Emilie Mathian

Background

About Emilie Mathian

I am a postdoctoral researcher at the International Agency for Research on Cancer (IARC-WHO), specializing in the application of deep learning techniques, particularly Transformer-based models, to histopathological images. My research is dedicated to developing innovative methods that link morphological features with molecular profiles, with the goal of advancing the understanding and classification of rare lung tumors. I completed my PhD at IARC in collaboration with the École Centrale de Lyon, where I focused on self-supervised learning models to enhance the interpretability of pathological images. In addition to my work in deep learning, I have contributed to various statistical and multi-omics projects, involving the modeling of clinical and large-scale data. This experience has deepened the strong foundation in bioinformatics, statistics, and mathematical modeling that I acquired during my studies at INSA Lyon. My technical expertise includes a wide range of deep learning and data science tools, such as Python (with libraries like PyTorch, TensorFlow, Scikit-learn), R, Nextflow, Slurm, and GitHub. I have a proven track record in model development, data analysis, and interdisciplinary collaboration, as demonstrated through my publications and presentations at international conferences. Passionate about integrating cutting-edge technologies to address complex biomedical challenges, I am eager to apply my expertise to innovative research or industry projects that push the boundaries of medical science.