# Publications

### Computational Outlier Detection Methods in Sliced Inverse Regression

Sliced inverse regression (SIR) focuses on the relationship between a dependent variable $y$ and a $p$-dimensional explanatory variable …

### The relationships between Sensory and Nutritional Quality are not consistent from one muscle to another in the same bovine carcass

The sensory and nutritional qualities of meats are strong expectations for consumers. However, these two types of quality are sometimes …

### Advanced topics in Sliced Inverse Regression

Since its introduction in the early 90’s, the Sliced Inverse Regression (SIR) methodology has evolved adapting to increasingly complex …

### Data-Driven Sparse Partial Least Squares

In the supervised high dimensional settings with a large number of variables and a low number of individuals, variable selection allows …

### ddsPLS: A Package to Deal with Multiblock Supervised Problems with Missing Samples in High Dimension

The ddsPLS method considers regression and classification problems in the context of multiblock structured covariate data sets taking …

### An Original Methodology for the Selection of Biomarkers of Tenderness in Five Different Muscles

For several years, studies conducted for discovering tenderness biomarkers have proposeda list of 20 candidates. The aim of the present …

### High-dimensional multi-block analysis of factors associated with thrombin generation potential

The identification of novel biological factors associated with thrombin generation, a key biomarker of the coagulation process, remains …

### Une PLS multivoie parcimonieuse avec observations manquantes pour données hétérogènes

Several sets of variables can be analyzed simultaneously by canonical correlation in a multi-way analysis. These sets of variables are …

### Systems Vaccinology Identifies an Early Innate Immune Signature as a Correlate of Antibody Responses to the Ebola Vaccine rVSV-ZEBOV

Predicting vaccine efficacy remains a challenge. We used a systems vaccinology approach to identify early innate immune correlates of …

# Talks and posters

### Sélection de variables en régression SIR par seuillage doux/dur de la matrice d'intérêt.

An interesting feature of Sliced Inverse Rregression (SIR) is that it allows the construction of indices, as linear combinations of the …

### Imputation for supervised learning problems in high dimension

The problem of missing data often occurs in data analysis. Missing values of the type MAR (Missing At Random) are cosidered here. Then, …

### Koh-Lanta, missing data imputation in supervised context

The imputation is the process that estimates the missing values. Simplest approaches impute to fixed values such as mean/median based …

### Detection of atypical individuals in SIR regression

How can we deal with atypical observations in SIR regression. This can be generalized to any predictive model.

### How to deal with missing values in the high dimensional supervised context?

A reflexion about missing data imputation in the supervised context, with a solution and simulation results.

### Multiblock supervised analyses. Should we really normalize blocks?

In recent years, data analysis methods have had to deal with new type of heterogeneous data sets. Multi-omics studies are perfect …

### Détection d’individus atypiques en régression SIR.

Sliced inverse regression (SIR) focuses on the relationship between a dependent variable $y$ and a $p$-dimensional explanatory variable …

### High-dimensional multi-block analysis of factors associated with thrombin generation potential

The identification of novel biological factors associated with thrombin generation, a key biomarker of the coagulation process, remains …

### Apprentissage supervisé pour données massives multi-blocs incomplètes

Les récentes innovations techniques ont permis la production de données massives en biologie, comme les données omiques par exemple …

### A mono/multi-block sparse PLS for heterogeneous data with missing samples

This is the last update of the current project on dealing with missing samples in multi-block context for supervised datasets.

# Teaching

#### How to deal with missing values, 2021

What are missing values and how to deal with them using R classical packages.

#### Partial Least Squares (PLS), an introduction

What is PLS and why ?

#### Introduction à l'apprentissage statistique

That course shows main goals of statistical learning detailing bias/variance tradeoff and most of my thesis results.

#### Genetics project in the context of high dimensional data

That course shows main goals of supervised and unsupervised methods to help students to prepare a project dealing with the 10X data set.

#### Supervised and unsupervised analysis for high dimensinal data

That course shows main goals of supervised and unsupervised methods and their applications through the spectrum of my proper applications.

#### PLS and sparse PLS

Introduction to PLS (Partial Least Square) and one of its sparse versions.

#### Maths and function analysis

Mathematical courses designed to fit to French second year medical students of the Double Cursus Sante Sciences expecting strong mathematical bases.

#### Maths and linear algebra

Mathematical courses designed to fit to French second year medical students of the Double Cursus Sante Sciences expecting strong mathematical bases.

#### Statistics and Probability

Mathematical courses designed to fit to second year medical students expecting strong mathematical bases.

#### Single-cell datasets visualizations : PCA Versus tSNE

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.

# Experience

October 2019 – Present
Talence

#### ASTRAL team

Perform high dimensional analyses through statistical modeling keeping interpretability
October 2016 – October 2019
Bordeaux

#### SISTM team, U1219

Data analysis and eigen-space decomposition for supervised problems. Compressing many thoughts :

Missing Value + High-dimension = Act carefully

October 2015 – October 2016
Bordeaux

#### SISTM team, U1219

Discover public health and its tools.
February 2015 – August 2015
Châtenay-Malabry/Montréal

#### CentraleSupelec and Montreal Polytechnique University

Finite elements modelisation and real application to bridge movements with Fourier analysis. Great teams and amazing moments!
September 2014 – January 2015
Gif-sur-Yvette

#### CentraleSupelec and GE Healthcare

A team project dedicated to image segmentation using SVM tools and morphological transformation. A work done in cooperation with GE Healthcare and supervised by Arthur Tenenhaus and Laurent Le Brusquet.
June 2014 – September 2015
Nice

#### Lagrange Laboratory

Extra large images implying special dedicated tools such as spherical wavelets but also deeply open minds. What else to say ? It is hard!

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