I am a PhD in Biostatistics and currently in PostDoc in the SISTM team. I work on analysing high dimensional datasets (just a few individuals and thousands of variables) with longitudinal and or multi-block structures. SVD-based methods are what I love the most and I currently work on dealing with missing values in supervised context.
SISTM is mainly devoted to vaccine research and clinical trial data analyses in the context of largely scaled datasets. This is on of the few INSERM/INRIA teams.
I unreasonably work on the ddsPLS (CRAN-R-package) for which I build a R-Vignette. That vignette is updated as often as I can. This is a titanous task and I also work on a py_ddspls (GitHub-Python-package) which is under developpment. My wish is to be useful to both of the communities which have so much to share!
PhD in Biostatistics, 2019
Université de Bordeaux
Research Master, Applied Mathematics for Image and Signal Processing, 2015
CentraleSupelec and Paris-Sud University
That course shows main goals of supervised and unsupervised methods to help students to prepare a project dealing with the 10X data set.
That course shows main goals of supervised and unsupervised methods and their applications through the spectrum of my proper applications.
Introduction to PLS (Partial Least Square) and one of its sparse versions.
Mathematical courses designed to fit to French second year medical students of the Double Cursus Sante Sciences expecting strong mathematical bases.
Mathematical courses designed to fit to French second year medical students of the Double Cursus Sante Sciences expecting strong mathematical bases.
Mathematical courses designed to fit to second year medical students expecting strong mathematical bases.
Among new RNA sequencing methods, one permits to detail the RNA (or DNA) of a sample at a cellular level. That is the Single-Cell RNA-seq. That course explores methods to visualize those datasets.
Data analysis and eigen-space decomposition for supervised problems. Compressing many thoughts :
Missing Value + High-dimension = Act carefully
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