💡 I defended my PhD in April 2024 on Deep Learning Applications to Climate Change Mitigation. This adventure led me to explore various areas, from computer vision and GANs to geometric graphs for materials modeling.
1️⃣ My first PhD project focused on creating AI visualizations of what your home could look like if climate-related extreme events (floods, wildfires or smog events) happened there. It’s not about climate projections, it’s about empathy: a climate in which every address in the world is experiencing floods, wildfires and smog at the same time does not exist. But the one we have is getting warmer, more dangerous, and everyone’s actions have global consequences. This is why we published ClimateGAN (ICLR 2022) and deployed it on This Climate Does Not Exist.
2️⃣ The second project I worked on for 2 years is about using AI for materials discovery. The goal was to contribute to the discovery of more efficient electro-catalysts that would improve the energy efficiency of a wide range of chemical reactions. To that end, I worked on graph neural networks for materials property prediction: PhAST (JMLR 2024) and FAENet (ICML 2023). I then turned to using this work within a generative model (GFlowNet) to efficiently explore the huge space of potential electro-catalysts.
3️⃣ Finally, I wanted to keep an eye on my own community and work towards quantifying the carbon emissions of machine learning. This lead to a workshop paper (Climate Change AI workshop, NeurIPS 2019) and its online emissions calculator, and a Python open-source library: codecarbon.
Community work
Beyond publishing, I try to be a good member of our community.
Student Lab Representative for 3 consecutive years at Mila
I contribute a module on AI & Climate Change to an upcoming MOOC by the Geography Department of Université de Montréal (with the amazing Mélisande Teng) and I like writing tutorials (from as early as 2015 or more recently an introduction to Pytorch).
@article{schmidt2021climategan,title={ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods},dimensions={false},author={Schmidt*, Victor and Luccioni*, Alexandra Sasha and Teng, Mélisande and Zhang, Tianyu and Reynaud, Alexia and Raghupathi, Sunand and Cosne, Gautier and Juraver, Adrien and Vardanyan, Vahe and Hernandez-Garcia, Alex and Bengio, Yoshua},year={2022},journal={ICLR},}
PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design
Alexandre Duval, Victor Schmidt, Santiago Miret, and
3 more authors
@article{JMLR:v25:23-0680,author={Duval, Alexandre and Schmidt, Victor and Miret, Santiago and Bengio, Yoshua and Hern{{\'a}}ndez-Garc{{\'i}}a, Alex and Rolnick, David},title={PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design},journal={Journal of Machine Learning Research},year={2024},volume={25},number={106},pages={1-26},}
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Alexandre Duval*, Victor Schmidt*, Alex Hernandez Garcia, and
4 more authors
@article{duval2023faenet,title={FAENet: Frame Averaging Equivariant GNN for Materials Modeling},dimensions={false},author={Duval*, Alexandre and Schmidt*, Victor and Garcia, Alex Hernandez and Miret, Santiago and Malliaros, Fragkiskos D. and Bengio, Yoshua and Rolnick, David},year={2023},journal={ICML 2023},}
Quantifying the Carbon Emissions of Machine Learning
Alexandre Lacoste*, Alexandra Luccioni*, Victor Schmidt*, and
1 more author
@article{lacoste2019quantifying,title={Quantifying the Carbon Emissions of Machine Learning},dimensions={false},author={Lacoste*, Alexandre and Luccioni*, Alexandra and Schmidt*, Victor and Dandres, Thomas},year={2019},journal={Climate Change AI workshop, NeurIPS},}
A Hitchhiker’s Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, and
7 more authors
@article{duval2023hitchhikers,title={A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems},author={Duval, Alexandre and Mathis, Simon V. and Joshi, Chaitanya K. and Schmidt, Victor and Miret, Santiago and Malliaros, Fragkiskos D. and Cohen, Taco and Lio, Pietro and Bengio, Yoshua and Bronstein, Michael},year={2023},journal={arXiv preprint arXiv: 2312.07511},}
