I am a graduate Télécom Paris engineer since 2017. Notably, I followed the EURECOM cursus where I specialised in Data Science from 2015-2017. In 2021, I received a PhD in computational mathematics (mathématiques aux interfaces in French) from the Institut Polytechnique de Paris. This was an industrial PhD (CIFRE) with Télécom SudParis (SAMOVAR) and Nokia Bell Labs supervised by , , , and . During that time, I focused on alarm prediction in telecommunication networks via space-time pattern matching and machine learning, aiming to provide experts with tools to understand how failures cascade across telecommunication networks.
Currently, I work as a postdoc at the Oxford Institute of Biomedical Engineering (part of the Department of Engineering of the University of Oxford), within the CHI Lab. With , , and , I develop interpretable AI tools to support clinical decisions in organ transplantation.
Feel free to reach out to me at .
PhD in Computational Mathematics, 2017-2021
Institut Polytechnique de Paris
Master's degree in Data Science and Engineering, 2015-2017
EURECOM
Diplôme d'ingénieur, 2014-2017
Télécom Paris
With CHI Lab, IBME, University of Oxford, and the clinicians and , I contribute to the construction of a computerised decision support system (CDSS) to help surgeons and patients understand if a given organ offer is a suitable one, or if it is preferable to wait for another one.
at theMore precisely, given more than twenty years of data provided by the National Institute for Health and Care Research (NIHR) with ethical approval, we develop machine learning models predicting transplant outcomes (graft failure and patient death) in the case of an accepted offer, or telling what could happen in the case of a denied one. These models are supported by interpretability methods.
During this industrial PhD (CIFRE) between Télécom SudParis (SAMOVAR) and Nokia Bell Labs, I worked on predicting alarms in networks via space-time pattern matching and machine learning. For this, I benefited from the supervision of , , , and .
This thesis is two-fold. On the one hand, we proposed a structure, called DIG-DAG, able to store online causality chains observed within log. On the other hand, we compared analytically the expressivity of two popular generative models: Hidden Markov Models and Recurrent Neural Networks.
Apart from the theoretical work, my thesis shows a strong applied component. Indeed, Anne, Marc-Olivier and I implemented our DIG-DAG related algorithms into a Python 3 module, which has been at the core of a collaboration with Nokia’s business units.
Please follow this link for more details about my thesis.
The recent pandemic definitely taught us that social relationships are precious from both a personal and scientific point of view. Therefore, here are some topics I would be happy to discuss if we meet around a cup of coffee (it can be a glass of water as well)!