jaxscape

A minimal JAX library for graph-based connectivity analysis at scales.

HybridDynamicModels.jl

A Julia library for easily building and training hybrid dynamic models which combine mechanistic and data driven components.

HighDimPDE.jl

Solver 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.

EcoEvoModelZoo.jl

A zoo of eco-evolutionary models with high fitness.

EvoId.jl

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.

SciML

I 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.

I am also a reviewer at the Journal of Open Source Software Science (JOSS).

Publications

• Boussange, V., Vilimelis-Aceituno, P., Schäfer, F., Pellissier, L., A calibration framework to improve mechanistic forecasts with hybrid dynamic models. Accepted in Methods in Ecology and Evolution (2025). [preprint]

• 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. Accepted in SIAM Review (2025). [preprint]

• Aceituno, P., Miller, J., Marti, N., Farag, Y., Boussange, V., Temporal horizons in forecasting: a performance-learnability trade-off. Transactions on Machine Learning Research (2025). [preprint]

• Reji Chacko, M., Albouy, C., Altermatt, F., Boussange, V., Br{“a}ndle, M., Farwig, N., Gossner, M. M., Ho, H.C., Joss, A., Neff, F., Pellissier, L., Species loss in key habitats accelerates regional food web disruption. Communications Biology (2025).

• 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., A species-level multi-trophic metaweb for Switzerland. Scientific Data (2024).

• 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. Philosophical Transactions of the Royal Society B (2023). [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 (2023).

• Boussange, V., Becker, S., Jentzen, A., Kuckuck, B., Pellissier, L., Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions. Partial Differential Equations and Applications (2022). [preprint]

• Boussange, V. & Pellissier, L., Eco-evolutionary model on spatial graphs reveals how habitat structure affects phenotypic differentiation. Communications Biology (2022). [preprint]

Books

• RE-BD AR2024. Accelerating renewable energy development while enhancing biodiversity protection in Switzerland. doi:10.5075/epfl.20.500.14299/241642 (2024). Contributing author of chapter 2 and 5.

Preprints

• Boussange, V., Brun, P., Malle, J. T., Midolo, G., Portier, J., Sanchez, T., Zimmermann, N. E., Axmanova, I., Bruelheide, H., Chytry, M., Kambach, S., Lososova, Z., Vecera, M., Birrun, I., Ecker, K. T., Lenoir, J., Svenning, J.C., Karger, D. N., Multi-scale species richness estimation with deep learning. [arXiv] (2025). In revision at Nature Communications.

• Adde, A., Boussange, V., Chauvier, Y., Dahito, M.A., Fruh, J., Gross, A., Stofer, S., Rey, E., Sieber, P., Fopp, F., Schouten, R., Van Moorter, B., Guisan, A., Graham, C., Pellissier, L., Zimmermann, N.E., Altermatt, F., Spatial biodiversity indicators and a composite index for conservation prioritization in Switzerland. [bioRxiv] (2025). In revision at Scientific Data.

• Poulet, T., Truttmann, S., Boussange, V., Veveakis, M., Chaotic Slow Slip Events in New Zealand from two coupled slip patches: a proof of concept. [arXiv] (2024). GitHub repository. 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.

Proceedings

• Poulet, T., Alevizos, S., Veveakis, M., Boussange, V., Regenauer-Lieb, K., Episodic mineralising fluid injection through chemical shear zones. ASEG Extended Abstracts (2018), 5 pages.

Monographs

• Boussange, V., Forward and inverse modelling of eco-evolutionary dynamics in ecological and economic systems. [ETH library] (2022), 207 pages.

Talks

• PiecewiseInference.jl: inverse modelling for complex dynamics, speaker, JuliaCon2024, Eindhoven, Netherlands (July 2024).
• Introduction to Julia for Geosciences, co-convener, Short course at EGU 2024, Vienna, Austria (April 2024).
• A scalable machine learning approach to assess the combined effect of habitat loss and climate change on biodiversity, speaker, International Biogeography Society conference 2024, Prague, Czech Republic (January 2024).
• Learning from scarce data by combining machine learning and fundamental ecological knowledge, invited speaker, Bioinformatics seminar, Fribourg University, Fribourg, Switzerland (December 2023). [slides]
• PiecewiseInference.jl: a machine learning framework for inverse ecosystem modelling, speaker, EGU 2023, Vienna, Austria (April 2023). [slides]
• Combining eco-evolutionary theory and machine learning to advance our understanding of living systems, invited speaker, Seminar at the Laboratoire interdisciplinaire de physique (LiPhy), Grenoble, France (February 2023). [slides]
• HighDimPDE.jl: A Julia package for solving high-dimensional PDEs, JuliaCon2022, online video 📺
• Interpretable machine learning for forecasting dynamical processes in ecosystems, World Biodiversity Forum, Davos, Switzerland (June 2022). [slides]
• Investigating empirical patterns of biodiversity with mechanistic eco-evolutionary models, invited speaker, Seminar at the Theoretical Ecology and Evolution group, Universität Bern (June 2022).
• Deep learning approximations for non-local nonlinear PDEs, invited speaker, StAMBio seminar, St Andrews, UK (November 2021). [slides]
• Graph topology and habitat assortativity drive phenotypic differentiation in an eco-evolutionary model, Conference on Complex Systems, Lyon, France (October 2021). [slides]
• Using graph-based metrics to assess the effect of landscape topography on diversification, ECBC, Amsterdam, Netherlands (October 2021). [slides]
• Solving non-local nonlinear Partial Differential Equations in high dimensions with HighDimPDE.jl, International Conference on Computational Methods in Systems Biology, Bordeaux, France (October 2021). [poster]
• Responses of neutral and adaptive diversity to complex geographic population structure, Mathematical Population Dynamics, Ecology and Evolution, CIRM Marseille, France (April 2021). [poster]

Students

• Jeffrey Zweidler, Master thesis, Forecasting invasive species range expansion using ecologically-informed neural networks, Department of Computer Science, ETH Zürich (2025-2026, co-supervision with Swiss Data Science Center)
• Moritz Dieinger, Master thesis, Deep multiple instance learning for species richness estimation, Department of Computer Science, ETH Zürich (2025-2026, co-supervision with Swiss Data Science Center)

Alumni

• Cecilia Valenzuela Agui, Taste of research internship, Computational Biology and Bioinformatics, ETH Zürich (2020)
• Nicolas Demolin, Research internship, Applied Mathematics and Modeling, Polytech Nice (2020)

Resources

• Swiss SolarWind Explorer: a web tool to identify optimal sites for solar and wind energy projects based on potential energy production, biodiversity and landscape protection.
• Practical introduction to Julia for modelling and data analysis in biodiversity and earth sciences
• Course On Reproducible Research, Data Pipelines, and Scientific Computing

Teaching

2024

• 2-day Course On Reproducible Research, Data Pipelines, and Scientific Computing
• Landscape Modelling of Biodiversity: From Global Changes to Conservation, mentoring the project Modelling the spread dynamics of invasive alien species, ETH Zürich, D-USYS

2023

• Environmental Systems Data Science, in charge of the unit Supervised Deep Learning - Application, ETH Zürich, D-USYS
• 2 day workshop on Julia for modelling and data analysis in biodiversity and earth sciences