Victor Boussange

Victor Boussange

Postdoctoral researcher

WSL Birmensdorf

ETH Zürich

Biography

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.

Interests
  • Ecology and evolution
  • Mathematical modeling
  • Scientific machine learning
  • Complex systems
Education
  • PhD in Environmental Sciences, 2022

    ETH Zürich, Switzerland

  • MSc in Energy and Environmental Sciences, 2018

    INSA Lyon, France

Open source softwares 🧑🏽‍💻

Open source as a philosphy.

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.

Star

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.

Star

PiecewiseInference.jl

Suite for parameter inference and model selection with dynamical models characterised by complex dynamics.

Star

ParametricModels.jl

Utilities for parametric and composite differential equation models.

Star

EcoEvoModelZoo.jl

A zoo of eco-evolutionary models with high fitness.

Star

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.

Star


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

Recent Posts

Publications & Talks

Publications

  • 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]

Preprints

  • 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.

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.

Works in progress

  • Vilimelis-Aceituno, P., Miller, J., Marti, N., Farag, Y., Boussange, V., Temporal horizons in forecasting: a performance-learnability trade-off
  • Boussange, V., Karger, D., Malle, J. T., Midolo, D., Unveiling climate-biodiversity interactions: linking earth system and biodiversity models. (in alphabetical order)

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]

Teaching and teaching material

Resources

Teaching

2024

2023

2020

Mentor for master’s theses:

  • Cecilia Valenzuela Agui, Computational Biology and Bioinformatics, ETH Zürich,
  • Nicolas Demolin, Applied Mathematics and Modeling, Polytech Nice