models. Furthermore, the dataset can be used to train an ML emulator on several climate models instead of just one. Such a “super-emulator” can quickly project new climate change scenarios, complementing existing scenarios already provided to policymakers. We believe ClimateSet will create the basis needed for the ML community to tackle climate-related tasks at scale.
SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data
Mélisande Teng
Amna Elmustafa
Benjamin Akera
Hager Radi
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Jose Gonz'alez-Abad
'Alex Hern'andez-Garc'ia
Paula Harder
Jos'e Manuel Guti'errez