About

 

I’m a postdoctoral researcher at the Institute of Environmental Geosciences (IGE) in Grenoble, working on mountain glacier modelling. I work on differentiable programming methods to solve complex modelling problems in glaciology. I actively participate in the develoment of the open source projects ODINN.jl and MassBalanceMachine.

Before switching to geosciences, I used to work in the private sector at GoPro on deep learning and embedded image processing.

My mathematical background lies at the intersection of numerical methods for PDEs, optimization and machine learning. I did my PhD at the Toulouse Mathematics Institute under the supervision of Pierre Weiss and Frédéric de Gournay, working primarly on inverse problems, optimization and learning algorithms. Before that I received a Master of Science in Fundamental Mathematics and Applications (PDE, numerical analysis and optimization track) from Université Paul Sabatier in 2019. I also graduated from INSA Toulouse in Applied Mathematics (numerical modelling and image processing) in 2019.

Research

Preprints

• Adaptive scaling of the learning rate by second order automatic differentiation
  F. de Gournay & A. Gossard. PDF

Journal papers

• Training Adaptive Reconstruction Networks for Blind Inverse Problems
  A. Gossard & P. Weiss. SIAM Journal on Imaging Sciences. PDF

• Bayesian Optimization of Sampling Densities in MRI
  A. Gossard, F. de Gournay & P. Weiss., Journal of Machine Learning for Biomedical Imaging (MELBA), Volume 2 (2023). PDF

• Spurious minimizers in non uniform Fourier sampling optimization
  A. Gossard, F. de Gournay & P. Weiss, Inverse Problems (2022). PDF

Conference papers

• iTWIST, 2020, remote. Off-the-grid data-driven optimization of sampling schemes in MRI. With Frédéric de Gournay & Pierre Weiss. PDF

Oral presentations

• Applied Inverse Problems (AIP) Conference, Göttingen (Germany), September 2023, Learning Fourier sampling schemes for MRI by density optimization.
• Torus AI Tasting seminar, June 2023, Training adaptive reconstruction networks for blind inverse problems.
• PhD defence, Toulouse, December 2022, Learning strategies for computational MRI.
• Curves & Surfaces 2022, Arcachon, June 2022, Spurious minimizers in non uniform Fourier sampling optimization.
• SIAM Imaging Science 2022, remote, March 2022, Learning to sample and reconstruct: computational issues and tricks.
• Séminaire doctorant de l’IMT, Toulouse, March 2022, Globalization approach for sampling scheme optimization in MRI.
• EUROPT 2021, remote, July 2021, Continuous optimization of Fourier sampling schemes for MRI.
• iTWIST 2020, remote, December 2020, Off-the-grid data-driven optimization of sampling schemes in MRI.

Contact

@lb@n.p@ul.goss@rd@gm@il.com (replace ‘@’ by ‘a’ at relevant places)

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