My area of research is statistics, machine learning and computational biology. I have a particular interest for biological applications. My research has so far been mostly focused on applying modern data processing techniques to histopathology. Histopathology examines skin pieces seen through a very powerful microscope. Histopathology data can be very large (>60 GB per image) and are very complex to analyse due to the high variability in cell shape, type and staining. Our goal is to use histopathological data in order to discover biologically relevant information which is predictive of treatment responses. I am currently working on two medical topics:
- Breast Cancer; This project has been developed with the help Fabien Reyal’s group and works with data from Tripple Negative Breast Cancer patients, who underwent chemotherapy. Patient responses are very different and we wish to find biologically relevant patterns or features that could predict the patient’s response to chemotherapy. The bottleneck of this project is nuclei segmentation within histopathology data. It is performed with the state of the art deep neural networks. To be able to quantify patients’ histopathological data will eventually lead to personal patient features such as proportion of cancerous cells with respect to the normal endothelial cells, or spatial arrangement features could be revealed.
- pan-cancer; Using the same biologically relevant content mentioned above, we wish to uncover patterns that lead to cancer subgroups.