Overview: I'm interested in understanding cell-level biology using bioimage analysis. My research centers on facing the challenges when applying machine-learning techniques to microscopy images and on contributing to biological discoveries with it. Previously, I developed methods to process TEM images and phase-contrast time-lapse movies to contribute to the characterization of cancer cell motility. I have also conceived new biostatistical approaches to analyse big data. I'm also one of the creators of deepImageJ, an environment to bridge deep-learning to ImageJ. A crucial part of my work and dedication is to make computational tools accessible (open and user-friendly) and reusable, and train non-experts to benefit from them. As a postdoc in Ridardo's lab I'll be learning (a lot) of super-resolution microscopy, and building novel machine-learning methodologies applied to it.
Research themes with our lab
Publications with our group (see more on Google Scholar)
| DeepBacs: Bacterial image analysis using open-source deep learning approaches |
Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques
Published in bioRxiv, November 2021 (see preprint)