Florian Hébert

Developed a complete stack to train neural networks for image classification, from image annotation to CNN training and assessment
Optimized and fine-tuned a deep learning-based image segmentation method, to perform automatic segmentation and size estimation of droplets. Enabled to perform automatic correction of sample concentration by estimated droplet volume, which drastically reduced concentration estimation variability and chip production scrap rates
Led mathematical studies for the development of new analysis features to be included in the software, including the derivation of theoretical expressions and the development of data simulation algorithms to check the accuracy of these formulas
Developed automated defect detection tools, enabling effective defect tracking and root cause identification to improve digital PCR chip quality and reliability
Implemented interactive image annotation tools embedded in web applications feeding databases to collect essential data for the development and refinement of automated defect detection algorithms
Developed web scraping tools for the molecular biology team, automating repetitive data collection tasks, resulting in faster digital PCR assay development
Developed a new vignetting (flat-field) correction method, enabling the release to market of numerous instruments

Pioneered the development of monitoring and analysis tools for the R&D team, enabling to efficiently identify product quality improvement solutions
Developed, assessed and integrated machine learning - based bacteria recognition algorithms

Comparison and development of global testing methods in the context of genome-wide association studies (GWAS), namely for the detection of
- gene - disease association effects
- gene - environmental factor interaction effects
- gene - gene interaction effects
Development of genotypic data simulation algorithms used to conduct realistic simulation studies, specifically to assess the performance of competing detection methods in GWAS
Regression on highly correlated explanatory variables; application on chemometrics datasets

General statistics for agronomy engineering students

Tutoring in mathematics for students preparing an applied mathematics bachelor degree

Resume