I am a PostDoc in the ASTRAL team. I work on analysing high dimensional datasets (just a few individuals and thousands of variables) with longitudinal and or multi-block structures. I currently work on dealing with missing values in supervised context, which is the future of data analysis problems I think (not my work but this problem I mean…).
ASTRAL, Advanced StatisTical infeRence And controL. The research activities of our team mainly focus on the development of advanced statistical and probabilistic methods for the analysis and the control of complex stochastic systems. Our approach is based on the classic triptych consisting of the following topics: Statistical/stochastic modeling, Estimation/calibration and Control/decision.
I unreasonably work on R and you can view the current stage on my packages on (GitHub)](https://github.com/hlorenzo). 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
Some new things.
What are missing values and how to deal with them using R classical packages.
What is PLS and why ?
That course shows main goals of statistical learning detailing bias/variance tradeoff and most of my thesis results.
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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