Greeting! I’m Victor, a postdoctoral researcher in the Dynamic Macroecology Group at the Swiss Federal Institute for Forest, Snow & Landscape (WSL), Switzerland.
My work is centered on developing innovative models and methods to better understand and forecast the dynamics of ecosystems and their response to disruptions. My focus lies at the interface between process-based modelling and machine learning. I am specifically interested in leveraging the extrapolation ability of mechanistic models with the flexibility of state-of-the-art data driven techniques.
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.
PhD in Environmental Sciences, 2022
ETH Zürich, Switzerland
MSc in Energy and Environmental Sciences, 2018
INSA Lyon, France
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 parameter inference and model selection with dynamical models characterised by complex dynamics.
StarUtilities for parametric and composite differential equation models.
StarA zoo of eco-evolutionary models with high fitness.
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).
Faunal turnover in Indo-Australia across Wallace’s Line is one of the most recognizable patterns in biogeography and has catalyzed debate about the role of evolutionary and geoclimatic history in biotic interchanges. Here, analysis of more than 20,000 vertebrate species with a model of geoclimate and biological diversification shows that broad precipitation tolerance and dispersal ability were key for exchange across the deep-time precipitation gradient spanning the region. Sundanian (Southeast Asian) lineages evolved in a climate similar to the humid “stepping stones” of Wallacea, facilitating colonization of the Sahulian (Australian) continental shelf. By contrast, Sahulian lineages predominantly evolved in drier conditions, hampering establishment in Sunda and shaping faunal distinctiveness. We demonstrate how the history of adaptation to past environmental conditions shapes asymmetrical colonization and global biogeographic structure.
Analogies between organisational routines and genes, firms and phenotypes, economic activities and biological populations, and between the processes acting upon these entities, have been used to advance our qualitative understanding of mechanisms driving economic change. Yet, it remained unclear whether biological concepts can be used to quantitatively describe long-term economic change. Here, we use an inverse modelling framework together with economic time-series data to test whether eco-evolutionary processes can explain the collective dynamics of national economic activities. Comparing the support of different biologically inspired dynamic community models against a null model, we find evidence for positive interactions between economic activities and their spatial dispersal.
Alsos, I.G., Boussange, V., Rijal, D.P., Beaulieu, M., Brown, A.G., Herzschuh, U., Svenning, J.C., Pellissier, L., Ancient sedimentary DNA to forecast trajectories of ecosystem under climate change. (2023). Accepted in Philosophical Transactions of the Royal Society B. [preprint]
Skeels, A., Boschman, L. M., McFadden, I. R., Joyce, E.M., Hagen, O., Jiménez Robles, O., Bach, W., Boussange, V., Keggin, T., Jetz, W., Pellissier, L., Paleoenvironments shaped the exchange of terrestrial vertebrates across Wallace’s Line. Science 381, 86-92 (2023).
Boussange, V., Becker, S., Jentzen, A., Kuckuck, B., Pellissier, L., Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions. Partial Differ. Equ. Appl. 4 (2022), Paper no. 51, 59 pp. [arXiv]
Boussange, V. & Pellissier, L., Eco-evolutionary model on spatial graphs reveals how habitat structure affects phenotypic differentiation. Commun Biol 5, 668 (2022). [bioRxiv]
Sapienza, F., Bolibar, J., Schäfer, F., Groenke, B., Pal, A., Boussange, V., Heimbach, P., Hooker, G., Pérez, F, Persson, P.O., Rackauckas, C., Differentiable Programming for Differential Equations: A Review. [arXiv] (2024), 72 pages. GitHub repository. In review.
Reji Chacko, M., Albouy, C., Altermatt, F., Casanelles Abella, J., Brändle, M., Boussange, V., Campell, F., Ellis, W. N., Fopp, F., Gossner, M., Ho, H. C., Joss, A., Kipf, P., Neff, F., Petrović, A., Prié, V., Tomanović, Ž., Zimmerli, N., Pellissier, L., trophiCH - a national species-level trophic metaweb of 23k species for Switzerland. [EcoEvoRxiv] (2024), 32 pages. In review.
Boussange, V., Vilimelis-Aceituno, P., Schäfer, F., Pellissier, L., Partitioning time series to improve process-based models with machine learning. [bioRxiv] (2024), 46 pages. In review.
Boussange, V., Sornette, D., Lischke, H., Pellissier, L., Processes analogous to ecological interactions and dispersal shape the dynamics of economic activities. [arXiv] (2023), 23 pages.
HighDimPDE.jl
, International Conference on Computational Methods in Systems Biology, Bordeaux, France (October 2021). [poster]Mentor for master’s theses: