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Tackling Climate Change with ML
David Dao · Evan Sherwin · Priya Donti · Lauren Kuntz · Lynn Kaack · Yumna Yusuf · David Rolnick · Catherine Nakalembe · Claire Monteleoni · Yoshua Bengio

Fri Dec 11 03:00 AM -- 04:00 PM (PST) @ None
Event URL: https://www.climatechange.ai/events/neurips2020 »

Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. Since climate change is a complex issue, action takes many forms, from designing smart electric grids to tracking greenhouse gas emissions through satellite imagery. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques. These applications require algorithmic innovations in machine learning and close collaboration with diverse fields and practitioners. This workshop is intended as a forum for those in the machine learning community who wish to help tackle climate change. Building on our past workshops on this topic, this workshop aims to especially emphasize the pipeline to impact, through conversations about machine learning with decision-makers and other global leaders in implementing climate change strategies. The all-virtual format of NeurIPS 2020 provides a special opportunity to foster cross-pollination between researchers in machine learning and experts in complementary fields.

Fri 3:00 a.m. - 3:35 a.m.
Welcome and opening remarks (Introductory remarks)
Fri 4:00 a.m. - 4:05 a.m.
Introduction to Spotlights (Live introduction)
Fri 4:05 a.m. - 4:15 a.m.

Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.

Michael Steininger
Fri 4:15 a.m. - 4:22 a.m.

While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries.

Kevin Mayer
Fri 4:22 a.m. - 4:32 a.m.

Natural disasters ravage the world's cities, valleys, and shores on a monthly basis. Having precise and efficient mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we train multiple convolutional neural networks to assess building damage on a per-building basis. In order to investigate how to best classify building damage, we present a highly interpretable deep-learning methodology that seeks to explicitly convey the most useful information required to train an accurate classification model. We also delve into which loss functions best optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal loss function to use and that including the type of disaster that caused the damage in combination with a pre- and post-disaster image best predicts the level of damage caused. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by climate change.

Thomas Chen
Fri 4:32 a.m. - 4:42 a.m.
Reducing methane emissions from the oil and gas sector is a key component of climate policy in the United States. Methane leaks across the supply chain are stochastic and intermittent, with a small number of sites (‘super-emitters’) responsible for a majority of emissions. Thus, cost-effective emissions reduction critically relies on effectively identifying the super-emitters from thousands of well-sites and millions of miles of pipelines. Conventional approaches such as walking surveys using optical gas imaging technology are slow and time-consuming. In addition, several variables contribute to the formation of leaks such as infrastructure age, production, weather conditions, and maintenance practices. Here, we develop a machine learning algorithm to predict high-emitting sites that can be prioritized for follow-up repair. Such prioritization can significantly reduce the cost of surveys and increase emissions reductions compared to conventional approaches. Our results show that the algorithm using logistic regression performs the best out of several algorithms. The model achieved a 70% accuracy rate with a 57% recall and a 66% balanced accuracy rate. Compared to the conventional approach, the machine learning model reduced the time to achieve a 50% emissions mitigation target by 42%. Correspondingly, the mitigation cost reduced from $85/t CO2e to $49/t CO2e.
Jiayang Wang
Fri 4:42 a.m. - 4:52 a.m.

Climate change is expected to aggravate extreme precipitation events, directly impacting the livelihood of millions. Without a global precipitation forecasting system in place, many regions -- especially those constrained in resources to collect expensive groundstation data -- are left behind. To mitigate such unequal reach of climate change, a solution is to alleviate the reliance on numerical models (and by extension groundstation data) by enabling machine-learning-based global forecasts from satellite imagery. Though prior works exist in regional precipitation nowcasting, there lacks work in global, medium-term precipitation forecasting. Importantly, a common, accessible baseline for meaningful comparison is absent. In this work, we present RainBench, a multi-modal benchmark dataset dedicated to advancing global precipitation forecasting. We establish baseline tasks and release PyRain, a data-handling pipeline to enable efficient processing of decades-worth of data by any modeling framework. Whilst our work serves as a basis for a new chapter on global precipitation forecast from satellite imagery, the greater promise lies in the community joining forces to use our released datasets and tools in developing machine learning approaches to tackle this important challenge.

Catherine Tong
Fri 4:52 a.m. - 5:00 a.m.
Introduction to first poster session (Live introduction)
Fri 5:00 a.m. - 6:00 a.m.
Poster session 1 (Poster session)
Fri 6:00 a.m. - 6:09 a.m.
Introduction to Spotlights (Live introduction)
Fri 6:09 a.m. - 6:19 a.m.

This paper introduces the Peruvian Amazon Forestry Dataset, which includes 59,441 leaves samples from ten of the most profitable and endangered Amazon timber-tree species. Besides, the proposal includes a background removal algorithm to feed a fine-tuned CNN. We evaluate the quantitative (accuracy metric) and qualitative (visual interpretation) impacts of each stage by ablation experiments. The results show a 96.64 % training accuracy and 96.52 % testing accuracy on the VGG-19 model. Furthermore, the visual interpretation of the model evidences that leaf venations have the highest correlation in the plant recognition task.

Gerson Vizcarra Aguilar
Fri 6:19 a.m. - 6:25 a.m.

Heating, ventilation, and air conditioning (HVAC) systems account for 30% of the total energy consumption in buildings. Design and implementation of energy-efficient schemes can play a pivotal role in minimizing energy usage. As an important first step towards improved HVAC system controls, this study proposes a new framework for modeling the thermal response of buildings by leveraging data measurements and formulating a data-driven system identification model. The proposed method combines principal component analysis (PCA) to identify the most significant predictors that influence the cooling demand of a building with an auto-regressive integrated moving average with exogenous variables (ARIMAX) model. The performance of the developed model was evaluated both analytically and visually. It was found that our PCA-based ARIMAX (2-0-5) model was able to accurately forecast the cooling demand for the prediction horizon of 7 days. In this work, the actual measurements from a university campus building are used for model development and validation.

Aqsa Naeem
Fri 6:25 a.m. - 6:37 a.m.

Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes.

Trey H McNeely
Fri 6:37 a.m. - 6:47 a.m.

With fires becoming increasingly frequent and severe across the globe in recent years, understanding climate change’s role in fire behavior is critical for quantifying current and future fire risk. However, global climate models typically simulate fire behavior at spatial scales too coarse for local risk assessments. Therefore, we propose a novel approach towards super-resolution (SR) enhancement of fire risk exposure maps that incorporates not only 2000 to 2020 monthly satellite observations of active fires but also local information on land cover and temperature. Inspired by SR architectures, we propose an efficient deep learning model trained for SR on fire risk exposure maps. We evaluate this model on resolution enhancement and find it outperforms standard image interpolation techniques at both 4x and 8x enhancement while having comparable performance at 2x enhancement. We then demonstrate the generalizability of this SR model over northern California and New South Wales, Australia. We conclude with a discussion and application of our proposed model to climate model simulations of fire risk in 2040 and 2100, illustrating the potential for SR enhancement of fire risk maps from the latest state-of-the-art climate models.

Tristan Ballard
Fri 6:47 a.m. - 6:57 a.m.

Understanding the changing distributions of butterflies gives insight into the impacts of climate change across ecosystems and is a prerequisite for conservation efforts. eButterfly is a citizen science website created to allow people to track the butterfly species around them and use these observations to contribute to research. However, correctly identifying butterfly species is a challenging task for non-specialists and currently requires the involvement of entomologists to verify the labels of novice users on the website. We have developed a computer vision model to label butterfly images from eButterfly automatically, decreasing the need for human experts. We employ a model that incorporates geographic and temporal information of where and when the image was taken, in addition to the image itself. We show that we can successfully apply this spatiotemporal model for fine-grained image recognition, significantly improving the accuracy of our classification model compared to a baseline image recognition system trained on the same dataset.

Marta Skreta
Fri 7:00 a.m. - 8:00 a.m.
Climate Change and ML for Policy (Discussion Panel)
Angel Hsu, Dava Newman, James Rattling Leaf, Sr., Mouhamadou M Cisse
Fri 8:00 a.m. - 9:00 a.m.
Poster session 2 (Poster session)
Fri 9:00 a.m. - 9:10 a.m.
Introduction to Zico Kolter (Live introduction)
Fri 9:40 a.m. - 10:00 a.m.
Q&A with Zico Kolter (Live Q&A)
Fri 10:00 a.m. - 11:00 a.m.
Climate Change and ML in the Private Sector (Discussion Panel)
Aisha Walcott-Bryant, Lea Boche, Anima Anandkumar
Fri 11:00 a.m. - 11:05 a.m.
Introduction to Spotlights (Live introduction)
Fri 11:05 a.m. - 11:15 a.m.

Glacier mapping is key to ecological monitoring in the Hindu Kush Himalaya region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and correct model predictions, with the ultimate aim of accelerating the glacier mapping process.

Kris Sankaran
Fri 11:15 a.m. - 11:25 a.m.

Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke. Here we show how machine learning, such as spectral clustering and manifold learning, can provide interpretable representations and powerful tools for differentiating between smoke types, hence providing forest managers with vital information on effective strategies to reduce climate-induced wildfires while minimizing production of harmful smoke.

Lorenzo Tomaselli
Fri 11:25 a.m. - 11:36 a.m.

At least a quarter of the warming that the Earth is experiencing today is due to anthropogenic methane emissions. There are multiple satellites in orbit and planned for launch in the next few years which can detect and quantify these emissions; however, to attribute methane emissions to their sources on the ground, a comprehensive database of the locations and characteristics of emission sources worldwide is essential. In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions. We use the best algorithm, which we call OGNet, together with expert review to identify the locations of oil refineries and petroleum terminals in the U.S. We show that OGNet detects many facilities which are not present in four standard public datasets of oil and gas infrastructure. All detected facilities are associated with characteristics critical to quantifying and attributing methane emissions, including the types of infrastructure and number of storage tanks. The data curated and produced in this study is freely available at https://link/provided/in/camera/ready/version.

Hao Sheng
Fri 11:36 a.m. - 11:45 a.m.

The effect of extreme temperatures, precipitation and variations in other meteorological factors affect crop yields, and hence climate change jeopardizes the entire food supply chain and dependent economic activities. We utilize Deep Neural Networks and Gaussian Processes for understanding crop yields as functions of climatological variables, and use change detection techniques to identify climatological thresholds where yield drops significantly.

Somya Sharma
Fri 11:45 a.m. - 11:55 a.m.

Greenhouse gases emitted from fossil-fuel-burning power plants are a major contributor to climate change. Current methods to track emissions from individual sources are expensive and only used in a few countries. While carbon dioxide concentrations can be measured globally using remote sensing, direct methods do not provide sufficient spatial resolution to distinguish emissions from different sources. We use machine learning to infer power generation and emissions from visible and thermal power plant signatures in satellite images. By training on a data set of power plants for which we know the generation or emissions, we are able to apply our models globally. This paper demonstrates initial progress on this project by predicting whether a power plant is on or off from a single satellite image.

Heather Couture
Fri 12:00 p.m. - 1:00 p.m.
Poster session 3 (Poster session)
Fri 1:00 p.m. - 1:10 p.m.
Introduction to Jennifer Chayes (Live introduction)
Fri 1:40 p.m. - 2:00 p.m.
Q&A with Jennifer Chayes (Live Q&A)
Fri 2:50 p.m. - 3:15 p.m.
Closing remarks
Fri 3:15 p.m. - 4:00 p.m.
Poster reception (Poster session)
Electric Vehicle Range Improvement by Utilizing Deep Learning to Optimize Occupant Thermal Comfort (Poster)   
Alok Warey
Explaining Complex Energy Systems: A Challenge (Poster)   
Jonas Hülsmann
Is Africa leapfrogging to renewables or heading for carbon lock-in? A machine-learning-based approach to predicting success of power-generation projects (Poster)   
Galina Alova
pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research (Poster)   
Gonzague Henri
Optimal District Heating in China with Deep Reinforcement Learning (Poster)   
Adrien Le Coz
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs (Poster)   
Alex Kell
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models (Poster)   
Eric Zelikman
The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning (Poster)
Kyle Tilbury
A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications (Poster)   
Quentin Paletta
Characterization of Industrial Smoke Plumes from Remote Sensing Data (Poster)   
Michael Mommert
A Way Toward Low-Carbon Shipping: Improving Port Operations Planning using Machine Learning (Poster)   
Sara El Mekkaoui
Learning the distribution of extreme precipitation from atmospheric general circulation model variables (Poster)   
Philipp Hess
Spatio-Temporal Learning for Feature Extraction inTime-Series Images (Poster)
Gael Kamdem De Teyou
Meta-modeling strategy for data-driven forecasting (Poster)   
Dominic Skinner
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters (Poster)
Chris Briggs
Short-term prediction of photovoltaic power generation using Gaussian process regression (Poster)   
Yahya Al Lawati
Leveraging Machine learning for Sustainable and Self-sufficient Energy Communities (Poster)   
Anthony Faustine
Formatting the Landscape: Spatial conditional GAN for varying population in satellite imagery (Poster)   
Tomas Langer
Storing Energy with Organic Molecules: Towards a Metric for Improving Molecular Performance for Redox Flow Batteries (Poster)   
Luis Martin Mejia Mendoza
Predicting Landsat Reflectance with Deep Generative Fusion (Poster)   
Shahine Bouabid
Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter (Poster)   
Beichen Zhang
Estimating Forest Ground Vegetation Cover From Nadir Photographs Using Deep Convolutional Neural Networks (Poster)   
Martin Barczyk
Hyperspectral Remote Sensing of Aquatic Microbes to Support Water Resource Management (Poster)   
Grace Kim
HECT: High-Dimensional Ensemble Consistency Testing for Climate Models (Poster)   
Nic Dalmasso
Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a proposed general purpose sensor-fusion semantic embedding model (Poster)   
Lucas Kruitwagen
Monitoring the Impact of Wildfires on Tree Species with Deep Learning (Poster)   
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery (Poster)   
Jeremy Irvin
Monitoring Shorelines via High-Resolution Satellite Imagery and Deep Learning (Poster)
Venkatesh Ramesh
Mangrove Ecosystem Detection using Mixed-Resolution Imagery with a Hybrid-Convolutional Neural Network (Poster)   
Dillon Hicks
Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models (Poster)   
Ben Choi
Deep learning architectures for inference of AC-OPF solutions (Poster)   
Thomas Falconer
Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data (Poster)   
Daniel de Barros Soares
Revealing the Oil Majors' Adaptive Capacity to the Energy Transition with Deep Multi-Agent Reinforcement Learning (Poster)   
Dylan Radovic
Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution (Poster)   
Matteo Bohm
Annual and in-season mapping of cropland at field scale with sparse labels (Poster)   
Gabriel Tseng
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations (Poster)   
Paula Harder
Analyzing Sustainability Reports Using Natural Language Processing (Poster)   
Sasha Luccioni
Automated Identification of Oil Field Features using CNNs (Poster)
Using attention to model long-term dependencies in occupancy behavior (Poster)   
Max Kleinebrahm
Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation (Poster)   
Veda Sunkara
Accurate river level predictions using a Wavenet-like model (Poster)   
Shannon Doyle
Graph Neural Networks for Improved El Ni√±o Forecasting (Poster)   
Salva Rühling Cachay
Movement Tracks for the Automatic Detection of Fish Behavior in Videos (Poster)   
Declan GD McIntosh
Residue Density Segmentation for Monitoring and Optimizing Tillage Practices (Poster)   
Jennifer Hobbs
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery (Poster)   
Issam Hadj Laradji
Machine learning for advanced solar cell production: adversarial denoising, sub-pixel alignment and the digital twin (Poster)   
Matthias Demant
Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics (Poster)   
Jan Drgona
Machine Learning Informed Policy for Environmental Justice in Atlanta with Climate Justice Implications (Poster)
Lelia Hampton
Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse (Poster)
Ancil Crayton
FlowDB: A new large scale river flow, flash flood, and precipitation dataset (Poster)   
Isaac Godfried
Can Federated Learning Save The Planet ? (Poster)   
Xinchi Qiu
A Multi-source, End-to-End Solution for Tracking Climate Change Adaptation in Agriculture (Poster)
Alejandro Coca-Castro
Satellite imagery analysis for Land Use, Land Use Change and Forestry: A pilot study in Kigali, Rwanda (Poster)   
Bright Aboh
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts (Poster)   
Christian Requena-Mesa
DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet (Poster)   
Yash Narayan
Machine Learning Climate Model Dynamics: Offline versus Online Performance (Poster)   
Noah Brenowitz
Expert-in-the-loop Systems Towards Safety-critical Machine Learning Technology in Wildfire Intelligence (Poster)   
Maria João Sousa
VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder (Poster)   
Matthew Ehrler
A Comparison of Data-Driven Models for Predicting Stream Water Temperature (Poster)   
Helen Weierbach
Automated Salmonid Counting in Sonar Data (Poster)   
Peter Kulits
ACED: Accelerated Computational Electrochemical systems Discovery (Poster)   
Rachel C Kurchin
Forecasting Marginal Emissions Factors in PJM (Poster)   
Amy Wang
Short-term PV output prediction using convolutional neural network: learning from an imbalanced sky images dataset via sampling and data augmentation (Poster)   
Yuhao Nie
OfficeLearn: An OpenAI Gym Environment for Building Level Energy Demand Response (Poster)
Lucas Spangher
Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR (Poster)   
Campbell Watson
Emerging Trends of Sustainability Reporting in the ICT Industry: Insights from Discriminative Topic Mining (Poster)   
Lin Shi
Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks (Poster)   
Brian Hutchinson
High-resolution global irrigation prediction with Sentinel-2 30m data (Poster)   
Will Hawkins
Do Occupants in a Building exhibit patterns in Energy Consumption? Analyzing Clusters in Energy Social Games (Poster)
Hari Prasanna Das
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness (Poster)   
Robbie Jones
Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change (Poster)
Luis Martí
Understanding global fire regimes using Artificial Intelligence (Poster)   
Cristobal Pais
ClimaText: A Dataset for Climate Change Topic Detection (Poster)   
Markus Leippold
Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery (Poster)   
Valentina Zantedeschi
A Generative Adversarial Gated Recurrent Network for Power Disaggregation & Consumption Awareness (Poster)   
Maria Kaselimi
Deep Fire Topology: Understanding the role of landscape spatial patterns in wildfire susceptibility (Poster)   
Cristobal Pais
Machine Learning towards a Global Parametrization of Atmospheric New Particle Formation and Growth (Poster)
Mihalis Nicolaou
Long-Range Seasonal Forecasting of 2m-Temperature with Machine Learning (Poster)   
Etienne Vos

Author Information

David Dao (ETH Zurich)

David Dao is a PhD student at ETH Zurich and the founder of GainForest, a non-profit working on decentralized technology to prevent deforestation. His research focuses on the deployment of novel machine learning systems for sustainable development and ecosystem monitoring. David served as a workshop co-organizer at ICLR, ICML and NeurIPS, and is a core member at Climate Change AI, a Global Shaper at World Economic Forum and a Climate Leader at Climate Reality. He is a research intern with Microsoft and was a former researcher at UC Berkeley and Stanford University.

Evan Sherwin (Stanford University)

I have devoted my professional career to evaluation of pathways toward a very low-carbon global energy system, developing expertise in energy modeling, statistics, machine learning, econometrics, and numerous engineering disciplines, economics, and policy domains as needed.

Priya Donti (Carnegie Mellon University)
Lauren Kuntz (Gaiascope)
Lynn Kaack (ETH Zurich)
Yumna Yusuf (City University London)
David Rolnick (McGill / Mila)
Catherine Nakalembe (University of Maryland)
Claire Monteleoni (University of Colorado Boulder)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

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