Conference talk, IAS Frontiers Conference on Geometry, Dynamics, and Learning (GDL2026), Nanyang Technological University, Singapore
Talk at GDL2026, an international conference at the intersection of geometric analysis, dynamical systems, and machine learning.
Conference talk, 16th International Conference on Spectral and High Order Methods (ICOSAHOM 2027), Politecnico di Milano, Milan, Italy
Invited talk proposal at ICOSAHOM 2027, the 16th International Conference on Spectral and High Order Methods (exact day within the conference, held July 5–9, 2027, TBD).
Conference talk, 16th International Conference on Spectral and High Order Methods (ICOSAHOM 2027), Politecnico di Milano, Milan, Italy
Invited talk proposal at ICOSAHOM 2027, the 16th International Conference on Spectral and High Order Methods (exact day within the conference, held July 5–9, 2027, TBD).
Conference talk, IAS Frontiers Conference on Geometry, Dynamics, and Learning (GDL2026), Nanyang Technological University, Singapore
Talk at GDL2026, an international conference at the intersection of geometric analysis, dynamical systems, and machine learning.
Conference talk, 47e Congrès National d'Analyse Numérique (CANUM), Saint-Jacut-de-la-Mer, France
Talk presenting joint work with Sheng Wan, Cyriaque Rousselot, Shorouk El-Hassanieh, Alena Shilova, and Cyril Furtlehner on adaptive sampling strategies for Physics-Informed Neural Networks (PINNs) through the lens of kernel theory.
Conference talk, 47e Congrès National d'Analyse Numérique (CANUM), Saint-Jacut-de-la-Mer, France
Talk presenting joint work with Roland Maier (Institute for Applied and Numerical Mathematics, KIT) on a unified framework connecting Physics-Informed Neural Networks (PINNs) and Finite Element Methods (FEMs) through a Petrov–Galerkin perspective and kernel theory.
Conference talk, European Conference on Numerical Mathematics and Advanced Applications (ENUMATH 2025), Heidelberg, Germany
Talk presenting joint work with Roland Maier (KIT) on a unifying perspective on Physics-Informed Neural Networks (PINNs) and Finite Element Methods (FEMs) through the notion of Natural Neural Tangent Kernel.
Conference talk, 12ème Biennale de la SMAI, Carcans-Plage, France
Talk presenting ANaGRAM, a theoretically founded and algorithmically efficient natural gradient method for training Physics-Informed Neural Networks (PINNs), at the 12th Biennale of the Société de Mathématiques Appliquées et Industrielles (SMAI).
Conference talk, International Conference on Learning Representations (ICLR 2025), Singapore EXPO, Singapore
Presentation of ANaGRAM at ICLR 2025. ANaGRAM is a theoretically founded and algorithmically efficient natural gradient method for training Physics-Informed Neural Networks (PINNs), connecting natural gradient descent to the Neural Tangent Kernel and the Green function of the PDE operator.
Workshop talk, JAX for HPC/AI Hybridization Hackathon, IDRIS, Paris-Saclay, France
Advanced participant at the JAX for HPC/AI Hybridization Hackathon, September 7–9, 2026.
Workshop talk, ML & AD in JAX for Scientific Computing Workshop, Strasbourg, France
Talk presenting an efficient JAX implementation of natural gradient methods for Physics-Informed Neural Networks (PINNs).
Workshop talk, HACE Workshop — HPC/AI Hybridization, AISSAI, Toulouse, France
Talk presenting natural gradient and kernel methods for Physics-Informed Neural Networks (PINNs).
Workshop talk, Numerical Analysis School, EDF, Palaiseau, France
Poster presentation of ANaGRAM at the EDF Numerical Analysis school.
Workshop talk, Méca–AAC day, LISN, Paris-Saclay University – CNRS, Gif-sur-Yvette, France
Talk given at the Méca–AAC day, presenting geometrical perspectives on the training of Physics-Informed Neural Networks (PINNs).
Workshop talk, SCALP Kick-off, LISN, Paris-Saclay University – CNRS, Gif-sur-Yvette, France
Talk given at the kick-off meeting of the SCALP project, presenting work on addressing spectral bias in Physics-Informed Neural Networks (PINNs).
Workshop talk, Infinite-dimensional Geometry: Theory and Applications, Erwin Schrödinger International Institute for Mathematics and Physics (ESI), Vienna, Austria
Talk given at the ESI workshop on Infinite-dimensional Geometry, presenting a kernel-based perspective on natural gradient methods with applications to Physics-Informed Neural Networks (PINNs).
Seminar, Thalès CortAIx Lab Seminar, Saclay, France
Presentation of a kernelization framework for natural gradient methods applied to Physics-Informed Neural Networks (PINNs).
Seminar, MIA Paris-Saclay Seminar, AgroParisTech-INRAE, Paris-Saclay, France
Presentation of a kernelization framework for natural gradient methods applied to Physics-Informed Neural Networks (PINNs).
Seminar, Machine Learning and Signal Processing Seminar, ENS Lyon, Lyon, France
Presentation of a kernelization framework for natural gradient methods applied to Physics-Informed Neural Networks (PINNs).
Seminar, MACARON Seminar, Université de Strasbourg, Strasbourg, France
Presentation of a kernelization framework for natural gradient methods applied to Physics-Informed Neural Networks (PINNs), highlighting connections to Galerkin methods.
Seminar, MILES Seminar, Université Paris-Dauphine, Paris, France
Presentation of a kernelization framework for natural gradient methods applied to Physics-Informed Neural Networks (PINNs).
Seminar, CRUNCH Seminar, Brown University, Providence, USA
Presentation of a kernelization framework for natural gradient methods applied to Physics-Informed Neural Networks (PINNs).
Seminar, ACSIOM Seminar, Institut Montpelliérain Alexander Grothendieck, Montpellier, France
Presentation of ANaGRAM, a theoretically founded and algorithmically efficient natural gradient method for training Physics-Informed Neural Networks (PINNs). The talk covers the functional analysis perspective on PINNs training, the connection to the Neural Tangent Kernel, and an efficient SVD-based implementation that reduces the complexity of natural gradient descent significantly.
Seminar, SCOOL Seminar, INRIA Lille – Nord Europe, Lille, France
Presentation of ANaGRAM, a theoretically founded and algorithmically efficient natural gradient method for training Physics-Informed Neural Networks (PINNs). The talk covers the functional analysis perspective on PINNs training, the connection to the Neural Tangent Kernel, and an efficient SVD-based implementation that reduces the complexity of natural gradient descent significantly.
Seminar, TAU Seminar, LISN – Paris-Saclay University, CNRS, INRIA, Gif-sur-Yvette, France
Presentation of ANaGRAM, a theoretically founded and algorithmically efficient natural gradient method for training Physics-Informed Neural Networks (PINNs). The talk covers the functional analysis perspective on PINNs training, the connection to the Neural Tangent Kernel, and an efficient SVD-based implementation that reduces the complexity of natural gradient descent significantly.
Seminar, Institute for Applied and Numerical Mathematics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Presentation of ANaGRAM, a theoretically founded and algorithmically efficient natural gradient method for training Physics-Informed Neural Networks (PINNs). The talk covers the functional analysis perspective on PINNs training, the connection to the Neural Tangent Kernel, and an efficient SVD-based implementation that reduces the complexity of natural gradient descent significantly.
PhD Defense, Université Paris-Saclay – LISN, Gif-sur-Yvette, France
PhD defense in Computer Science at Université Paris-Saclay, under the supervision of Cyril Furtlehner (TAU Team, INRIA Saclay – A&O–LISN–Paris-Saclay University–CNRS).