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PR[AI]RIE Researcher

Olivo-Marin Jean-Christophe


Jean-Christophe Olivo-Marin is Professor and Head of the Bioimage Analysis unit and the director of the institut Carnot Pasteur Microbes et Santé. He chaired the Cell Biology and Infection Department (2010-2014), was CTO and Director of the Center for Innovation and Technological Research (2015-2018) at Institut Pasteur, and was a cofounder and first CTO of the Institut Pasteur Korea, Seoul (2004-2005). Previous to that, he was a Staff Scientist at the European Molecular Biology Laboratory, Heidelberg, from 1990 to 1998. He received the PhD and HDR degrees in optics and signal processing from the Institut d’Optique Théorique et Appliquée, University of Paris-Orsay, France. He is a Fellow of IEEE, SPIE, OPTICA, and AAIA, an IEEE Signal Processing Society Distinguished Lecturer and an IEEE Engineering in Medecine and Biology Society Distinguished Lecturer. He is the recipient of the Distinguished Service Award from IEEE Engineering in Medicine and Biology Society and of the Prix Thérèse Lebrasseur from Fondation de France. He has been the founding Editor-in-Chief of the journal Biological Imaging published by Cambridge University Press (2021-2024), a senior area editor of the IEEE Signal Processing Letters (2013-2015), and a member of the Editorial Board of the journals Medical Image Analysis (2010-2015), BMC Bioinformatics (2016-2020) and IEEE Open Journal Computer Society (2025- ). He was General Chair of the IEEE International Symposium on Biomedical Imaging in 2008, Chair of the IEEE International Symposium on Biomedical Imaging Steering Committee (2014-2016), and Chair of the IEEE SPS BioImaging and Signal Processing Technical Committee (BISP-TC) (2009-2011). His research interests are in mathematical and machine learning approaches for bioimage analysis and computational pathology, pattern recognition and biophysics analysis for cellular dynamics.

Informations

Project in Prairie

This project addresses topics in cell biology, neurosciences and biomedicine that require an interdisciplinary research approach combining computational bioimaging with state-of-the-art AI. We are developing methods for bioimage analysis based on Deep Learning to perform multi-task learning cell segmentation, (semi)automatic image annotation, analysis of cell spatial distributions in cancer immunotherapy, and computational pathology