AMJAX
Algebraic multigrid solvers in JAX.
Hey there, I’m Victor, an applied ML researcher working in AI for science, currently at the Information and Network Dynamics Lab at the Ecole Polytechnique Fédérale de Lausanne (EPFL).
I develop methods for high-dimensional, spatio-temporal forecasting and inverse problems that remain robust under low signal-to-noise ratios and distribution shift, with application in biology and environmental sciences. I am particularly interested in hybrid modelling, integrating domain priors with deep learning and GPU-accelerated differentiable computing. I ship open-source libraries implementing these methods, lead funded research projects, and mentor students in applied machine learning and scientific computing.
Outside of work, I am a part-time alpinist, passionate about mountain adventures and writing. I also enjoy sailing and surfing occasionally. You can check out my alpine CV here.
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PhD in Environmental Sciences, 2023
ETH Zürich, Switzerland
MSc in Energy and Environmental Sciences, 2018
INSA Lyon, France
Algebraic multigrid solvers in JAX.
A Julia library for building and training hybrid dynamic models that combine mechanistic and data-driven components.
Multi-scale model for spatial biodiversity estimation.
A minimal JAX library for graph-based connectivity analysis at scale.
A Julia package providing access to a collection of eco-evolutionary models.
A Julia package for simulating evolutionary individual-based models.
I also review for the Journal of Open Source Software, Ecology Letters, Ecography, Biodiversity and Conservation, and Methods in Ecology and Evolution.
Forecasting alien invasive species range dynamics with GNNs.
Funding for the CORDS course, coordinated by Mauro Werder.
Funding for a three-day Julia workshop for biodiversity research.
Funding for a Julia workshop on modelling and data analysis in biodiversity and earth sciences.
A mechanistic approach to biome transitions across space and time
Forecasting invasive species range expansion using ecologically-informed neural networks
Co-supervision with Swiss Data Science Center
Attention-based deep multiple instance learning for species richness prediction
Co-supervision with Swiss Data Science Center