Current research
I am a Ph.D. student in Machine Learning and Neuromorphic Computing at Université Paris-Saclay, under the supervision of Dr. Damien Querlioz. Currently working at the Centre de Nanosciences et de Nanotechnologies (C2N), I focus on the creation of Binary Bayesian Neural Networks for continual learning and energy-efficient computing. My research interests include machine learning, neuromorphic computing, and hardware acceleration.
My research is conducted under the Metaspin Project, working towards the development of a new generation of energy-efficient and high-performance computing systems inspired by the brain. The project is funded by the European Research Council (ERC) and the Agence Nationale de la Recherche (ANR).
Publications
Bayesian continual learning and forgetting in neural networks
Bonnet, Djohan, et al. "Bayesian continual learning and forgetting in neural networks." Nature Communications 16.1 (2025): 9614.
Dynamic Control of Weight-Update Linearity in Magneto-Ionic Synapses
Bernard, Guillaume, et al. "Dynamic control of weight-update linearity in magneto-ionic synapses." Nano Letters 25.4 (2025): 1443-1450. doi: 10.1021/acs.nanolett.4c05247
A unified method to generate representative volume elements with tailored random fibre arrangements to estimate the shear and transverse behaviours of unidirectional continuous fibres composite plies
Mechin P-Y, Borras A, Cottart K, Keryvin V. A unified method to generate representative volume elements with tailored random fibre arrangements to estimate the shear and transverse behaviours of unidirectional continuous fibres composite plies. Journal of Composite Materials. 2024;0(0). doi: 10.1177/00219983241300144
Talks
Neuromorphic Symposium: Bayesian Continual Learning and Forgetting in Neural Networks (15 minutes)
Talk at Neuromorphic Symposium, Paris, France
UQ4ML / COMETA Workshop: Bayesian Continual Learning and Forgetting in Neural Networks (30 minutes)
Talk at UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning, CEA Paris-Saclay, France
Updates
2026
Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
Published:
We are excited to share that our paper, Active Continual Learning with Metaplastic Binary Bayesian Neural Networks , has been accepted as a poster at ICML 2026 in Seoul, South Korea!
A geometrical interpretation of variational methods in machine learning
Published:
Variational methods are often introduced through information theory: learning means finding an approximate distribution that remains close to a target Bayesian posterior. This has a geometrical interpretation. The mathematical object being optimized is not a point in a Euclidean parameter space, it's a probability distribution living on a curved statistical manifold.
2025
Itinérances - Ph.D Student Portrait - Lara Couronné
Published:
This portrait celebrates Lara Couronné, a brilliant scientist whose journey blends determination, passion, and creativity. Filmed as part of her daily life in research, this video offers a glimpse into who she is, both in and beyond the lab.
Bayesian Continual Learning in Nature Communications
Published:
I’m super happy to announce that Djohan Bonnet, Damien Querlioz and I, as well as co-authors recently published a paper about Bayesian continual learning & forgetting in Neural Networks in Nature Communications! It’s been a long journey to get this paper published and we are very proud of our research.
2024
Itinérances - Ph.D Student Portrait - Kellian Cottart
Published:
Itinérances is a portrait of my journey as a Ph.D. student, at the crossroads of scientific research, personal passion, and intellectual curiosity. This short film explores my daily life in the lab, the motivations that drive my work, and the ideas that shape my approach to science.