Alexis Hucteau

My expertise lies at the intersection of computational genomics and Machine Learning. Specializing in high-throughput sequencing data processing (NGS bulk & single-cell), I support biotech companies and research institutes in engineering bio-analytical pipelines and discovering predictive biomedical signatures, for example.

Bioinformatics, NGS & ML Expertise

NGS Pipelines & Raw Data Processing: I design end-to-end processing architectures, from read quality control (FastQC, MultiQC) to quantification (Salmon, STAR), through differential expression analysis (DESeq2, EdgeR) or single-cell analysis (Scanpy/Seurat).

Machine Learning for High Dimensionality: Expert in dimensionality management (p≫n), I combine dimension reduction (PCA, UMAP) and Deep Learning probabilistic models (VAE via scvi-tools) for batch effect correction and integration of heterogeneous datasets.

Interpretability & Systems Biology: I translate algorithmic performance into biological reality. I use interpretability methods (SHAP, feature importance) correlated with functional enrichment analyses (GSEA, KEGG, Reactome) to identify actionable biomarkers and therapeutic targets.

Pipeline Engineering & MLOps: Concerned with scientific reproducibility, I industrialize workflows using pipeline orchestrators (Nextflow, Snakemake), containerization (Docker, Singularity), and rigorous versioning (Git) for auditable and production-ready analyses.