Thomas Hermet

Thomas Hermet is a dedicated bioinformatics master's student currently based in Nice, actively engaged in advancing research at the intersection of computational biology and genomics. As part of his current role at IPMC CNRS-U, NICE SOPHIA, Thomas contributes to cutting-edge projects that leverage machine learning and statistical analysis to decode complex biological data. His expertise in bioinformatics tools such as Bash, R, and GitHub, combined with his experience in multi-omics integration and statistical data analysis, positions him as a valuable asset in multidisciplinary research teams.

During his Stage de Master 2, Thomas has been working on innovative applications of autoencoders for genetic screening, particularly focusing on CRISPR-based gene editing coupled with single-cell RNA sequencing (scRNA-seq). His project aims to develop robust computational pipelines for identifying genetic modifications and understanding their functional impacts at the single-cell level. This involves utilizing advanced bioinformatics techniques, including data normalization, dimensionality reduction, and visualization with tools like Scanpy, to interpret high-dimensional omics datasets.

Thomas’s background also includes a notable internship at the Informatics and Systems Lab (LIS) at the University of Toulon, where he specialized in bioacoustics—automating the recognition of bird vocalizations across different regions. This experience honed his skills in signal processing, statistical analysis, and machine learning, which he now applies to genomic data analysis. His multidisciplinary expertise, encompassing structural biochemistry, bioinformatics, and statistical modeling, enables him to approach biological questions with a comprehensive and innovative perspective, making significant contributions to the evolving landscape of computational genomics and precision medicine.