Research

I am currently part of the Simbiot team in the Loria laboratory. My goal in this team is to design and implement parallel and distributed algorithms to perform complex tasks in a set of mobile agents (drones, robots,…).

Before joining that team I have been member of the AlGorille team.

And yet before joining the Loria, I have been a member of the ADN team in the LIFC  laboratory (currently AND team in Femto-st) at university of Franche-Comté.

Those last years, I oriented my researches towards three main topics.

The first one logically follows my works on asynchronism in parallel iterative algorithms and deals with the design and implementation of parallel schemes that are best suited to heterogeneous hardware architectures (CPU, GPU, X-Phi,…). It includes the developments of large codes of physical simulations such as the reconstruction of heterogeneous volumes via an inverse method based on the radiative transfer equation, or the resolution of Navier-Stokes equations via a discontinuous Galerkin formulation. Also, I work on the development of a simulation software for biological neural networks (SiReNe).

The second one takes place in the continuity of my previous works on discrete dynamical systems. It consists in designing pseudo-random number generators that are chaotic and that verifiy strong statistical properties. In this context, I particularly focus on the generation of hamiltonien cycles in the N-cube as well as their properties and their adequation to be used in robust PRNG.

The third one is directly linked to the Simbiot team as it deals with collaborative algorithmic between mobile robots in order to perform complex tasks. In particular, we have developed a local positioning system that is independant of the environment as well as the global systems like GPS.

Parallel iterative algorithms

That first subject deals with the design of numerical computation algorithms to be used on large systems of heterogeneous machines (large clusters). At the theoretical level, it involves automata systems whose values are in continuous spaces and which evolve synchronously or asynchronously in time. That temporal evolution allows us to reformulate those systems under the form of parallel iterative algorithms. In this context, I have studied possible strategies of large scale parallel iterative computing to solve linear and non-linear problems. Several essential aspects have been treated such as operating mode, convergence conditions, convergence detection and the coupling of different optimization techniques such as load balancing, computation-communication overlapping and the use of GPUs. Finally, we have studied also the contexts (hardware, software and problem type) in which those algorithms present a particular interest according to the other methods.

Discrete dynamical systems

That second subject concerns the theoretical study of the behavior of systems composed of discrete-state automata evolving in discrete time. Those systems present a practical interest since they allow to model numerous complex systems and in particular the parallel iterative processes. Hence, they allow us to precisely study their dynamic according to their operating mode. In this context, I have worked particularly on the asynchronous case which offers better performances, especially when used on large scale systems. However, that operating mode implies a different dynamic than the synchronous one and, in some cases, the system may not stabilize or may stabilize on the wrong stable state. Thus, that mode requires specific conditions to ensure a stable and correct behavior. In this context, I have studied different features of those systems such as the convergence conditions towards stable states, the influence of cycles in the communication graph over their global behavior and the coupling of synchronism-asynchronism to improve their stability. I am still interested in the design of such systems to solve specific problems.

Neural networks

In the sequel of my previous works done in a collaboration with the IRMA team of the CREST/FEMTO-ST through a project dealing with external radiotherapy, I have developed an activity over the theme of neural networks used in scientific computation. In fact, as neural networks can approximate complex shape functions, we have shown in our previous works the interest of coupling a neural network with an algorithm that evaluates radiation dose deposits in heterogeneous environments. Our approach is not restricted to this particular application domain and can be used in numerous other cases.