As a Senior Data Scientist in Novo Nordisk, my goal is to find genetic predictors of disease that are useful for diagnosis, treatment, and understanding the underlying biology. To that end, I use machine learning to assist with modeling, biomarker discovery and variant interpretation.
I have over 10 years of experience in computational biology and applied statistics. I completed my PhD in MINES ParisTech, under the supervision of Chloé-Agathe Azencott. In my thesis I explored graph-based methods for genetic studies. Afterwards, I was accepted into RIKEN’s SPDR program to conduct my postdoctoral research with Makoto Yamada. I focused on the development and application of novel machine learning methods for feature selection.
|Jan 13, 2024
|Our preprint on predicting cardiovascular disease risk using interpretable AI was just published on medRxiv. This was a joint work with Microsoft scientists.
|Nov 9, 2023
|Our preprint on detecting epistasis using quantum computing was just published on medRxiv.
|Mar 1, 2023
|I joined the Novo Nordisk Research Center Oxford as Senior Data Scientist. I will be applying machine learning to genetics.
- The functional impact of alternative splicing in cancerCell reports, 2017
- Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional dataBioinformatics, 2019
- Interpretable network-guided epistasis detectionGigaScience, 2022
- A network-guided protocol to discover susceptibility genes in genome-wide association studies using stability selectionSTAR protocols, 2023
- Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker IdentificationmedRxiv, 2024