Bio

I was a French PhD Candidate in Statistics at CREST-ENSAE, under the direction of Dr. Olivier Catoni (CNRS Research Director). I am mainly working on various type of k-means and PAC-Bayesian theory with application to images. See some slides in french of my research and teaching activities.
I graduated from ENSAE ParisTech, completing an Engineer’s degree in Data Science, and from ENS Paris-Saclay, completing an MSc in Mathematics, Vision and Machine Learning. I also hold an MSc in Statistics and Econometrics from Toulouse School of Economics.

Research

You can find my PhD dissertation Information k-means, fragmentation and syntax analysis: A new approach to unsupervised Machine Learning and my PhD defense presentation (Slides)
Full R and C++ source code for the fragmentation and syntax analysis algorithm is available on GitHub
Presentation of a new type of k-means using the Kullback divergence as a distortion measure, with application to digital images (Slides)
New preprint about Risk bounds for k-means and information k-means New bounds for k-means and information k-means
G.Appert and O.Catoni

2020-2021, as a Temporary Teaching and Research Assistant in Statistics at University of Paris 1 Pantheon-Sorbonne

2019-2020, as a Teaching Assistant (“ATER” position) at Paris-Saclay University

Statistical Modeling with applications in R, at ENSTA ParisTech to 2nd year engineering students.
Probability and Statistics at Polytech Paris Sud to 1st year engineering students.
Statistics (revision course) with applications in R, to Master 2 Mathematics and application students.
Measure, Integration theory and Probability to undergraduate students (“Magistère de Mathématiques”).
Mathematical modeling (mainly applied probability) to undergraduate students.
Revision tutorial, Probability to undergraduate students (“Magistère de Mathématiques”).

2016-2019, as a Teaching Assistant at ENSAE Paris Tech (list of courses)

Statistics 1 (mainly Inferential Statistics) + statistical projects , 2nd year engineering students.
Statistics 2 (mainly Statistical tests), 2nd year engineering students.
Probability theory, 1st year engineering students.
Introduction to Machine Learning, 2nd year engineering students.