The AMORNET Network aims at training a world-class cohort of doctoral researchers (DRs) who will invent and implement the next generation of mathematical algorithms solving geometric problems arising in robotics. We aim to build strong lasting links between strategically selected industry and academic partners, combining a wide range of mathematical expertise with advanced expertise on robotics design and geometry. The research programme targets, in particular, computational problems arising from soft robotics, a topical area where robotics manipulators are no more rigid but designed with flexible bodies. Soft robots are expected to be more secure when interacting with their environment and to enjoy more mobility properties. Hence, robotics applications of soft robots are numerous and appealing but, to let the Industry adopt them, there is an urgent need to sharpen their geometric designs, models, and identify their mobility properties. This gives rise to challenging and stimulating problems in computational mathematics, especially since these are non-linear but algebraic. The planned training network will provide research and training opportunities to a new generation of DRs, who, in the long-run, shall address the Grand Challenge of making computational mathematics efficient and accurate enough to solve these algebraic problems arising in robotics. This will enable the design of robotics manipulators which will be safer, more reliable and more efficient. The AMORNET Network involves a number of industrial partners and academic facilities that will be used to validate experimentally the theoretical achievements brought by the DRs. The AMORNET Network will deliver more efficient and accurate mathematical methods, algorithms for solving algebraic problems, and their particular applications to issues robotics combined with experimental validation will accelerate the adoption of soft robotics by the EU Industry.

The objective of this project is to develop a standalone trusted execution module that enables secure cloud quantum computing. This module will undergo validation within the project by demonstrating a full stack software-hardware integration of the world's first secure optical access to a photonic quantum computing implementation for multi-user quantum cloud applications. Over the next three years, the consortium will conduct a (1) study, (2) development, (3) testing, (4) validation, and (5) demonstration of the HSM-QCC concept to obscure the computational task of the cloud computer. This project builds on a decade of research and development in several complementary domains, including hardware security, Quantum Cloud Computing, Photonic experiment, and software compilation. The original theoretical idea of a trusted execution environment in the quantum setting, namely QEnclave, was proposed by the members of the consortium which demonstrated that scrambling input states by single-qubit rotations in a trusted environment is sufficient to secure any universal quantum computing. However, the implementation of this core idea requires the multidisciplinary complementary expertise of this consortium to ensure all pieces can be assembled together to demonstrate and validate the vision. The target trusted environment will be a modified Hardware Security Module (HSM) with single-qubit quantum rotation functionalities, co-located with a scalable quantum cloud platform. A remote user, utilizing a classical cryptographic link to a quantum cloud platform, can securely obfuscate its desired quantum computation. The scrambling of the state will be performed according to the principle of Universal Blind Quantum Computing inside the HSM. To achieve this goal, the consortium will employ a general-purpose compiler that maps the target quantum algorithm of a user to an interactive client-server protocol and tailor it for our secure module.

E-democracy is a form of government that allows everybody to participate in the development of laws. It has numerous benefits since it strengthens the integration of citizens in the political debate. Several on-line platforms exist; most of them propose to represent a debate in the form of a graph, which allows humans to better grasp the arguments and their relations. However, once the arguments are entered in the system, little or no automatic treatment is done by such platforms. Given the development of online consultations, it is clear that in the near future we can expect thousands of arguments on some hot topics, which will make the manual analysis difficult and time-consuming. The goal of this project is to use artificial intelligence, computational argumentation theory and natural language processing in order to detect the most important arguments, estimate the acceptability degrees of arguments and predict the decision that will be taken.

The ICI-Lab joint laboratory, a partnership between LIP6 and the company BodyCAP, aims to study and develop smart biomedical electronic devices to aid in the diagnosis of diseases of the intestinal tract. The ICI-Lab benefits from scientific expertise in AI and in embedded systems for biomedical devices of LIP6 and from the know-how and patents in the design and production of "Capsule" type medical devices of BodyCAP. The work carried out within the ICI-Lab involves the integration of digital processing, from low to high level, within video-endoscopic capsules to produce a new generation of medical devices which will allow: i. a multimodal analysis (image, pH, temperature, gas) within an endoscopic capsule; ii. an interpretation of the data for the explicability of the algorithms; iii. the improvement and automation of procedures for analyzing and interpreting images of the intestinal tract; iv. support to the medical teams in establishing a diagnosis; v. the improvement of the follow-up and care of gastroenterology patients. Images acquired by a capsule can sometimes show obstruction of the lumen of the intestine due to the presence of fluid and / or feces. The multi-modality will make it possible upstream to have additional information to the image and to reinforce the discriminatory nature in the detection of polyps. The treatments will be dedicated to the acquisition of the characteristics of the various data (images, pH, temperature, gas) and on their interpretation for the recognition of pathology markers. These will be based on artificial intelligence algorithms, including deep learning, vector support machines and decision trees. The study, design and validation of intelligent endoscopic video capsules are the purpose of this joint laboratory. The pooling of industrial resources and the assets of the 2 stakeholders will make it possible to develop a platform for in-vitro testing and validation using a bio-mechanical intestinal simulator. These tools will make it possible to create a unique test environment to validate the designed devices on a real life scale, in an artificial environment. Thanks to the partnerships already set up by BodyCAP with various technical platforms, validations will be continued on an animal model and in humans, with the aim of bringing innovative devices to the market in 2025 in order to support diagnosis and improve the effectiveness of screening for bowel pathologies.

The year 2022 has witnessed the advent of the first supercomputer able to perform over 10^18 floating-point operations per second, officially launching the exascale computing era. While exascale computing holds promises of unprecedented computational power, it also brings numerous significant challenges. Supercomputers have grown larger, more heterogeneous, and more power hungry, and so it has become much harder for complex numerical algorithms to achieve high performance and scalability. This project, MixHPC, confronts these challenges by harnessing the power of lower precision arithmetics, whose recent emergence on modern hardware represents one of the most significant developments of the High Performance Computing (HPC) landscape of the last few years. Reducing the precision makes computations faster, communications lighter, and power consumption greener. However, reducing the precision is also a risky approach that requires major algorithmic innovations in order to avoid compromising the accuracy and robustness of the algorithms. With MixHPC I propose to tackle this challenge by rethinking the role of precision in HPC: rather than viewing it as a fixed, static parameter, MixHPC will dynamically and adaptively employ multiple precisions, strategically mixing them to obtain novel mixed precision algorithms. This research encompasses both fundamental and applied science, spanning the fields of HPC, numerical linear algebra, data science, and numerical analysis. Indeed, I believe that the key to develop effective mixed precision algorithms is to combine knowledge and skills from all these fields. I aim to leverage my double experience both as a numerical analyst and an HPC practitioner to develop innovative algorithms that are fast, numerically sound, provably robust, and able to scale on large problems and on exascale computers with modern hardware.