IBISC (Informatique, Bio-informatique et Systèmes Complexes – Computer Science, Bio-Informatics and Complex Systems) is a laboratory of Paris-Saclay University. It was formed through a merger between the LAMI (UMR 8042) and the LSC (REF 2494) laboratories. The governance of the laboratory consists of a Director, Frank Delaplace, and a Deputy Director, Samia Bouchafa-Bruneau. The research responds to major ICT societal challenges in precision and personalized medicine and vehicle of the future.
The IBISC laboratory is composed by 4 teams: AROBAS, COSMO, IRA2, and SIAM. Their scientific activities are divided into two axes: ICT & SMART SYSTEM and ICT & LIFE, each focused on a specific application area which is respectively: drone & vehicle, and precision and personalized medicine.
ICT & SMART SYSTEM: research deals with the design of autonomous and intelligent systems. The concept of system refers both to the road or air vehicles fleet, robot, software and services distributed and communicating or smart hardware components interacting sensors. These devices share the property of being composed of a large number of interacting components with an autonomous decision making while coordinating action to achieve a common goal. Two major questions underpinned the design of such complex systems: the former relates to the design method and the latter concerns the optimization of their collective behavior taking into account the environmental perturbations. Approaches combining methods and theories from different scientific fields are explored: automatic, algorithmic and formal methods. Applications are specifically targeted for new generation of drones and vehicles.
ICT & LIFE: these interdisciplinary research covers a wide spectrum of biological and biomedical issues at different scales of a living organism: data analysis, biological or biomedical signals, biological system modeling, assisted surgical gesture learning and assistance to the person. The research focuses on the development of theoretical frameworks, algorithmic methods and platforms to meet the challenges. More specifically, the biological data analysis relies on statistical learning, on structure prediction algorithms. The system biology modeling is based on formal methods dedicated to the network dynamics analysis. For the assisted surgical gestures learning and robotics assistance for the person, we develop systems coupling signal analysis based on several sensors to decision making techniques. The applicative scope of the research is devoted to personalized and precision medicine.