Clinical Genetics • Biostatistics
Biostatistician developing bioinformatics pipelines for rare disease diagnostics and genetic variant analysis
Genomics: AlphaGenome integration, pseudogene pathogenicity prediction, variant interpretation pipelines
Analysis: Python (pandas, scikit-learn, PyTorch), R (tidyverse, tidymodels), survival analysis, causal inference
Clinical: Pharmacoepidemiology, rare disease diagnostics, HPO phenotype modeling, regulatory genomics
Infrastructure: C/C++ (Ecole 42), Linux, SQL, Git
- Processed pseudogene regulatory effect prediction models
- Temporal phenotypic trajectory analysis using HPO ontologies
- UMAP-based phenotypic space visualization

