I am data scientist in the Department of Economic Studies at the French national statistical institute, Insee. I use Big Data and computational methods in microeconometric and data science fields.
I work with and sometimes use
C++ to improve performance. I also use
Python for big data analysis. I am a huge
Git fan. Most of my work is available on my Github page or Gitlab page page. I maintain the
utilitR project which is a collective effort involving many people from French administration to propose a high-quality documentation regarding software.
I currently teach Python for Data Scientists and Economists at ENSAE Paris Tech. You can find the course website here and the underlying Github repository . I used to teach urban economics for Master students at Sciences Po Paris. See teaching section for more details.
Msc Statistics and Data Science, 2017
Msc Econometrics, 2013-2018
ENS Lyon & Paris School of Economics
Msc Applied Mathematics, 2015-2017
Université Pierre et Marie Curie (Jussieu), Paris VI
foodbowl est un package Python visant à simplifier l’utilisation d’ElasticSearch pour effectuer des appariements flous
Le projet utilitR est une documentation opensource sur le logiciel R pour la manipulation de données
A docker container integrating together R and Python (anaconda environment) that can be used in gitlab CI/CD
I use microsimulation as a tool to understand capital accumulation in a life cycle perspective
I explore the evolution of segregation in the three French biggest cities within a typical day using individual mobile phone data combined with traditional data sources. I propose an innovative methodology to build within-day segregation indices and study segregation dynamics at fine spatial granularity
We bring together mobile phone and geocoded tax data on the three biggest French cities to shed a new light on segregation that accounts for population flows. Mobility being a key factor to reduce spatial segregation, we build a gravity model on an unprecedent scale to estimate the heterogeneity in travel costs.
Residential segregation represents the acme of segregation. Low-income people spread more than high-income people during the day. Distance plays a key role to limit population flows. Low-income people live in neighbourhoods where the spatial frictions are strongest.
Les données de mobilité issues de la téléphonie mobile permettent d’analyser la mixité sociale au-delà des seuls lieux de résidence. En …
We bring together mobile phone and geocoded tax data on the three biggest French cities to shed a new light on segregation that …
D’après ses estimations construites à partir de comptages issus de la téléphonie mobile, l’Insee estime que 1,4 million de résidents de …