Solaris is an ERC starting grant project, which started in March 2017 and ended in February 2022.

Project Description

Machine learning has become a key part of scientific fields that produce a massive amount of data and that are in dire need of scalable tools to automatically make sense of it. Unfortunately, classical statistical modeling has often become impractical due to recent shifts in the amount of data to process, and in the high complexity and large size of models that are able to take advantage of massive data. The promise of SOLARIS is to invent a new generation of machine learning models that fulfill the current needs of large-scale data analysis: high scalability, ability to deal with huge-dimensional models, fast learning, easiness of use, and adaptivity to various data structures. To achieve the expected breakthroughs, our angle of attack consists of novel optimization techniques for solving large-scale problems and a new learning paradigm called deep kernel machine. This paradigm marries two schools of thought that have been considered so far to have little overlap: kernel methods and deep learning. The former is associated with a well-understood theory and methodology but lacks scalability, whereas the latter has obtained significant success on large-scale prediction problems, notably in computer vision. Deep kernel machines will lead to theoretical and practical breakthroughs in machine learning and related fields. For instance, convolutional neural networks were invented more than two decades ago and are today's state of the art for image classification. Yet, theoretical foundations and principled methodology for these deep networks are nowhere to be found. The project will address such fundamental issues, and its results are expected to make deep networks simpler to design, easier to use, and faster to train. It will also leverage the ability of kernels to model invariance and work with a large class of structured data such as graphs and sequences, leading to a broad scope of applications with potentially groundbreaking advances in diverse scientific fields.


Principal Investigator

  • Julien Mairal

Research engineers

  • 2021 – 2022    Gedeon Muhawenayo

  • 2020 – 2021    Theo Bodrito

  • 2019    Alexandre Zouaoui

  • 2019    Bruno Lecouat

  • 2019    Xavier Martin

  • 2017 – 2019    Ghislain Durif, now research engineer at CNRS, Montpellier

Post-docs (who are either funded by, or have participated to the project)

  • 2021 – 2022    Michael Arbel

  • 2020 – 2022    Margot Selosse

PhD students (who are either funded by, or have participated to the project)

Master students


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2021: We organize PAISS, the PRAIRIE AI summer school from July 5 to July 9th, 2021.

{}{imgleft}{..resourceslogopaiss.jpg}{paiss logo}{60px}{50px}{} 2019: We organized PAISS, the PRAIRIE AI summer school from October 3rd to October 5th. The 2020 event is unfortunately cancelled due to the COVID-19 situation.

2019: We organized OSL 2019, Optimization and Statistical Learning workshop.

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2018: Along with Naver labs Europe, we organized PAISS, the PRAIRIE AI summer school from July 2nd to July 6th.


655, avenue de l'Europe
38330 Montbonnot Saint Martin

Prospective students or post-docs may contact me at

email: julien.mairal AT