Descripción del proyecto
Nuevos algoritmos para gestionar grandes cantidades de datos de tratamiento de imágenes
Varios campos científicos, como la biología, la medicina, la física y la química, generan una cantidad de datos cada vez mayor que deben tratarse y almacenarse eficientemente. Para buscar entre esta avalancha de datos, se tienen que diseñar nuevos algoritmos que sean informáticamente escalables, robustos y versátiles. El marco de maximización-minimización (MM), que emplea una clase de algoritmos de optimización eficientes y eficaces, desempeña un papel decisivo en el tratamiento eficiente de los datos. El proyecto financiado con fondos europeos MAJORIS diseñará nuevos algoritmos de MM que puedan seguir siendo eficaces al tratar datos masivos. El trabajo del proyecto incluirá el desarrollo de estrategias de aceleración y análisis de convergencia. Las aplicaciones específicas son la microscopía multifotónica de alta resolución, la reconstrucción de imágenes de tomosíntesis de mama y el tratamiento de datos de espectrometría de masas.
Objetivo
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 proved essential over many years. In addition, science in general is changing. Increasingly, in biology, medicine, astronomy, chemistry, physics, large amounts of data are collected by constantly improving signal and image acquisition devices, that must be analyzed by sophisticated optimization tools. In this proposal, we consider handling optimization problems with large datasets. This means minimizing a cost function with a complex structure and many variables. The computational load for solving these problems is too great for even state-of-the-art algorithms. Thus, only relatively rudimentary data processing techniques are employed, reducing the quality of the results and limiting the outcomes that can be achieved via these novel instruments. New algorithms must be designed with computational scalability, robustness and versatility in mind.
In this context, Majorization-Minimization (MM) approaches have a crucial role to play. They consist of a class of efficient and effective optimization algorithms that benefit from solid theoretical foundations. The MAJORIS project aims at proposing a breakthrough in MM algorithms, so that they remain efficient when dealing with big data. I propose to tackle several challenging questions concerning algorithm design. These include acceleration strategies, convergence analysis with complex costs and inexact schemes. I will also tackle practical, massively parallel and distributed architecture implementations. Three specific applications are targeted: super-resolution in multiphoton microscopy in biology; on-the-fly reconstruction for 3D breast tomosynthesis in medical imaging; and mass spectrometry data processing in chemistry.
Ámbito científico
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- natural sciencesphysical sciencesastronomy
- natural scienceschemical sciencesanalytical chemistrymass spectrometry
- natural sciencescomputer and information sciencesdata sciencedata processing
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
78153 Le Chesnay Cedex
Francia