Data Scientist (Life Sciences)
Bio-Informatics: High-Throughput « omics »
My research activities involve basic and applied research in the field of computational big data “omics”-biology.
My experience was consolidated through today over 15 years of experience in the field of computational biology, including 13 in the field of high-throughput data screening for public health at the Institut Pasteur. By training, I have a university Master’s degree in biotechnology, along with a PhD doctoral degree in computer science (INRIA/LaBRI). I obtained two postdoctoral fellowships in the field of statistical analysis of genomic data (University of Basel, Switzerland & École Normale Supérieure, Paris).
I currently work in the Unit of Research and Expertise Environment and Infectious Risks. My main activities range from data analysis of high-throughput sequencing data, to the design and development of analytical tools for characterizing new pathogens in the context of emerging diseases, and genetics involving microbial populations (with a special focus on advanced phylogenetic analyses).
The area of expertise of the research laboratories I’ve been working with, involve major forecasting players, in terms of bleeding-edge health-related applications. They have followed the evolution pursued in the field of biotechnology, with the migration to high-speed technologies, notably in the field of high-throughput sequencing (HTS/NGS) for biology and the wealth of metadata and text resources they deliver. I am very much interested in automated data processing in this very field of biology and human public health.
Project-wise, my work is geared towards upgrading daily-use HTS as a complementary tool to more traditional public health diagnostics.
My current activities focus on metagenomics, involving methods that provide public health sciences with the ability to detect a plethora of known and unknown viromes in clinical samples. In this context, Next-generation sequencing (NGS/HTS) technologies have a tremendous impact, taking into account currently available and possible future platforms and bioinformatics. The massive data produced by NGS presents a significant challenge for data analysis and management. As such, advanced bioinformatic tools have become essential for the successful application of NGS technology.