Victor Boussange

Victor Boussange

PhD candidate

ETH Zürich

Biography

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.

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

    ETH Zürich, Switzerland

  • MSc in Energy and Environmental Sciences, 2018

    INSA Lyon, France

Research projects 🔬

Click on each project to learn more.

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Machine learning to solve highly dimensional non-local nonlinear PDEs
Partial Differential Equations (PDEs) are equations that arise in a variety of models in physics, engineering, finance and biology. I develop numerical schemes based on machine learning techniques to solve for a special class of PDEs (cf below) in high dimension.

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.

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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.

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MiniBatchInference.jl

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

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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.

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

Publications & Talks

Publications

  • Boussange, V. & Pellissier, L., Eco-evolutionary model on spatial graphs reveals how habitat structure affects phenotypic differentiation. Commun Biol 5, 668 (2022). [bioRxiv]

Preprints

  • Boussange, V., Vilimelis-Aceituno, P., Pellissier, L., Mini-batching ecological data to improve ecosystem models with machine learning [bioRxiv] (2022), 46 pages. In review.
  • Boussange, V., Becker, S., Jentzen, A., Kuckuck, B., Pellissier, L., Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions. [arXiv] (2022), 59 pages. Revision requested from Partial Differential Equations and Applications.

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.

Works in progress

  • Boussange, V., Sornette, D., Lischke, H., Pellissier, L., Quantifiying eco-evolutionary dynamics in economic systems.

Talks

  • 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, Seminar at the Theoretical Ecology and Evolution group, invited speaker, Universität Bern (June 2022).
  • Deep learning approximations for non-local nonlinear PDEs, StAMBio seminar, invited speaker, St Andrews, UK (November 2021).
  • 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]