Julien Lalanne

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Bio

I am a 2nd year PhD student at Laboratoire Navier - École Nationale des Ponts et Chaussées and TotalEnergies, working at the intersection of generative modeling and geosciences under the supervision of Jean-Michel Pereira, Lina Guayacan, David Picard, Leon Barens and Lionel Boillot. My research focuses on stochastic interpolation and inversion techniques for subsurface data, with applications in soil characterization for offshore wind farms.

Prior to my PhD, I worked as a research engineer at TotalEnergies, where I developed machine learning methods for seismic and well log data analysis and defined my PhD project.

I hold a Master in Applied Mathematics from Université Grenoble Alpes and an engineering degree from Grenoble INP – Ensimag, with a focus in machine learning, statistical modeling, and optimization. My academic journey also includes a research internship at the National Institute of Informatics in Tokyo, where I explored multimodal context analysis for live video comment generation.

Research Interests

My research interests lie at the intersection of generative modeling, Bayesian inference and geostatistics, with a particular focus on subsurface data analysis for real-world applications.

  • Generative Modeling: Probabilistic and deep generative models for spatial data synthesis and interpolation.

  • Bayesian Methods: Uncertainty quantification and probabilistic inference in geoscientific applications.

  • Geostatistics: Machine learning approaches for seismic and geotechnical data inversion.