Section: Overall Objectives
OPIS
Mathematical optimization is the key to solving many problems in Science, based on the observation that physical systems obey a general principle of least action. While some problems can be solved analytically, many more can only be solved via numerical algorithms. Research in this domain has been steadily ongoing for decades.
In addition, many fields such as medecine continue to benefit from considerable improvements in data acquisition technology, based on sophisticated tools from optics and physics (e.g., new laser sources in microscopy, multicoil systems in MRI, novel Xray schemes in mammography, etc). This evolution is expected to yield significant improvements in terms of data resolution, making the interpretation and analysis of the results easier and more accurate for the practitioner. The large amounts of generated data must be analyzed by sophisticated optimization tools so that, in recent years, optimization has become a main driving force fostering significant advances in data processing. Previously hidden or hard to extract information can be pried from massive datasets by modern recovery and data mining methods. At the same time, automated decision and computeraided diagnoses are made possible through optimal learning approaches.
However, major bottlenecks still exist. Recent advances in instrumentation techniques come with the need to minimize functions involving increasingly large number of variables (at least one billion variables in 3D digital tomography modality), and with increasingly complex mathematical structure. The computational load for solving these problems may be too high for even stateoftheart algorithms. New algorithms must be designed with computational scalability, robustness, and versatility in mind. In particular, the following severe requirements must be fulfilled: i) ability to tackle highdimensional problems in a reasonable computation time; ii) lowrequirements in terms of memory usage; iii) robustness to incomplete or unreliable information; iv) adaptivity to statistically varying environments; v) resilience to latency issues arising in architectures involving multiple computing units.
These difficulties are compounded in the medical and biomedical areas. In these contexts, datasets are not easily available due to patient confidentiality and/or instrument limitations. Moreover, highlevel expertise is necessary to interpret the data which can be of very high dimension. Finally, the developed analysis methods must be reliable and interpretable by the medical/biomedical community.
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 in biomedical imaging will be major outcomes of this research project. We will seek optimization methods able to tackle data with both a large samplesize (“big $N$" e.g., $N={10}^{9}$) and/or many measurements (“big $P$" e.g., $P={10}^{4}$). The methodologies to be explored will be grounded on nonsmooth functional analysis, fixed point theory, parallel/distributed strategies, and neural networks. The new optimization tools that will be developed will be set in the general framework of graph signal processing, encompassing both regular graphs (e.g., images) and nonregular graphs (e.g., gene regulatory networks).
More specifically, three main research avenues will be explored, namely:

proposing novel algorithms able to encompass highdimensional continuous optimization problems, with established convergence guarantees, and that are wellsuited to parallel implementation;

designing efficient optimization approaches for the resolution of graph signal processing and graph mining problems;

developing a new generation of deep learning strategies, characterized by robustness guarantees, fast training and suitable account for prior information.
Our research program is detailed in the next subsections. We also indicate applications in the medical and biomedical areas on which our program is expected to have a significant impact.