I’m Victor, a $4^{th}$ year Ph.D candidate in the Landscape Ecology Group at ETH Zürich and at the Swiss Federal Institute for Forest, Snow & Landscape (WSL), Switzerland.
My Ph.D aims at better understanding evolutionary processes that affect the dynamics of ecosystems and economic systems. I conduct my investigations with mathematical models capturing eco-evolutionary dynamics. In parallel, I develop machine learning methods to confront these models with empirical data and infer scientific knowledge. I believe that the combination of mechanistic models and machine learning provides a powerful approach to better understand our world. This is crucial, in the face of potentially important ecosystem changes and accelerating threats.
Besides work, I am passionate about alpine adventures and spend my freetime climbing and going down mountains 🏔 , be it with chalk, ice-axes, skis, or a mountainbike. You can check out my alpine CV here.
PhD in Environmental Sciences, expected 2022
ETH Zürich, Switzerland
MSc in Energy and Environmental Sciences, 2018
INSA Lyon, France
Click on each project to learn more.
Open source as a philosphy.
Evolutionary Individual based modelling, mathematically grounded. A user friendly package aimed at simulating the evolutionary dynamics of a population structured over a complex spatio-evolutionary structures.
StarSolver for highly dimensional, non-local, nonlinear PDEs. It is integrated within the SciML ecosystem (see below). Try it out! 😃 If you want to learn more about the algorithms implemented, check out my research interests.
StarSuite for fitting ecological time series to mechanistic models.
StarI am a member of the SciML organisation, an open source ecosystem for Scientific Machine Learning in the Julia programming language. On top of being the main author of HighDimPDE.jl, I actively participate in the development of other packages such as DiffEqFlux.jl, a library to train differential equations with data.
StarI am also a reviewer at the Journal of Open Source Software Science (JOSS).
Nonlinear partial differential equations (PDEs) are used to model dynamical processes in a large number of scientific fields, ranging from finance to biology. In this article we propose two numerical methods based on machine learning and on Picard iterations, respectively, to approximately solve non-local nonlinear PDEs. Our work extends recently developed methods to overcome the curse of dimensionality in solving PDEs.
Differentiation mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. Here, we investigate how habitat connectivity and habitat heterogeneity affect phenotypic differentiation by formulating a stochastic eco-evolutionary model where individuals are structured over a spatial graph. By formalising the eco-evolutionary and spatial dynamics of biological populations on graphs, our study establishes fundamental links between landscape features and phenotypic differentiation.
HighDimPDE.jl
, International Conference on Computational Methods in Systems Biology, Bordeaux, France (October 2021). [poster]