Machine Learning Researcher @ Xaira Therapeutics
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I am a PhD student @ Oxford Department of Statistics, supervised by Pr Yee Whye Teh. I am most interested in probabilist machine learning and have been working more specifically on deep generative modelling and geometry. I previously graduated in Applied Mathematics from Ecole des Ponts ParisTech and in Machine Learning from Ecole Normale Supérieure de Cachan.
University of Oxford
Doctor of Philosophy - PhD, Statistics / Machine learning
January 1, 2017 – January 1, 2020
École Normale Supérieure Paris-Saclay
Master 2 (M2), Mathématiques / Vision / Apprentissage (Machine Learning)
January 1, 2016 – January 1, 2017
Ecole Nationale des Ponts et Chaussées
Applied Mathematics & Computer Science
January 1, 2013 – January 1, 2017
Lycée Georges Clémenceau (Nantes)
CPGE, Physique & Sciences de l'ingénieur
January 1, 2011 – January 1, 2013
Xaira Therapeutics
Machine Learning Researcher
March 1, 2024 – Present
University of Cambridge
Postdoctoral Research Associate
June 1, 2022 – February 1, 2024
University of Oxford
Postdoctoral Research Associate
September 1, 2021 – May 1, 2022
Facebook AI
Machine Learning Research Intern
September 1, 2019 – December 1, 2019
New York, United States
University of Oxford
PhD in Statistics/Machine Learning
September 1, 2017 – August 1, 2021
University of Oxford
Research Intern in Statistics/Machine Learning
May 1, 2017 – September 1, 2017
Criteo
Machine Learning Intern
January 1, 2016 – July 1, 2016
Greater Paris Metropolitan Region
BAM Lab
Web Developer Intern
July 1, 2015 – December 1, 2015
Greater Paris Metropolitan Region
Ifsttar
Data Scientist Intern
April 1, 2014 – July 1, 2014
Bâtiment Bienvenüe
“Learning from the crowd”
March 1, 2015 – June 1, 2015
For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple experts or annotators. In practice, there is a substantial amount of disagreement among the annotators, and hence it is of great practical interest to address conventional supervised learning problems in this scenario. We describe a probabilistic approach for supervised learning when we have multiple annotators providing (possibly noisy) labels but no absolute gold standard. The studied algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.
Prelib - Bicycle sharing system app
October 1, 2014 – June 1, 2015
Lead in a 4 person project on prediction of bicycle sharing system (bss) availability, developed with agile methodology and supervised by the company THEODO. Paris bss users can now easily find the nearest functional bicycle. The github repository can be found here: https://github.com/NathanRxl/Prelib
Cultural Fit Analysis
The candidate's background is heavily skewed towards academic research and Machine Learning, which might not be a direct fit for a pure 'Data Analyst' role that often requires more business-oriented data visualization, reporting, and SQL expertise. While the analytical skills are strong, the project diversity and explicit skill mentions do not strongly align with typical Data Analyst responsibilities. The 'Web Developer Intern' role is an outlier and doesn't seem to be a recent focus.
Soft Skills & Operational Fit
The candidate's project descriptions suggest an ability to work in teams (Prelib project) and apply agile methodologies. The research roles imply strong analytical thinking, problem-solving, and independent work. However, specific soft skill assessments are not available.