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 researcher - Cambridge University

Co-supervisor :

julien.boussard@mila.quebec

berkes.anais@mila.quebec

Postdoctorate - McGill University

Michael Bunsen

Collaborating researcher - McGill University

michael.bunsen@mila.quebec

Shahana Chatterjee

Collaborating researcher - N/A

Co-supervisor :

shahana.chatterjee@mila.quebec

Yuyan Chen

PhD - McGill University

yuyan.chen@mila.quebec

Graham Clyne

Postdoctorate - McGill University

graham.clyne@mila.quebec

Ruth Crasto

PhD - McGill University

ruth.crasto@mila.quebec

othmane.echchabi@mila.quebec

Othmane Echchabi

Master's Research - McGill University

Collaborating researcher

amna.elmustafa@mila.quebec

mohamed.elabbas@mila.quebec

Mohamed Elabbas

Collaborating researcher

jannik.endres@mila.quebec

Jannik Endres

Collaborating researcher

jacopo.ghirri@mila.quebec

Jacopo Ghirri

Independent visiting researcher - Politecnico di Milano

Independent visiting researcher

paula.harder@mila.quebec

Collaborating researcher - Johannes Kepler University

christina.humer@mila.quebec

Gaurav Iyer

Master's Research - McGill University

gaurav.iyer@mila.quebec

julia.kaltenborn@mila.quebec

Julia Kaltenborn

PhD - McGill University

vincent.lefalher@mila.quebec

Devin Kwok

PhD - McGill University

devin.kwok@mila.quebec

Independent visiting researcher - Université de Montréal

Pierre-Louis Lemaire

Collaborating researcher - Polytechnique Montréal Montréal

Principal supervisor :

Alex Hernández-García

pierre-louis.lemaire@mila.quebec

david.mickisch@mila.quebec

David Mickisch

Collaborating researcher

Postdoctorate - McGill University

Co-supervisor :

Étienne Laliberté

arthur.ouaknine@mila.quebec

Lena Podina

Collaborating researcher - University of Waterloo

Co-supervisor :

elena.podina@mila.quebec

Marlena Reil

Master's Research - McGill University

marlena.reil@mila.quebec

Rasha Saha

Master's Research - McGill University

sulagna.saha@mila.quebec

luca.schmidt@uni-tuebingen.de

Luca Marie Schmidt

Collaborating researcher - University of Tübingen

Independent visiting researcher - Karlsruhe Institute of Technology

hossein.shirali@mila.quebec

Wietze Suijker

Independent visiting researcher

wietze.suijker@gmail.com

ilija.trajkovic@mila.quebec

Ilija Trajković

Collaborating researcher - Karlsruhe Institute of Technology

gabriel.tseng@mila.quebec

Gabriel Tseng

PhD - McGill University

Collaborating Alumni - Université de Montréal

Principal supervisor :

donna.vakalis@mila.quebec

Collaborating researcher

anna.viklund@mila.quebec

catherine.villeneuve@mila.quebec

Catherine Villeneuve

PhD - McGill University

mohamed.yebari@mila.quebec

Mohamed Yebari

Collaborating researcher - Ecole Polytechnique Montréal Fédérale de Lausanne (EPFL)

Co-supervisor :

Loubna Benabbou

Shan Zhao

Collaborating researcher - Technical University of Munich

zhaoshanuestc@gmail.com

Democratizing Access to Satellite Data with AI

Blog Posts

diagram illustrating how the AI foundation model for Earth observation, Galileo, works

October 21, 2025

Gabriel Tseng

David Rolnick

Read the article

Publications

Causal Climate Emulation with Bayesian Filtering

Alex Archibald

Yaniv Gurwicz

Peer Nowack

Julien Boussard

Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These … (see more)simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physically-based causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a novel approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.

2025-12-02

Neural Information Processing Systems (Accept (poster))

Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring

Yuyan Chen

Nico Lang

B. Christian Schmidt

Aditya Jain

Yves Basset

Sara Beery

Maxim Larrivée

Global biodiversity is declining at an unprecedented rate, yet little information is known about most species and how their populations are … (see more)changing. Indeed, some 90% of Earth's species are estimated to be completely unknown. Machine learning has recently emerged as a promising tool to facilitate long-term, large-scale biodiversity monitoring, including algorithms for fine-grained classification of species from images. However, such algorithms typically are not designed to detect examples from categories unseen during training -- the problem of open-set recognition (OSR) -- limiting their applicability for highly diverse, poorly studied taxa such as insects. To address this gap, we introduce Open-Insect, a large-scale, fine-grained dataset to evaluate unknown species detection across different geographic regions with varying difficulty. We benchmark 38 OSR algorithms across three categories: post-hoc, training-time regularization, and training with auxiliary data, finding that simple post-hoc approaches remain a strong baseline. We also demonstrate how to leverage auxiliary data to improve species discovery in regions with limited data. Our results provide insights to guide the development of computer vision methods for biodiversity monitoring and species discovery.

2025-12-02

Conference on Neural Information Processing Systems (Accept (spotlight))

Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal

Christina Butsko

Gabriel Tseng

Kristof Van Tricht

Giorgia Milli

Ruben Cartuyvels

Inbal Becker Reshef

Zoltan Szantoi

Hannah Kerner

The increasing availability of geospatial foundation models has the potential to transform remote sensing applications such as land cover cl… (see more)assification, environmental monitoring, and change detection. Despite promising benchmark results, the deployment of these models in operational settings is challenging and rare. Standardized evaluation tasks often fail to capture real-world complexities relevant for end-user adoption such as data heterogeneity, resource constraints, and application-specific requirements. This paper presents a structured approach to integrate geospatial foundation models into operational mapping systems. Our protocol has three key steps: defining application requirements, adapting the model to domain-specific data and conducting rigorous empirical testing. Using the Presto model in a case study for crop mapping, we demonstrate that fine-tuning a pre-trained model significantly improves performance over conventional supervised methods. Our results highlight the model’s strong spatial and temporal generalization capabilities. Our protocol provides a replicable blueprint for practitioners and lays the groundwork for future research to operationalize foundation models in diverse remote sensing applications. Application of the protocol to the WorldCereal global crop-mapping system showcases the framework’s scalability.

2025-12-01

Proceedings of The TerraBytes {ICML} Workshop: Towards global datasets and models for Earth Observation (published)

proceedings.mlr.press

On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling

Paula Harder

Christian Lessig

Matthew Chantry

Francis Pelletier

2025-11-30

ArXiv (preprint)

arxiv.org

A HOT Dataset: 150,000 Buildings for HVAC Operations Transfer Research

Anaïs Berkes

About 12% of global energy consumption is attributable to heating, ventilation, and air conditioning (HVAC) systems in buildings [11]. Machi… (see more)ne learning-based intelligent HVAC control offers significant energy efficiency potential, but progress is constrained by limited data for training and evaluating performance across different kinds of buildings. Existing datasets primarily target energy prediction rather than control applications, forcing studies to rely on limited building sets or single-variable perturbations that fail to capture real-world complexity. We present HOT (HVAC Operations Transfer), the first large-scale open-source dataset purpose-built for research into transfer learning in building control. HOT contains 159,744 unique building-weather combinations with systematic variations across envelope properties, occupancy patterns, and climate conditions spanning all 19 ASHRAE climate zones across 76 global locations. We formalise a comprehensive similarity-based framework with quantitative metrics for assessing transfer feasibility between source and target buildings across multiple context dimensions. Our key contributions: (1) a large-scale, open dataset and tooling enabling systematic, multi-variable transfer studies across 19 climate zones; (2) a quantitative similarity framework spanning geometry, thermal, climate, and function; and (3) zero-shot climate transfer experiments showing why realistic context variation matters for HVAC control.

2025-11-10

ACM Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (published)

A HOT Dataset: 150,000 Buildings for HVAC Operations Transfer Research

Anaïs Berkes

2025-11-10

Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (published)

Galileo: Learning Global&Local Features of Many Remote Sensing Modalities

Gabriel Tseng

Anthony Fuller

Marlena Reil

Henry Herzog

Patrick Beukema

Favyen Bastani

James R Green

Evan Shelhamer

Hannah Kerner

We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, … (see more)elevation, weather, pseudo-labels, and more - across space and time. These inputs are useful for diverse remote sensing tasks, such as crop mapping and flood detection. However, learning shared representations of remote sensing data is challenging, given the diversity of relevant data modalities, and because objects of interest vary massively in scale, from small boats (1-2 pixels and fast) to glaciers (thousands of pixels and slow). We present a novel self-supervised learning algorithm that extracts multi-scale features across a flexible set of input modalities through masked modeling. Our dual global and local contrastive losses differ in their targets (deep representations vs. shallow input projections) and masking strategies (structured vs. not). Our Galileo is a single generalist model that outperforms SoTA specialist models for satellite images and pixel time series across eleven benchmarks and multiple tasks.

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions

Devin Kwok

Gül Sena Altıntaş

Colin Raffel

Neural network training is inherently sensitive to initialization and the randomness induced by stochastic gradient descent. However, it is … (see more)unclear to what extent such effects lead to meaningfully different networks, either in terms of the models' weights or the underlying functions that were learned. In this work, we show that during the initial "chaotic" phase of training, even extremely small perturbations reliably causes otherwise identical training trajectories to diverge-an effect that diminishes rapidly over training time. We quantify this divergence through (i)

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

HVAC-SPICE: Value-Uncertainty In-Context RL with Thompson Sampling for Zero-Shot HVAC Control

Anaïs Berkes

Urban buildings consume 40\% of global energy, yet most rely on inefficient rule-based HVAC systems due to the impracticality of deploying a… (see more)dvanced controllers across diverse building stock. In-context reinforcement learning (ICRL) offers promise for rapid deployment without per-building training, but standard supervised learning objectives that maximise likelihood of training actions inherit behaviour-policy bias and provide weak exploration under the distribution shifts common when transferring across buildings and climates. We present SPICE (Sampling Policies In-Context with Ensemble uncertainty), a novel ICRL method specifically designed for zero-shot building control that addresses these fundamental limitations. SPICE introduces two key methodological innovations: (i) a propensity-corrected, return-aware training objective that prioritises high-advantage, high-uncertainty actions to enable improvement beyond suboptimal training demonstrations, and (ii) lightweight value ensembles with randomised priors that provide explicit uncertainty estimates for principled episode-level Thompson sampling. At deployment, SPICE samples one value head per episode and acts greedily, resulting in temporally coherent exploration without test-time gradients or building-specific models. We establish a comprehensive experimental protocol using the HOT dataset to evaluate SPICE across diverse building types and climate zones, focusing on the energy efficiency, occupant comfort, and zero-shot transfer capabilities that are critical for urban-scale deployment.

2025-09-29

NeurIPS.cc/2025/Workshop/UrbanAI (poster)

Graph Dreamer: Temporal Graph World Models for Sample-Efficient and Generalisable Reinforcement Learning

Anaïs Berkes

2025-09-21

NeurIPS.cc/2025/Workshop/WiML (published)

GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction

Eya Cherif

Arthur Ouaknine

Luke A. Brown

Phuong D. Dao

Kyle R. Kovach

Bing Lu

Daniel Mederer

Hannes Feilhauer

Teja Kattenborn

Plant traits such as leaf carbon content and leaf mass are essential variables in the study of biodiversity and climate change. However, con… (see more)ventional field sampling cannot feasibly cover trait variation at ecologically meaningful spatial scales. Machine learning represents a valuable solution for plant trait prediction across ecosystems, leveraging hyperspectral data from remote sensing. Nevertheless, trait prediction from hyperspectral data is challenged by label scarcity and substantial domain shifts (\eg across sensors, ecological distributions), requiring robust cross-domain methods. Here, we present GreenHyperSpectra, a pretraining dataset encompassing real-world cross-sensor and cross-ecosystem samples designed to benchmark trait prediction with semi- and self-supervised methods. We adopt an evaluation framework encompassing in-distribution and out-of-distribution scenarios. We successfully leverage GreenHyperSpectra to pretrain label-efficient multi-output regression models that outperform the state-of-the-art supervised baseline. Our empirical analyses demonstrate substantial improvements in learning spectral representations for trait prediction, establishing a comprehensive methodological framework to catalyze research at the intersection of representation learning and plant functional traits assessment. All code and data are available at: https://github.com/echerif18/HyspectraSSL.

2025-09-17

NeurIPS.cc/2025/Datasets_and_Benchmarks_Track (poster)

CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations

Hager Radi Abdelwahed

Mélisande Teng

Robin Zbinden

Laura Pollock

Hugo Larochelle

Devis Tuia

Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological re… (see more)search and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.

2025-08-07

ArXiv (preprint)