Environment and Energy

Around the world, people are being increasingly affected by climate change. Through thoughtful interdisciplinary partnerships, artificial intelligence (AI) can become a powerful tool in reducing greenhouse gas emissions, while also helping society adapt to the many climate-based challenges it will face in the decades ahead.

When it comes to tackling the climate crisis, AI can help distill raw data into useful information, such as using satellite imagery to automatically monitor biodiversity or forest carbon. AI can also help optimize complex systems by reducing the energy required for heating and cooling buildings, and improving the efficiency of electrical grids. In addition, AI can improve forecasting by predicting agricultural productivity in response to extreme weather.

AI can also accelerate the integration of scientific modeling and discovery in everything from accelerated climate modeling to the design of new materials for renewable energy. Through research initiatives spanning a wide range of environmental and energy-related issues, Mila researchers are using their expertise to help build a more sustainable future for us all.

Featured Projects

A bright yellow-orange moth stands on a leaf.

Antenna

Antenna is helping scientists study insect populations worldwide, informing responses to climate and biodiversity crises.

Learn more

AI-generated image of a flood in front of Mila's office.

This Climate Does Not Exist

This Climate Does Not Exist is an AI-driven experience based on empathy, allowing users to imagine the environmental impacts of the current climate crisis, one address at a time.

Learn more

AI can help tackle the climate crisis on many levels, including mapping ecosystems, monitoring change, and supporting the development of materials to improve energy efficiency.

David Rolnick, Assistant Professor, McGill University, Core Academic Member, Mila

Resources

Tackling Climate Change with Machine Learning

This report describes how machine learning can contribute to climate change mitigation and adaptation.

Read the report https://dl.acm.org/doi/full/10.1145/3485128

Climate Change AI (CCAI)

CCAI is an organization that aims to catalyze impactful work at the intersection of climate change and machine learning.

Learn more https://www.climatechange.ai/

Climate Change and AI: Recommendations for Government Action

Report by the Global Partnership on AI (GPAI), published in collaboration with Climate Change AI and the Center for AI & Climate.

Read the report https://www.gpai.ai/projects/climate-change-and-ai.pdf

Aligning Artificial Intelligence with Climate Change Mitigation

Published in Nature Climate Change

Read the article https://www.nature.com/articles/s41558-022-01377-7

In the Media

Canadian researchers using machine learning to mitigate effects of climate change (CBC)

Read the article https://www.cbc.ca/news/science/ai-machine-learning-climate-change-1.6561790

This website helps you imagine what extreme climate change will do to your home (CNN)

Read the article https://www.cnn.com/2021/10/14/tech/ai-envisions-climate-change/index.html

Aerial view of a boreal forest and river in summer.

Research Labs

Mila professors exploring the subject as part of their research.

Mila Faculty

Core Academic Member

Pierre-Luc Bacon

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Canada CIFAR AI Chair

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Affiliate Member

Giovanni Beltrame

Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

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Core Academic Member

Yoshua Bengio

Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department

Canada CIFAR AI Chair

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Core Academic Member

Glen Berseth

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Canada CIFAR AI Chair

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Affiliate Member

Quentin Cappart

Associate Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

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Affiliate Member

Jeff Cardille

Associate Professor, McGill University, Department of Natural Resource Sciences

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Associate Academic Member

Margarida Carvalho

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

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Core Academic Member

Laurent Charlin

Associate Professor, HEC Montréal, Department of Decision Sciences

Canada CIFAR AI Chair

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Associate Academic Member

Hanane Dagdougui

Full Professor, Polytechnique Montréal, Department of Mathematical and Industrial Engineering

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Affiliate Member

Samira Ebrahimi Kahou

Assistant Professor, University of Calgary, Deparment of Electrical and Software Engineering

Canada CIFAR AI Chair

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Core Academic Member

Amir-massoud Farahmand

Associate Professor, Polytechnique Montréal

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Associate Academic Member

Christian Gagné

Full Professor, Université Laval, Department of Electrical and Computer Engineering

Canada CIFAR AI Chair

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Core Industry Member

Ross Goroshin

Adjunct professor, Université de Montréal, Department of Computer Science and Operations Research

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Core Academic Member

Alex Hernandez (Upcoming member)

Assistant Professor, Université de Montréal

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Associate Academic Member

Toby Dylan Hocking

Associate Professor, Université Sherbrooke, Department of Computer Science

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Affiliate Member

Shin (Alexandre) Koseki

Assistant Professor, Université de Montréal, School of Urban Planning and Landscape Architecture

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Associate Academic Member

Étienne Laliberté

Full Professor, Université de Montréal, Department of Biological Sciences

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Core Industry Member

Hugo Larochelle

Research Scientist, Google DeepMind

Canada CIFAR AI Chair

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Associate Academic Member

Antoine Lesage-Landry

Associate Professor, Polytechnique Montréal, Department of Electrical Engineering

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Associate Academic Member

Xue (Steve) Liu

Full Professor, McGill University, School of Computer Science

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Core Academic Member

Tegan Maharaj

Assistant Professor in Machine Learning, HEC Montréal, Department of Decision Science

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Affiliate Member

Vladimir Makarenkov

Full Professor, UQAM, Department of Computer Science

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Associate Academic Member

David Meger

Associate Professor, McGill University, School of Computer Science

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Associate Academic Member

Borke Obada-Obieh

Assistant Professor, McGill University, School of Computer Science

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Core Academic Member

Chris Pal

Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

Canada CIFAR AI Chair

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Affiliate Member

Marco Pedersoli

Associate Professor, École de technologie suprérieure

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Core Academic Member

Reihaneh Rabbany

Assistant Professor, McGill University, School of Computer Science

Canada CIFAR AI Chair

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Core Academic Member

Siamak Ravanbakhsh

Assistant Professor, McGill University, School of Computer Science

Canada CIFAR AI Chair

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Associate Academic Member

Mirco Ravanelli

Assistant Professor, Concordia University, Gina Cody School of Engineering and Computer Science

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Core Academic Member

David Rolnick

Assistant Professor, McGill University, School of Computer Science

Canada CIFAR AI Chair

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Featured Video

Hear Prof. David Rolnick introduce the Antenna project from the tropical rainforest in Panama.

Publications

Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes

Aleksandar Jakovljevic

Laurent Charlin

Benoit Barbeau

The jar test is the current standard method for predicting the performance of a conventional drinking water treatment (DWT) process and opti… (see more)mizing the coagulant dose. This test is time-consuming and requires human intervention, meaning it is infeasible for making continuous process predictions. As a potential alternative, we developed a machine learning (ML) model from historical DWT plant data that can operate continuously using real-time sensor data without human intervention for predicting clarified water turbidity 15 min in advance. We evaluated three types of models: multilayer perceptron (MLP), the long short-term memory (LSTM) recurrent neural network (RNN), and the gated recurrent unit (GRU) RNN. We also employed two training methodologies: the commonly used holdout method and the theoretically correct blocked cross-validation (BCV) method. We found that the RNN with GRU was the best model type overall and achieved a mean absolute error on an independent production set of as low as 0.044 NTU. We further found that models trained using BCV typically achieve errors equal to or lower than their counterparts trained using holdout. These results suggest that RNNs trained using BCV are superior for the development of ML models for DWT processes compared to those reported in earlier literature.

2024-04-04

Water (published)

doi.org

Application-Driven Innovation in Machine Learning

David Rolnick

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.

2024-01-01

ICML (published)

doi.org

arxiv.org

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