Annuaire

I graduated from École Polytechnique (Palaiseau, France, X2009) and obtained a Master’s degree in Physics from École Polytechnique Fédérale de Lausanne (EPFL) in 2014. I prepared my PhD thesis in Tobias Kippenberg’s lab at EPFL, focusing on the generation of low-noise optical frequency combs through nonlinear frequency conversion and Kerr soliton formation in micro-resonators, and defended it in 2019.

I then joined the National Institute of Standards and Technology (NIST) in Boulder, Colorado as a postdoctoral researcher, where I developed methods for inverse design and optimization of frequency combs on micro-resonators.

I joined CNRS as a full-time researcher in 2022 and am now part of the photonics department at ICB. My research focuses on designing innovative photonic architectures in optical fibers or integrated photonics, to explore and control the influence of additional parameters on the shape and dynamics of optical combs. The ultimate goal is to tailor optical combs for targeted applications.

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M-Engine

3 years project funded by the European union’s Innovation council (EIC Transition) aiming to develop a hybrid architecture of highly compact generic microcomb modules and establish their scalable fabrication using MTP processes.

ICB is involved in optimizing the microcomb’s performances to meet the demanding criteria required for commercial applications.

See the project website

SMARTKOMBS

Project funded by the French Agence Nationale de la Recherche ANR-23-CE24-0005

This 42-month project is focused on exploring new paradigms for the generation of Kerr combs. The aim is to develop innovative photonic architectures with complex control of dispersion and dissipation or multimode cavities to realize “exotic” dissipative Kerr solitons or, more generally, self-localized light structures. We will introduce additional degrees of freedom in the intra- or inter-cavity couplings to enable spectral modeling of the comb, and study the associated dynamics, opening up prospects for the emergence of new phenomena, thus advancing our understanding of fundamental nonlinear physics. While these systems will initially be modeled using rigorous equations, the project will then rely on artificial intelligence tools (machine learning) to map these hitherto unexplored parameter regimes. Experimental demonstrations will be carried out in fiber-optic waveguide cavities in order to validate the principles and propose, via numerical simulations, subsequent implementations with integrated photonic systems. The machine learning models developed will also be used for inverse design approaches to optimize the generation of coherent Kerr combs with customized properties on demand. This fundamental knowledge will be useful for producing optimized combs for applications such as optical communication, frequency metrology and spectroscopy.