Caio Vaz Rimoli

He obtained a Bachelor's degree in Physical and Biomolecular Sciences (2012) and a Master's degree in Applied Physics: Biomolecular Physics (2015) from the University of São Paulo (IFSC/USP), Brazil. He then acquired his Ph.D. in Biophysics from the Institut Fresnel at Aix-Marseille Université (France, 2020). Between 2022-2023, he worked in Paris as a postdoctoral researcher in the group of Prof. Dr. Sylvain Gigan (Laboratoire Kastler Brossel, LKB - ENS/PSL/SU/CdF/CNRS) and Dr. Cathie Ventalon (IBENS/ENS), at the traditional École Normale Supérieure (ENS) in Paris. In 2024, he passed a French public concourse to become a tenured Research Engineer at INSERM, the French public institute for biomedical research, to work in the teams of Dr. Ludger Johannes (Institut Curie, Paris) and Dr. Charles Kervrann (INRIA, Rennes). His main role is to be a multidisciplinary bridge researcher, i.e. connecting the research carried out at the Institut Curie (Paris) related to chemical-biology of cancer, with the research of the engineers and mathematicians at INRIA (Rennes) related to the development of new Artificial Intelligence methods for the processing of microscopy images. His current research consists of developing and converging different quantitative strategies for fluorescence microscopy (e.g., super-resolution) and image analysis using Artificial Intelligence in order to obtain greater interpretability of biophysical parameters of biomembranes and biomolecular complexes in cells. In general, he has experience in the development of new quantitative optical methods dedicated to applications in biology and medicine. Optical techniques include: spectroscopic methods (fs-Pump-probe spectroscopy, SFG nonlinear vibrational spectroscopy), polarized and super-resolution fluorescence microscopy (4polar-STORM, polar-spinning disk, polar-confocal,4polar-widefield, 4polar-TIRF microscopy) and minimally invasive MMF endoscopy using multivariate image analysis algorithms (demixed fiber photometry via Non-negative Matrix Factorization, NMF). (Source: Lattes Curriculum)