PhD

💡 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.


Selected Publications

  1. climategan.jpeg
    ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
    Victor Schmidt*, Alexandra Sasha Luccioni*, Mélisande Teng, and 8 more authors
    ICLR, 2022
  2. phast.png
    PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design
    Alexandre Duval, Victor Schmidt, Santiago Miret, and 3 more authors
    Journal of Machine Learning Research, 2024
  3. faenet.png
    FAENet: Frame Averaging Equivariant GNN for Materials Modeling
    Alexandre Duval*, Victor Schmidt*, Alex Hernandez Garcia, and 4 more authors
    ICML 2023, 2023
  4. impact.png
    Quantifying the Carbon Emissions of Machine Learning
    Alexandre Lacoste*, Alexandra Luccioni*, Victor Schmidt*, and 1 more author
    Climate Change AI workshop, NeurIPS, 2019
  5. hitchhiker.png
    A Hitchhiker’s Guide to Geometric GNNs for 3D Atomic Systems
    Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, and 7 more authors
    arXiv preprint arXiv: 2312.07511, 2023