OPtImization for large Scale biomedical data
OPtImization for large Scale biomedical data

The objective of the OPIS project is to design advanced optimization methods for the analysis and processing of large and complex data. Applications to inverse problems and machine learning tasks involving high-dimensional biomedical data, e.g. 3D CT, PET, ultrasound images, and MRI are targeted in this research project. The focus is put on optimization methods able to tackle data with both a large sample-size (“big N”) and/or many measurements (“big P”). The explored methodologies are grounded on nonsmooth functional analysis, fixed point theory, parallel/distributed strategies, and neural networks. The new optimization tools that are developed are set in the general framework of graph signal processing, encompassing both regular graphs (e.g., images) and non-regular graphs (e.g., gene regulatory networks).

Centre(s) inria
Inria Saclay Centre
In partnership with
Université Paris-Saclay


Team leader

Joyce Soares Brito

Team assistant